Section 2 Anaerobic Digestion

**13**

**Chapter 2**

**Abstract**

**1. Introduction**

requiring mineral fertilizers [2].

Microbial Responses to Different

Biogas production requires a number of different microbial groups that work in a synchronized and closely interacting manner. For bioreactors constructed to maximize waste treatment and energy production, it is crucial to manage this process in a way that secures the growth and activity of these microorganisms, as otherwise there is a great risk of process failure. However, the microbiome has a remarkable ability to adapt to various conditions related to substrate composition and operating conditions, thus showing high functional redundancy and robustness. In order to optimize and steer the process, it is important to have an understanding of the anaerobic microbiome, how it responds to various conditions, and its upper limits. This chapter reviews current knowledge regarding microbial responses to different operational management strategies. Microbial responses under various conditions and how the process can be operated to maintain the activity of key species are addressed. Parameters discussed include for example substrate composition, pre-

**Keywords:** anaerobic degradation, microbiology, taxonomy, start-up, temperature,

As the world's population continues to grow, it is necessary to find ways to develop resourceful waste treatment methods while concurrently reducing the dependency on fossil fuels. In this regard, biogas produced through anaerobic degradation (AD) is highly interesting, as it can replace fossil fuels in power and heat production, be used as feedstock for production of biochemicals, or be converted to vehicle fuel [1]. The biogas technology also enables resource sustainability when the digestion residue (digestate) is used as organic fertilizer to replace fossil energy-

Anaerobic digestion of organic material to biogas is a complex microbiological process requiring the combined activity of several groups of microorganisms with different metabolic capacities and growth requirements. To obtain a stable and efficient biogas process, it is important to meet the growth requirements of all microorganisms involved. The substrate is one critical parameter in this regard, contributing growth factors and macro- and micronutrients. Some organic materials can be used as the sole substrate, while others have to be co-digested with

Operating Practices for Biogas

Production Systems

*Maria Westerholm and Anna Schnürer*

treatment, ammonia level, temperature and organic load.

substrate composition, feeding, additives, bioaugmentation

### **Chapter 2**

## Microbial Responses to Different Operating Practices for Biogas Production Systems

*Maria Westerholm and Anna Schnürer*

### **Abstract**

Biogas production requires a number of different microbial groups that work in a synchronized and closely interacting manner. For bioreactors constructed to maximize waste treatment and energy production, it is crucial to manage this process in a way that secures the growth and activity of these microorganisms, as otherwise there is a great risk of process failure. However, the microbiome has a remarkable ability to adapt to various conditions related to substrate composition and operating conditions, thus showing high functional redundancy and robustness. In order to optimize and steer the process, it is important to have an understanding of the anaerobic microbiome, how it responds to various conditions, and its upper limits. This chapter reviews current knowledge regarding microbial responses to different operational management strategies. Microbial responses under various conditions and how the process can be operated to maintain the activity of key species are addressed. Parameters discussed include for example substrate composition, pretreatment, ammonia level, temperature and organic load.

**Keywords:** anaerobic degradation, microbiology, taxonomy, start-up, temperature, substrate composition, feeding, additives, bioaugmentation

### **1. Introduction**

As the world's population continues to grow, it is necessary to find ways to develop resourceful waste treatment methods while concurrently reducing the dependency on fossil fuels. In this regard, biogas produced through anaerobic degradation (AD) is highly interesting, as it can replace fossil fuels in power and heat production, be used as feedstock for production of biochemicals, or be converted to vehicle fuel [1]. The biogas technology also enables resource sustainability when the digestion residue (digestate) is used as organic fertilizer to replace fossil energyrequiring mineral fertilizers [2].

Anaerobic digestion of organic material to biogas is a complex microbiological process requiring the combined activity of several groups of microorganisms with different metabolic capacities and growth requirements. To obtain a stable and efficient biogas process, it is important to meet the growth requirements of all microorganisms involved. The substrate is one critical parameter in this regard, contributing growth factors and macro- and micronutrients. Some organic materials can be used as the sole substrate, while others have to be co-digested with

substrates that are complementary in composition in order to provide favorable conditions for microbial growth [3]. However, addition of additives such as iron, trace metals, or buffering chemicals may be essential in certain processes in order to ensure sufficient microbial activity and to prevent process collapse [4]. In addition to the nutrient composition, operating parameters such as pretreatment method, load of input material, retention time, process temperature, and stirring are of critical importance. All these parameters have to be set at appropriate levels in order to ensure high activity and gas yield with minimized risk of inhibition or washout of critical functions and microorganisms [5–9]. Thus, many different aspects need to be taken into consideration to achieve optimal microbial activity giving a high degree of degradation and gas production. It should be borne in mind that many operating and biological parameters are interlinked, sometimes with counteracting effects.

### **2. Characteristics of substrates used for biogas production**

The composition of substrates can vary considerably between anaerobic digesters, which bring different challenges depending on the feed characteristics combined with the parameters chosen for the specific system. For example, substrates rich in protein and fat have a high energy content and thus a high methane potential, but can sometimes cause process disturbances due to formation of inhibitory compounds or foaming [10–12]. Other materials posing a lower risk of process disturbance, such as lignocellulosic materials, can require an unfeasibly long time for degradation. In order to explain the prerequisites for microbial degradation and the challenges that exist, this section briefly describes the main characteristics of common substrates for biogas production. This provides background for a detailed description of the microbial degradation process and the responses to changes in operating parameters.

Plant-based materials, such as fruit, grains, vegetables, and root crops, are typically rich in different polysaccharides. Polysaccharides are chains of sugars linked in linear chains (cellulose and starch) or branched chains (hemicellulose, pectin, and glycogen). In the plant cell wall, hemicellulose, cellulose, and lignin are associated in the form of lignocellulose [13]. Simple polysaccharides such as starch and glycogen are easily cleaved by microorganisms into glucose units. Hemicellulose and cellulose are also relatively easily degraded but, when combined with lignin (i.e., lignocellulose) as in plants, the structure becomes relatively persistent to microbial degradation [14, 15]. Lignocellulosic materials such as straw (wheat, rice, corn, barley) and sugarcane bagasse are the most abundant renewable biomass and have high potential to contribute to expansion of worldwide biogas production [13, 16].

Protein-rich materials for biogas production include waste from animal rearing (slaughterhouse, dairy, animal manure, aquaculture sludge), ethanol fermentation (distiller's waste), food industry, and households [10, 17–21]. Proteins consist of long chains of amino acids joined by peptide (or amide) bonds and there are 20 different amino acids of various lengths. A feature of all amino acids is that they have at least one amine group (-NH2). The efficiency of protein degradation depends on the structure of these compounds and their solubility [22].

Slaughterhouse waste, food waste, and grease-separation sludge are materials with a high fat content [23–25]. Fat molecules are of different lengths (saturated or unsaturated) and are hydrolyzed to long-chain fatty acids (LCFA, >12 carbon atoms) and glycerol [26]. Lipids are normally rapidly degraded in AD, whereas the conversion of LCFA can represent a rate-limiting step [27, 28].

**15**

**Figure 1.**

*The figure is adapted from Kougias et al. [39].*

*Microbial Responses to Different Operating Practices for Biogas Production Systems*

The microbial process comprises the main degradation steps hydrolysis, acidogenesis, acetogenesis, and methanogenesis (**Figure 1**) and this process has to be efficient and balanced in order to obtain successful anaerobic digestion. The initial step is performed by hydrolytic bacteria, and possibly also fungi, that convert polymers (polysaccharides, lipids, proteins, etc.) into soluble monomers (LCFA, glycerol, amino acids, sugars, etc.) [29, 30]. The hydrolytic reaction is mediated by extracellular enzymes secreted by bacteria to the bulk solution and/or attached to their cell wall. Cellulose is hydrolyzed to cellobiose and glucose, while hemicelluloses are degraded to monomeric sugars and acetic acid by bacteria that often have several different enzymes combined into so-called cellulosomes situated on their cell wall [16, 31]. These cellulosomes contain proteins that have the ability to bind to cellulose, which makes the degradation more efficient because the enzymes can work directly "on-site." Fungal cellulases use a different mechanism and not only bind to the surface of the cellulose, but also to penetrate inside the complex biomass materials (e.g., plant cell walls) [32]. Through the action of extracellular enzymes (proteases), proteins are hydrolyzed into amino acids, which are subsequently degraded in the Stickland reaction or through uncoupled oxidation. In the Stickland reaction, one amino acid acts as an electron donor and the other as an electron acceptor, and the oxidation process produces a volatile carboxylic acid that is one carbon atom shorter than the original amino acid. For example, alanine with its three-carbon chain is converted to acetate [33]. Amino acids can also be fermented through uncoupled oxidation where electrons are instead released as hydrogen. This process can only occur in cooperation with a hydrogen-utilizing partner, such as methanogens, that keeps the hydrogen partial pressure low [34]. Irrespective of the degradation pathway, the amino group in the amino acid is released as ammonia and the sulfur in cysteine and methionine results in sulfide. Lipases are excreted by hydrolytic bacteria and catalyze the hydrolysis of lipids at the water-lipid interface [35], forming saturated or unsaturated LCFA and glycerol [36]. LCFAs thereafter absorb to and are transported through microbial

*Anaerobic degradation of carbohydrates, lipids, and proteins and the phyla commonly reported to be involved in the different steps. Biogas digester parameters identified as main drivers for community structure is depicted.* 

**3. The microbial degradation steps leading to methane**

*DOI: http://dx.doi.org/10.5772/intechopen.82815*

*Microbial Responses to Different Operating Practices for Biogas Production Systems DOI: http://dx.doi.org/10.5772/intechopen.82815*

### **3. The microbial degradation steps leading to methane**

*Anaerobic Digestion*

effects.

operating parameters.

wide biogas production [13, 16].

the structure of these compounds and their solubility [22].

conversion of LCFA can represent a rate-limiting step [27, 28].

substrates that are complementary in composition in order to provide favorable conditions for microbial growth [3]. However, addition of additives such as iron, trace metals, or buffering chemicals may be essential in certain processes in order to ensure sufficient microbial activity and to prevent process collapse [4]. In addition to the nutrient composition, operating parameters such as pretreatment method, load of input material, retention time, process temperature, and stirring are of critical importance. All these parameters have to be set at appropriate levels in order to ensure high activity and gas yield with minimized risk of inhibition or washout of critical functions and microorganisms [5–9]. Thus, many different aspects need to be taken into consideration to achieve optimal microbial activity giving a high degree of degradation and gas production. It should be borne in mind that many operating and biological parameters are interlinked, sometimes with counteracting

**2. Characteristics of substrates used for biogas production**

The composition of substrates can vary considerably between anaerobic digesters, which bring different challenges depending on the feed characteristics combined with the parameters chosen for the specific system. For example, substrates rich in protein and fat have a high energy content and thus a high methane potential, but can sometimes cause process disturbances due to formation of inhibitory compounds or foaming [10–12]. Other materials posing a lower risk of process disturbance, such as lignocellulosic materials, can require an unfeasibly long time for degradation. In order to explain the prerequisites for microbial degradation and the challenges that exist, this section briefly describes the main characteristics of common substrates for biogas production. This provides background for a detailed description of the microbial degradation process and the responses to changes in

Plant-based materials, such as fruit, grains, vegetables, and root crops, are typically rich in different polysaccharides. Polysaccharides are chains of sugars linked in linear chains (cellulose and starch) or branched chains (hemicellulose, pectin, and glycogen). In the plant cell wall, hemicellulose, cellulose, and lignin are associated in the form of lignocellulose [13]. Simple polysaccharides such as starch and glycogen are easily cleaved by microorganisms into glucose units. Hemicellulose and cellulose are also relatively easily degraded but, when combined with lignin (i.e., lignocellulose) as in plants, the structure becomes relatively persistent to microbial degradation [14, 15]. Lignocellulosic materials such as straw (wheat, rice, corn, barley) and sugarcane bagasse are the most abundant renewable biomass and have high potential to contribute to expansion of world-

Protein-rich materials for biogas production include waste from animal rearing (slaughterhouse, dairy, animal manure, aquaculture sludge), ethanol fermentation (distiller's waste), food industry, and households [10, 17–21]. Proteins consist of long chains of amino acids joined by peptide (or amide) bonds and there are 20 different amino acids of various lengths. A feature of all amino acids is that they have at least one amine group (-NH2). The efficiency of protein degradation depends on

Slaughterhouse waste, food waste, and grease-separation sludge are materials with a high fat content [23–25]. Fat molecules are of different lengths (saturated or unsaturated) and are hydrolyzed to long-chain fatty acids (LCFA, >12 carbon atoms) and glycerol [26]. Lipids are normally rapidly degraded in AD, whereas the

**14**

The microbial process comprises the main degradation steps hydrolysis, acidogenesis, acetogenesis, and methanogenesis (**Figure 1**) and this process has to be efficient and balanced in order to obtain successful anaerobic digestion. The initial step is performed by hydrolytic bacteria, and possibly also fungi, that convert polymers (polysaccharides, lipids, proteins, etc.) into soluble monomers (LCFA, glycerol, amino acids, sugars, etc.) [29, 30]. The hydrolytic reaction is mediated by extracellular enzymes secreted by bacteria to the bulk solution and/or attached to their cell wall. Cellulose is hydrolyzed to cellobiose and glucose, while hemicelluloses are degraded to monomeric sugars and acetic acid by bacteria that often have several different enzymes combined into so-called cellulosomes situated on their cell wall [16, 31]. These cellulosomes contain proteins that have the ability to bind to cellulose, which makes the degradation more efficient because the enzymes can work directly "on-site." Fungal cellulases use a different mechanism and not only bind to the surface of the cellulose, but also to penetrate inside the complex biomass materials (e.g., plant cell walls) [32].

Through the action of extracellular enzymes (proteases), proteins are hydrolyzed into amino acids, which are subsequently degraded in the Stickland reaction or through uncoupled oxidation. In the Stickland reaction, one amino acid acts as an electron donor and the other as an electron acceptor, and the oxidation process produces a volatile carboxylic acid that is one carbon atom shorter than the original amino acid. For example, alanine with its three-carbon chain is converted to acetate [33]. Amino acids can also be fermented through uncoupled oxidation where electrons are instead released as hydrogen. This process can only occur in cooperation with a hydrogen-utilizing partner, such as methanogens, that keeps the hydrogen partial pressure low [34]. Irrespective of the degradation pathway, the amino group in the amino acid is released as ammonia and the sulfur in cysteine and methionine results in sulfide. Lipases are excreted by hydrolytic bacteria and catalyze the hydrolysis of lipids at the water-lipid interface [35], forming saturated or unsaturated LCFA and glycerol [36]. LCFAs thereafter absorb to and are transported through microbial

### **Figure 1.**

*Anaerobic degradation of carbohydrates, lipids, and proteins and the phyla commonly reported to be involved in the different steps. Biogas digester parameters identified as main drivers for community structure is depicted. The figure is adapted from Kougias et al. [39].*

cell membranes of acetogenic bacteria, where the LCFAs are converted to acetate via beta-oxidation to acetate, carbon dioxide (CO2), and hydrogen (H2) [37, 38].

The soluble monomers produced in the hydrolytic and acidogenic steps are further degraded to intermediate products. These mainly comprise volatile fatty acids (e.g., acetate, propionate, butyrate, lactate, valerate, and caproate), alcohols, formate, H2, and CO2 [40]. During acetogenesis, the products formed in hydrolysis/acidogenesis are further converted by a group of bacteria called acetogens, generating acetate, H2, and CO2 as main products. During this process, various electron acceptors can be used, including CO2, nitrate, sulfate, and protons, with the latter being most important in the biogas process [41]. Acetogens can also directly use products from hydrolysis, such as sugars and amino acids [42], or oxidize pyruvate, which is a common intermediate in anaerobic degradation reactions, to acetate [43]. For thermodynamic reasons, many reactions performed by acetogens, such as oxidation of organic acids and LCFA, can only proceed if the partial pressure of H2 (*p*H2) is kept low [44]. For some acids, such as propionate, the removal of acetate can also be of crucial importance [45]. The removal of the acidogenic products acetate and H2/formate and some methylated compounds mainly proceeds through consumption by methanogens. The energetic situation for the methanogens is comparatively more favorable than acetogenesis, and thus combining these reactions allows both organisms to obtain energy for growth. This type of symbiosis, in which neither organism can operate without the other but together they exhibit metabolic activities that they could not accomplish on their own, is called syntrophy [43, 44].

In the last step, methanogenic archaea use acetate, CO2, or methylated compounds to produce methane (CH4) (**Figure 1**). In acetate-utilizing (aceticlastic) methanogenesis, acetate is split into a methyl group and CO2, and the methyl group is later reduced to methane using an electron provided by the carboxyl group. CO2 is reduced to methane by hydrogenotrophic methanogens, using H2 or formate as primary electron donors. In methanogenesis from methylated compounds such as methanol, methylamines, and methylsulfides, the methyl group is reduced to methane. Most methylotrophic methanogens then obtain the electrons they require for reduction from oxidation of additional methyl groups to CO2 [46, 47].

### **4. Microorganisms engaged in the different degradation steps**

Organisms that are active during the hydrolysis of polysaccharides in biogas processes include various bacteria and anaerobic fungi [14, 29]. Cellulose and starch-degrading bacteria are found within the genera *Acetivibrio*, *Butyrivibrio*, *Caldanaerobacter*, *Caldicellulosiruptor*, *Clostridium, Eubacterium*, *Halocella*, *Ruminoclostridium* and *Ruminococcus* (phylum Firmicutes), *Bacteroides* and *Paludibacter* (phylum Bacteroidetes), *Fibrobacter* (phylum Fibrobacteres), Spirochaetes (phylum Spirochaeta), and *Fervidobacterium* and *Thermotoga* (phylum Thermotogae) [14, 48–57]. Identification of the genes necessary for degradation of cellulose has also led to the suggestion that members of the phylum Proteobacteria [56], candidate phylum Cloacimonetes [58] and Actinomyces [59] have this ability. Among the anaerobic fungi, representatives of the phylum Neocallimastigomycota, commonly also found in ruminants, have been suggested as promising candidates to improve biogas production from lignocellulosic material [60, 61]. Protein and amino acid degradation in anaerobic digesters has been shown to be performed by various genera within the phylum Firmicutes, such as *Anaeromusa*, *Anaerosphaera*, *Aminobacterium*, *Aminomonas*, *Gelria*, *Peptoniphilus*,

**17**

*Microbial Responses to Different Operating Practices for Biogas Production Systems*

*Thermanaerovibrio* [62–67], *Clostridium* [68], *Proteiniborus* [69], and

*Sporanaerobacter* [70]*.* However, members of the phyla Bacteroidetes (e.g., genera *Fermentimonas* and *Proteiniphilum*), Fusobacteria, and Cloacimonetes have also been suggested to have an active amino acid-based metabolism in anaerobic digesters [71, 72]. Less is known about bacteria involved in hydrolysis of fat. Lipolytic bacteria in anaerobic digesters has so far been proposed to belong to families Caldilineaceae (phylum Firmicutes), Bacteroidaceae (phylum Bacteroidetes) and to genera *Trichococcus* (phylum Firmicutes), *Devosia*, and *Psycrobacter* (phylum

Acetogenesis and syntrophic acid degradation are often performed by bacteria belonging to the genera *Clostridium* and *Acetobacterium* (phylum Firmicutes), but have also been assigned to the phylum Proteobacteria [14, 43, 75]. Bacteria identified so far that are capable of β-oxidizing LCFA in syntrophy with methanogens all belong to the families Syntrophomonadaceae and Syntrophaceae [23, 76]. Syntrophs that degrade short-chain fatty acids, such as butyrate, propionate, and acetate, in association with methanogens are phylogenetically distributed. Syntrophic propionate and butyrate degradation is performed by genera such as *Syntrophomonas*, *Syntrophospora*, *Syntrophothermus*, *Thermosyntropha*, and *Pelotomaculum* (phylum Firmicutes), or the genera *Syntrophus*, *Smithella*, and *Syntrophobacter* (phylum Proteobacteria) [77]. In addition, the phyla Cloacimonetes, Synergistetes, and Chloriflexi have been suggested to contain bacteria capable of performing syntrophic metabolism in association with hydrogenotrophic methanogens [78–80]. Bacteria capable of syntrophic acetate oxidation identified to date belong to the genera *Clostridium*, *Thermoacetogenium*, *Syntrophaceticus*, and *Tepidanaerobacter* (phylum Firmicutes) [81]. Novel syntrophic acetate-oxidizing bacteria (SAOB) candidates have been suggested within the order Clostridiales and/or Thermoanaerobacterales [82–86], *Synergistes* group 4 [87], the genus *Coprothermobacter* [88] and the phyla Spirochaetes [89], Thermotogae [83],

In terms of relative abundance, the methanogenic community generally represents a minor part (2–5%) of the total community, but methanogens have been observed to have high activity relative to their abundance [83, 91, 92]. Methanogens commonly detected in biogas digesters belong to the orders Methanobacteriales, Methanomicrobiales, and Methanosarcinales (phylum Euryarchaeota). However, the orders Methanococcales and

Methanomassiliicoccales (phylum Euryarchaeota) have also been found in AD systems [30, 93]. Hydrogenotrophs are found within all methanogenic orders except for the Methanomassiliicoccales [93]. Acetate is only used by members of the families Methanosarcinaceae and Methanosaetaceae (order Methanosarcinales). Members of the Methanosarcinaceae are comparatively more versatile, having the ability to grow on several different substrates, such as acetate, hydrogen, and methanol, while members of the Methanosaetaceae use only acetate [94]. Methane formation from methylated compounds is performed by members of the Methanomassiliicoccales, Methanobacteriales, and Methanosarcinales [93]. A candidate methanogenic class, WSA2, has also been proposed and suggested to be

With ongoing advances in molecular techniques and cultivation studies, the list of anaerobic microorganisms responsible for different degradation pathways is continually being updated. The complexity of the cooperation involved in degradation is further illustrated by the fact that members within one and the same genus are often able degrade chemically different compounds. In future, the introduction of omics approaches, combined with isolates of novel species, will most likely increase

restricted to methanogenesis through methylated thiol reduction [95].

*DOI: http://dx.doi.org/10.5772/intechopen.82815*

Proteobacteria) [73, 74].

Chloroflexi, and Bacteroidetes [90].

insights into the taxa involved [30, 96–99].

*Microbial Responses to Different Operating Practices for Biogas Production Systems DOI: http://dx.doi.org/10.5772/intechopen.82815*

*Thermanaerovibrio* [62–67], *Clostridium* [68], *Proteiniborus* [69], and *Sporanaerobacter* [70]*.* However, members of the phyla Bacteroidetes (e.g., genera *Fermentimonas* and *Proteiniphilum*), Fusobacteria, and Cloacimonetes have also been suggested to have an active amino acid-based metabolism in anaerobic digesters [71, 72]. Less is known about bacteria involved in hydrolysis of fat. Lipolytic bacteria in anaerobic digesters has so far been proposed to belong to families Caldilineaceae (phylum Firmicutes), Bacteroidaceae (phylum Bacteroidetes) and to genera *Trichococcus* (phylum Firmicutes), *Devosia*, and *Psycrobacter* (phylum Proteobacteria) [73, 74].

Acetogenesis and syntrophic acid degradation are often performed by bacteria belonging to the genera *Clostridium* and *Acetobacterium* (phylum Firmicutes), but have also been assigned to the phylum Proteobacteria [14, 43, 75]. Bacteria identified so far that are capable of β-oxidizing LCFA in syntrophy with methanogens all belong to the families Syntrophomonadaceae and Syntrophaceae [23, 76]. Syntrophs that degrade short-chain fatty acids, such as butyrate, propionate, and acetate, in association with methanogens are phylogenetically distributed. Syntrophic propionate and butyrate degradation is performed by genera such as *Syntrophomonas*, *Syntrophospora*, *Syntrophothermus*, *Thermosyntropha*, and *Pelotomaculum* (phylum Firmicutes), or the genera *Syntrophus*, *Smithella*, and *Syntrophobacter* (phylum Proteobacteria) [77]. In addition, the phyla Cloacimonetes, Synergistetes, and Chloriflexi have been suggested to contain bacteria capable of performing syntrophic metabolism in association with hydrogenotrophic methanogens [78–80]. Bacteria capable of syntrophic acetate oxidation identified to date belong to the genera *Clostridium*, *Thermoacetogenium*, *Syntrophaceticus*, and *Tepidanaerobacter* (phylum Firmicutes) [81]. Novel syntrophic acetate-oxidizing bacteria (SAOB) candidates have been suggested within the order Clostridiales and/or Thermoanaerobacterales [82–86], *Synergistes* group 4 [87], the genus *Coprothermobacter* [88] and the phyla Spirochaetes [89], Thermotogae [83], Chloroflexi, and Bacteroidetes [90].

In terms of relative abundance, the methanogenic community generally represents a minor part (2–5%) of the total community, but methanogens have been observed to have high activity relative to their abundance [83, 91, 92]. Methanogens commonly detected in biogas digesters belong to the orders Methanobacteriales, Methanomicrobiales, and Methanosarcinales (phylum Euryarchaeota). However, the orders Methanococcales and Methanomassiliicoccales (phylum Euryarchaeota) have also been found in AD systems [30, 93]. Hydrogenotrophs are found within all methanogenic orders except for the Methanomassiliicoccales [93]. Acetate is only used by members of the families Methanosarcinaceae and Methanosaetaceae (order Methanosarcinales). Members of the Methanosarcinaceae are comparatively more versatile, having the ability to grow on several different substrates, such as acetate, hydrogen, and methanol, while members of the Methanosaetaceae use only acetate [94]. Methane formation from methylated compounds is performed by members of the Methanomassiliicoccales, Methanobacteriales, and Methanosarcinales [93]. A candidate methanogenic class, WSA2, has also been proposed and suggested to be restricted to methanogenesis through methylated thiol reduction [95].

With ongoing advances in molecular techniques and cultivation studies, the list of anaerobic microorganisms responsible for different degradation pathways is continually being updated. The complexity of the cooperation involved in degradation is further illustrated by the fact that members within one and the same genus are often able degrade chemically different compounds. In future, the introduction of omics approaches, combined with isolates of novel species, will most likely increase insights into the taxa involved [30, 96–99].

*Anaerobic Digestion*

own, is called syntrophy [43, 44].

to CO2 [46, 47].

cell membranes of acetogenic bacteria, where the LCFAs are converted to acetate via

The soluble monomers produced in the hydrolytic and acidogenic steps are further degraded to intermediate products. These mainly comprise volatile fatty acids (e.g., acetate, propionate, butyrate, lactate, valerate, and caproate), alcohols, formate, H2, and CO2 [40]. During acetogenesis, the products formed in hydrolysis/acidogenesis are further converted by a group of bacteria called acetogens, generating acetate, H2, and CO2 as main products. During this process, various electron acceptors can be used, including CO2, nitrate, sulfate, and protons, with the latter being most important in the biogas process [41]. Acetogens can also directly use products from hydrolysis, such as sugars and amino acids [42], or oxidize pyruvate, which is a common intermediate in anaerobic degradation reactions, to acetate [43]. For thermodynamic reasons, many reactions performed by acetogens, such as oxidation of organic acids and LCFA, can only proceed if the partial pressure of H2 (*p*H2) is kept low [44]. For some acids, such as propionate, the removal of acetate can also be of crucial importance [45]. The removal of the acidogenic products acetate and H2/formate and some methylated compounds mainly proceeds through consumption by methanogens. The energetic situation for the methanogens is comparatively more favorable than acetogenesis, and thus combining these reactions allows both organisms to obtain energy for growth. This type of symbiosis, in which neither organism can operate without the other but together they exhibit metabolic activities that they could not accomplish on their

In the last step, methanogenic archaea use acetate, CO2, or methylated compounds to produce methane (CH4) (**Figure 1**). In acetate-utilizing (aceticlastic) methanogenesis, acetate is split into a methyl group and CO2, and the methyl group is later reduced to methane using an electron provided by the carboxyl group. CO2 is reduced to methane by hydrogenotrophic methanogens, using H2 or formate as primary electron donors. In methanogenesis from methylated compounds such as methanol, methylamines, and methylsulfides, the methyl group is reduced to methane. Most methylotrophic methanogens then obtain the electrons they require for reduction from oxidation of additional methyl groups

**4. Microorganisms engaged in the different degradation steps**

Organisms that are active during the hydrolysis of polysaccharides in biogas processes include various bacteria and anaerobic fungi [14, 29]. Cellulose and starch-degrading bacteria are found within the genera *Acetivibrio*, *Butyrivibrio*, *Caldanaerobacter*, *Caldicellulosiruptor*, *Clostridium, Eubacterium*, *Halocella*, *Ruminoclostridium* and *Ruminococcus* (phylum Firmicutes), *Bacteroides* and *Paludibacter* (phylum Bacteroidetes), *Fibrobacter* (phylum Fibrobacteres), Spirochaetes (phylum Spirochaeta), and *Fervidobacterium* and *Thermotoga* (phylum Thermotogae) [14, 48–57]. Identification of the genes necessary for degradation of cellulose has also led to the suggestion that members of the phylum Proteobacteria [56], candidate phylum Cloacimonetes [58] and Actinomyces [59] have this ability. Among the anaerobic fungi, representatives of the phylum Neocallimastigomycota, commonly also found in ruminants, have been suggested as promising candidates to improve biogas production from lignocellulosic material [60, 61]. Protein and amino acid degradation in anaerobic digesters has been shown to be performed by various genera within the phylum Firmicutes, such as *Anaeromusa*, *Anaerosphaera*, *Aminobacterium*, *Aminomonas*, *Gelria*, *Peptoniphilus*,

beta-oxidation to acetate, carbon dioxide (CO2), and hydrogen (H2) [37, 38].

**16**

### **5. The impact of different operating conditions on AD microbial communities**

To optimize the anaerobic digestion process and steer it in a desired direction, it is important to have knowledge and understanding of the metabolic capacities of key microorganisms. Knowledge of the level of functional redundancy within the community (how easily the microbial community adapts to operating changes) and microbial requirements for activity can also help identify operating management practices for improved process performance. In this section, the impact on the microbial community of different operating strategies is described.

### **5.1 Start-up strategies**

The inoculum used for starting up a biogas process has been shown to be of importance for the degradation rate, specific methane yield, and stress tolerance, possibly depending on differences in the composition of the microbial community [52, 100–104]. In addition, chemical parameters, such as presence of trace elements needed for microbial activity, have been suggested to be important [105]. Inocula, most commonly applied in practice, can be categorized as originating from one of the following three sources: wastewater treatment plants, agricultural biogas plants, and plants treating various biowastes, such as municipal and industrial food waste [101]. Microbial analyses of biogas plants belonging to these different groups have clearly shown separation based on microbial community structure [102, 103, 106, 107]. This separation is believed to be caused by the substrate characteristics and operating conditions, with temperature and ammonia being strong regulating parameters [106]. It has been suggested that wastewater sludge is most optimal as the inoculum for biomethane potential (BMP) tests, due to its diverse and highly active community [101]. However, Koch et al. [108] found that inoculum originating from a plant degrading similar substrate to that evaluated in the BMP test gave the best results, suggesting that a substrate-adjusted microbial community is more suitable. Choosing inoculum from a well-functioning biogas process degrading similar substrate and operating under the parameters planned for the new process has also been shown to reduce the period for start-up and avoid initial instability during continuous operation [52, 100]. It has been suggested that methanogenic activity and abundance are appropriate parameters for assessing the suitability of an inoculum and for achieving high rates and yields in BMP tests, as well as for operation of a continuous biogas process [100, 109]. Another factor that can be favorable for the process is to use an inoculum with high microbial diversity, which is considered to correlate with high functional redundancy. One hypothesis to explain this is that having a large number of species provides potential for failing species to be easily replaced by other species performing similar functions, with little impact on the overall process [110].

Evaluations of different inocula during semi-continuous operation using the same substrate have been made for mesophilic processes operating with maize silage [103], a mix of manure and grass [52], cellulose [102], and a mix of waste-activated sludge and glycerol [100]. These studies have produced some contradictory results with regard to the composition of the microbial community over time. Han et al. [102] found that the inoculum source was determining for methane yield, pH, and volatile fatty acid (VFA) production using cellulose as a substrate, both during start-up and after reaching stable operation. Different steady state community patterns were also obtained in the different reactors started with different inocula. Moreover, reactors characterized by high VFA levels and low pH had comparatively

**19**

**5.2 Temperature**

*Microbial Responses to Different Operating Practices for Biogas Production Systems*

conditions on long-term effects and optimized performance.

Temperature strongly affects the microbial community structure and thus also process performance and stability [5, 92, 106, 107, 114–118]. When choosing the operating temperature, other operating parameters such as substrate, feeding strategy, and presence or possible formation of inhibitory compounds should be taken into account. The temperatures normally used for digestion in industrial biogas processes are not only mesophilic (37–40°C) or thermophilic (50–55°C), but also psychrophilic (<25°C) and temperatures between mesophilic and thermophilic (41–45°C) have been shown to be achievable [57, 118–122]. Some studies investigating AD at 41–45°C have even reported higher methane production compared with the more commonly used mesophilic or thermophilic range, with associated microbial shifts [57, 119, 121]. In general, metabolic rates and biochemical processes increase with increasing temperature [115, 123, 124]. However, thermophilic conditions can also make the process more sensitive to disturbances and inhibitory compounds [115, 125] and cause less efficient degradation of some inhibitory compounds [126]. Shifts in microbial community in response to temperature change can take time and involve periods of instability. It is therefore recommended to allow the community to adapt to the temperature change by a slow increase/decrease (±1°C per day) [5, 127–130]. In order to avoid process collapse, temperature changes should be carefully monitored, both when increasing and decreasing the operating temperature. A temporary reduction in feed rate and prolonged retention time can be required in the event of disturbance during step-wise temperature changes [5]. Another important aspect to consider during AD operation is that the microbial community, specifically the methanogens, is sensitive to long-term

low levels of Methanosarcinales, highlighting the importance of this methanogen for efficient biogas production. In line with this, high levels of Methanosarcinales have also been shown to be important for efficient start-up and revival of a thermophilic process suffering from high acetate levels [111]. In contradiction to the results reported by Han et al. [102], a study employing three different inocula for start-up of parallel processes using a manure-grass mix as substrate found that the overall microbial community and process performance became similar in the parallel processes after three hydraulic retention times (HRT) of operation [52]. However, a clear difference in performance was seen during the initial phase after start-up in that study, with poor performance when using an inoculum from a high-ammonia process. Less efficient start-up using a high-ammonia inoculum was also seen in a study by de Vrieze et al. [100] on AD with sludge and glycerol. High-ammonia levels usually impact microbial richness and cause significant shifts in both the bacterial and methanogenic community [82, 106]. This possibly explains the less efficient start-up performance when using substrate with a comparatively low nitrogen level [100]. A negative correlation between ammonia level and cellulose degradation efficiency was also found in the abovementioned study by Liu et al. [52]. Interestingly, when processes started with different inocula and unified in performance and microbial community were supplemented with an additional substrate in that study, the processes again diverged in both performance and microbiology. These results illustrate that choice of inoculum can influence long-term performance of biogas processes [112]. Moreover, even when the same inoculum and operating parameters are used during start-up, different process performances and microbial communities can evolve [102, 113]. This illustrates that stochastic factors play an important role in the microbial community assembly in biogas reactors. It also highlights the need for further research on the impact of inoculum source and operating

*DOI: http://dx.doi.org/10.5772/intechopen.82815*

### *Microbial Responses to Different Operating Practices for Biogas Production Systems DOI: http://dx.doi.org/10.5772/intechopen.82815*

low levels of Methanosarcinales, highlighting the importance of this methanogen for efficient biogas production. In line with this, high levels of Methanosarcinales have also been shown to be important for efficient start-up and revival of a thermophilic process suffering from high acetate levels [111]. In contradiction to the results reported by Han et al. [102], a study employing three different inocula for start-up of parallel processes using a manure-grass mix as substrate found that the overall microbial community and process performance became similar in the parallel processes after three hydraulic retention times (HRT) of operation [52]. However, a clear difference in performance was seen during the initial phase after start-up in that study, with poor performance when using an inoculum from a high-ammonia process. Less efficient start-up using a high-ammonia inoculum was also seen in a study by de Vrieze et al. [100] on AD with sludge and glycerol. High-ammonia levels usually impact microbial richness and cause significant shifts in both the bacterial and methanogenic community [82, 106]. This possibly explains the less efficient start-up performance when using substrate with a comparatively low nitrogen level [100]. A negative correlation between ammonia level and cellulose degradation efficiency was also found in the abovementioned study by Liu et al. [52]. Interestingly, when processes started with different inocula and unified in performance and microbial community were supplemented with an additional substrate in that study, the processes again diverged in both performance and microbiology. These results illustrate that choice of inoculum can influence long-term performance of biogas processes [112]. Moreover, even when the same inoculum and operating parameters are used during start-up, different process performances and microbial communities can evolve [102, 113]. This illustrates that stochastic factors play an important role in the microbial community assembly in biogas reactors. It also highlights the need for further research on the impact of inoculum source and operating conditions on long-term effects and optimized performance.

### **5.2 Temperature**

*Anaerobic Digestion*

**communities**

**5.1 Start-up strategies**

**5. The impact of different operating conditions on AD microbial** 

microbial community of different operating strategies is described.

To optimize the anaerobic digestion process and steer it in a desired direction, it is important to have knowledge and understanding of the metabolic capacities of key microorganisms. Knowledge of the level of functional redundancy within the community (how easily the microbial community adapts to operating changes) and microbial requirements for activity can also help identify operating management practices for improved process performance. In this section, the impact on the

The inoculum used for starting up a biogas process has been shown to be of importance for the degradation rate, specific methane yield, and stress tolerance, possibly depending on differences in the composition of the microbial community [52, 100–104]. In addition, chemical parameters, such as presence of trace elements needed for microbial activity, have been suggested to be important [105]. Inocula, most commonly applied in practice, can be categorized as originating from one of the following three sources: wastewater treatment plants, agricultural biogas plants, and plants treating various biowastes, such as municipal and industrial food waste [101]. Microbial analyses of biogas plants belonging to these different groups have clearly shown separation based on microbial community structure [102, 103, 106, 107]. This separation is believed to be caused by the substrate characteristics and operating conditions, with temperature and ammonia being strong regulating parameters [106]. It has been suggested that wastewater sludge is most optimal as the inoculum for biomethane potential (BMP) tests, due to its diverse and highly active community [101]. However, Koch et al. [108] found that inoculum originating from a plant degrading similar substrate to that evaluated in the BMP test gave the best results, suggesting that a substrate-adjusted microbial community is more suitable. Choosing inoculum from a well-functioning biogas process degrading similar substrate and operating under the parameters planned for the new process has also been shown to reduce the period for start-up and avoid initial instability during continuous operation [52, 100]. It has been suggested that methanogenic activity and abundance are appropriate parameters for assessing the suitability of an inoculum and for achieving high rates and yields in BMP tests, as well as for operation of a continuous biogas process [100, 109]. Another factor that can be favorable for the process is to use an inoculum with high microbial diversity, which is considered to correlate with high functional redundancy. One hypothesis to explain this is that having a large number of species provides potential for failing species to be easily replaced by other species performing similar functions, with little impact on the

Evaluations of different inocula during semi-continuous operation using the same substrate have been made for mesophilic processes operating with maize silage [103], a mix of manure and grass [52], cellulose [102], and a mix of waste-activated sludge and glycerol [100]. These studies have produced some contradictory results with regard to the composition of the microbial community over time. Han et al. [102] found that the inoculum source was determining for methane yield, pH, and volatile fatty acid (VFA) production using cellulose as a substrate, both during start-up and after reaching stable operation. Different steady state community patterns were also obtained in the different reactors started with different inocula. Moreover, reactors characterized by high VFA levels and low pH had comparatively

**18**

overall process [110].

Temperature strongly affects the microbial community structure and thus also process performance and stability [5, 92, 106, 107, 114–118]. When choosing the operating temperature, other operating parameters such as substrate, feeding strategy, and presence or possible formation of inhibitory compounds should be taken into account. The temperatures normally used for digestion in industrial biogas processes are not only mesophilic (37–40°C) or thermophilic (50–55°C), but also psychrophilic (<25°C) and temperatures between mesophilic and thermophilic (41–45°C) have been shown to be achievable [57, 118–122]. Some studies investigating AD at 41–45°C have even reported higher methane production compared with the more commonly used mesophilic or thermophilic range, with associated microbial shifts [57, 119, 121]. In general, metabolic rates and biochemical processes increase with increasing temperature [115, 123, 124]. However, thermophilic conditions can also make the process more sensitive to disturbances and inhibitory compounds [115, 125] and cause less efficient degradation of some inhibitory compounds [126]. Shifts in microbial community in response to temperature change can take time and involve periods of instability. It is therefore recommended to allow the community to adapt to the temperature change by a slow increase/decrease (±1°C per day) [5, 127–130]. In order to avoid process collapse, temperature changes should be carefully monitored, both when increasing and decreasing the operating temperature. A temporary reduction in feed rate and prolonged retention time can be required in the event of disturbance during step-wise temperature changes [5]. Another important aspect to consider during AD operation is that the microbial community, specifically the methanogens, is sensitive to long-term

temperature variations. Experience from large-scale operations shows that constant temperature fluctuations should not exceed ±2–3°C in order to avoid instability [131].

One quite consistent effect of operation at thermophilic instead of mesophilic temperature is a higher level of Firmicutes compared with Bacteroidetes/ Proteobacteria [5, 57, 116, 118, 121, 132–136]. A high Firmicutes to Bacteroidetes ratio in mesophilic AD has been shown to correlate positively with high methane yield [137, 138]. However, an increase in this ratio has also been suggested to decrease the richness of predicted lignocellulolytic enzymes in biogas digesters, an effect attributed to lower hydrolysis in comparison with natural anaerobic systems [139]. Whether similar correlations arise in comparisons between mesophilic and thermophilic biogas communities has yet to be determined. Another characteristic feature of thermophilic communities is a higher dominance of the phylum Thermotogae [91, 114, 117, 123, 133, 135, 136, 140–143]. Members of the Thermotogae degrade polysaccharides to ethanol, acetate, CO2, and H2 [72, 144], but can also be involved in degradation of alcohols to CO2 and H2 in syntrophic association with a hydrogen-consuming partner [72, 145].

Another aspect to consider during operation at thermophilic temperature is that ammonia inhibition occurs more quickly at higher temperature, as the equilibrium between ammonium and ammonia shifts towards the latter when the temperature rises [146]. Irrespective of temperature, methanogens performing the last step in AD are among the least tolerant to ammonia and reduced methane yield, and accumulation of fatty acids is a common consequence of microbial inhibition of this group [81]. Methanogenic community changes related to temperature, often combined with increasing ammonia levels, have been reported to include positive correlations between high temperature and enhanced relative abundance of Methanobacteriales (often *Methanothermobacter*) and/or Methanomicrobiales (often *Methanoculleus*) [5, 106, 116–118, 133, 136, 140–142, 147–150]. The shift in methanogenic community often also involves a shift in acetate degradation pathway from aceticlastic methanogenesis to syntrophic acetate oxidation (SAO) [81]. However, in AD processes that seldom reach high-ammonia levels, such as AD of wastewater sludge, instant temperature changes without associated instability have been shown to be possible [143, 151, 152].

### **5.3 Pretreatment**

Wastes rich in lignocellulose (e.g., forestry by-products, straw) or keratinase (e.g., waste from poultry, meat, and fish industries) and wastewater sludge have significant biogas potential [15, 16, 153, 154]. However, the complex floc structures of microbial cells in sewage sludge and the recalcitrant structure of lignocellulose make hydrolysis the rate-limiting step in AD systems [16, 155, 156]. Pretreatment is a well-proven approach to improve degradation of such waste. Common pretreatment strategies comprise physical (e.g., heat/pressure, irradiation, ultrasonic), chemical (e.g., acids/bases, ozonation, oxidation), and biological (addition of fungi/bacteria/enzymes under aerobic or anaerobic conditions) methods [16, 157]. The general concept of pretreatment is that it should improve the accessibility of the material to microbial degradation by disrupting the structure, changing the biomass porosity, and reducing the particle size to enhance the surface area that can be attacked. Many studies have investigated the effect on methane yield of pretreatment of various materials and many methods have shown improved process efficiency following pretreatment [16, 157, 158]. However, fewer studies have examined the influence of pretreatment on microbial communities and relationships to the increase in methane yield, and most of the studies performed to date have been on AD of waste-activated sludge, with differing results. For example, during mesophilic AD of sewage sludge, some studies have found no

**21**

*Microbial Responses to Different Operating Practices for Biogas Production Systems*

responses in the microbial community following thermophilic aerobic digestion or ultrasonic or alkaline pretreatment [159–161]. However, in other studies investigating mesophilic AD processes, ultrasonic, microwave, and electrokinetic pretreatments have all been shown to increase the relative abundance of *Clostridiales* (phylum Firmicutes) and Cloacimonetes and decrease the relative abundance of Proteobacteria [162, 163]. Moreover, in mesophilic AD of microalgae biomass, thermal pretreatment has been found to increase the relative abundance of the families Rikenellaceae (phylum Bacteriodetes) and Anaerolineaceae (phylum Chloroflexi) and decrease the relative abundance of the phylum Proteobacteria [164]. Using metatranscriptomic analysis, Xia et al. [165] found that low-frequency ultrasonic treatment of sludge during thermophilic digestion increased the hydrolytic activity of representatives of the phyla Bacteroidetes and Cloacimonetes and increased motility and chemotaxis in members of the phylum Thermotoga. Another noteworthy finding in that study was that, among the bacteria involved in cellulose degradation, members of the order Bacteroidales were more active than members of the Clostridiales*.* Both these groups contain well-known cellulosedegrading bacteria, but members of the Bacteroidales typically do not possess the cellulosomes often seen in Clostridiales. Xia et al. [165] concluded that lowfrequency ultrasonic pretreatment allows enrichment of a community with high

For substrates other than sludge, Wang et al. [166] reported a weak effect on the microbial community structure during digestion of thermal pretreated distilled grain waste in thermophilic solid AD. Thermal and thermochemical pretreatment approaches are the most commonly used methods for lignocellulosic materials used for bioenergy production purposes [167]. Such methods are often efficient in breaking the carbohydrate polymers to soluble sugars and improving the accessibility of the substrate to microbial degradation, thus increasing the biogas yield. However, these pretreatments can also release inhibitors such as furfural, 5-HMF, vanillin, and other phenolic compounds [167]. Depending on concentration, these lignin-derived compounds have been found to be inhibitory to methanogen and to result in decreased hydrolytic activity, and major shifts have been shown to occur in both archaeal and bacterial populations (see reviews [167, 168]). However, adaptation and degradation of these compounds is possible and is suggested to involve members within the families Syntrophorhabdaceae and Synergistaceae, combined with hydrogenotrophic methanogens [167–169]. For optimized degradation of phenolic compounds, thermophilic pretreatment has been suggested [126].

The combined results from studies performed to date suggest that pretreatment mostly causes minor structural adjustments in the prevailing AD microbial community, but still impacts the activity. It is likely that the effect of pretreatment depends strongly on the prevailing operating conditions (e.g., substrate and temperature) and the activity of the microbial community. It can be anticipated that the response in microbial community structure is also linked to the physical effects of the pretreatment on the substrate. Thus, if the pretreatment enhances the solubilization of all components in the substrate, the impact on community structure will be lower than if the pretreatment increases the solubilization of one particular compound

The hydraulic retention time (HRT) or solid retention time (SRT), i.e., the average time that the biomass is maintained in the digester, and the organic loading rate (OLR) are of great importance for the microbial community. A short HRT and a high OLR are often desirable in commercial biogas production plants, since they

*DOI: http://dx.doi.org/10.5772/intechopen.82815*

hydrolytic activity without attachment to its substrate.

(i.e., proteins, carbohydrates, or lipids).

**5.4 Loading rate and retention time**

### *Microbial Responses to Different Operating Practices for Biogas Production Systems DOI: http://dx.doi.org/10.5772/intechopen.82815*

responses in the microbial community following thermophilic aerobic digestion or ultrasonic or alkaline pretreatment [159–161]. However, in other studies investigating mesophilic AD processes, ultrasonic, microwave, and electrokinetic pretreatments have all been shown to increase the relative abundance of *Clostridiales* (phylum Firmicutes) and Cloacimonetes and decrease the relative abundance of Proteobacteria [162, 163]. Moreover, in mesophilic AD of microalgae biomass, thermal pretreatment has been found to increase the relative abundance of the families Rikenellaceae (phylum Bacteriodetes) and Anaerolineaceae (phylum Chloroflexi) and decrease the relative abundance of the phylum Proteobacteria [164]. Using metatranscriptomic analysis, Xia et al. [165] found that low-frequency ultrasonic treatment of sludge during thermophilic digestion increased the hydrolytic activity of representatives of the phyla Bacteroidetes and Cloacimonetes and increased motility and chemotaxis in members of the phylum Thermotoga. Another noteworthy finding in that study was that, among the bacteria involved in cellulose degradation, members of the order Bacteroidales were more active than members of the Clostridiales*.* Both these groups contain well-known cellulosedegrading bacteria, but members of the Bacteroidales typically do not possess the cellulosomes often seen in Clostridiales. Xia et al. [165] concluded that lowfrequency ultrasonic pretreatment allows enrichment of a community with high hydrolytic activity without attachment to its substrate.

For substrates other than sludge, Wang et al. [166] reported a weak effect on the microbial community structure during digestion of thermal pretreated distilled grain waste in thermophilic solid AD. Thermal and thermochemical pretreatment approaches are the most commonly used methods for lignocellulosic materials used for bioenergy production purposes [167]. Such methods are often efficient in breaking the carbohydrate polymers to soluble sugars and improving the accessibility of the substrate to microbial degradation, thus increasing the biogas yield. However, these pretreatments can also release inhibitors such as furfural, 5-HMF, vanillin, and other phenolic compounds [167]. Depending on concentration, these lignin-derived compounds have been found to be inhibitory to methanogen and to result in decreased hydrolytic activity, and major shifts have been shown to occur in both archaeal and bacterial populations (see reviews [167, 168]). However, adaptation and degradation of these compounds is possible and is suggested to involve members within the families Syntrophorhabdaceae and Synergistaceae, combined with hydrogenotrophic methanogens [167–169]. For optimized degradation of phenolic compounds, thermophilic pretreatment has been suggested [126].

The combined results from studies performed to date suggest that pretreatment mostly causes minor structural adjustments in the prevailing AD microbial community, but still impacts the activity. It is likely that the effect of pretreatment depends strongly on the prevailing operating conditions (e.g., substrate and temperature) and the activity of the microbial community. It can be anticipated that the response in microbial community structure is also linked to the physical effects of the pretreatment on the substrate. Thus, if the pretreatment enhances the solubilization of all components in the substrate, the impact on community structure will be lower than if the pretreatment increases the solubilization of one particular compound (i.e., proteins, carbohydrates, or lipids).

### **5.4 Loading rate and retention time**

The hydraulic retention time (HRT) or solid retention time (SRT), i.e., the average time that the biomass is maintained in the digester, and the organic loading rate (OLR) are of great importance for the microbial community. A short HRT and a high OLR are often desirable in commercial biogas production plants, since they

*Anaerobic Digestion*

**5.3 Pretreatment**

temperature variations. Experience from large-scale operations shows that constant temperature fluctuations should not exceed ±2–3°C in order to avoid instability [131]. One quite consistent effect of operation at thermophilic instead of mesophilic temperature is a higher level of Firmicutes compared with Bacteroidetes/ Proteobacteria [5, 57, 116, 118, 121, 132–136]. A high Firmicutes to Bacteroidetes ratio in mesophilic AD has been shown to correlate positively with high methane yield [137, 138]. However, an increase in this ratio has also been suggested to decrease the richness of predicted lignocellulolytic enzymes in biogas digesters, an effect attributed to lower hydrolysis in comparison with natural anaerobic systems [139]. Whether similar correlations arise in comparisons between mesophilic and thermophilic biogas communities has yet to be determined. Another characteristic feature of thermophilic communities is a higher dominance of the phylum Thermotogae [91, 114, 117, 123, 133, 135, 136, 140–143]. Members of the Thermotogae degrade polysaccharides to ethanol, acetate, CO2, and H2 [72, 144], but can also be involved in degradation of alcohols to CO2 and H2 in syntrophic

Another aspect to consider during operation at thermophilic temperature is that ammonia inhibition occurs more quickly at higher temperature, as the equilibrium between ammonium and ammonia shifts towards the latter when the temperature rises [146]. Irrespective of temperature, methanogens performing the last step in AD are among the least tolerant to ammonia and reduced methane yield, and accumulation of fatty acids is a common consequence of microbial inhibition of this group [81]. Methanogenic community changes related to temperature, often combined with increasing ammonia levels, have been reported to include positive correlations between high temperature and enhanced relative abundance of Methanobacteriales (often *Methanothermobacter*) and/or Methanomicrobiales (often *Methanoculleus*) [5, 106, 116–118, 133, 136, 140–142, 147–150]. The shift in methanogenic community often also involves a shift in acetate degradation pathway from aceticlastic methanogenesis to syntrophic acetate oxidation (SAO) [81]. However, in AD processes that seldom reach high-ammonia levels, such as AD of wastewater sludge, instant temperature changes

without associated instability have been shown to be possible [143, 151, 152].

Wastes rich in lignocellulose (e.g., forestry by-products, straw) or keratinase (e.g., waste from poultry, meat, and fish industries) and wastewater sludge have significant biogas potential [15, 16, 153, 154]. However, the complex floc structures of microbial cells in sewage sludge and the recalcitrant structure of lignocellulose make hydrolysis the rate-limiting step in AD systems [16, 155, 156]. Pretreatment is a well-proven approach to improve degradation of such waste. Common pretreatment strategies comprise physical (e.g., heat/pressure, irradiation, ultrasonic), chemical (e.g., acids/bases, ozonation, oxidation), and biological (addition of fungi/bacteria/enzymes under aerobic or anaerobic conditions) methods [16, 157]. The general concept of pretreatment is that it should improve the accessibility of the material to microbial degradation by disrupting the structure, changing the biomass porosity, and reducing the particle size to enhance the surface area that can be attacked. Many studies have investigated the effect on methane yield of pretreatment of various materials and many methods have shown improved process efficiency following pretreatment [16, 157, 158]. However, fewer studies have examined the influence of pretreatment on microbial communities and relationships to the increase in methane yield, and most of the studies performed to date have been on AD of waste-activated sludge, with differing results. For example, during mesophilic AD of sewage sludge, some studies have found no

association with a hydrogen-consuming partner [72, 145].

**20**

allow for high-quantity waste treatment and high biogas production (if the AD can maintain efficiency). However, SRT should exceed the microbial doubling time of prevailing microorganisms, in order to avoid washout of the consortium and thus process collapse. Immobilization of microorganisms through inclusion of support material or by allowing the formation of granular sludge, flocks, or biofilms is a strategy used in high HRT systems to support and maintain organisms with lower growth rate than the solid retention time [170, 171].

The response by the microbial community to change in OLR and HRT has been shown to vary depending on operating conditions such as temperature and composition of substrate [5, 6]. The prevailing microbial community at the time of OLR/ HRT change is also important for the overall response [172]. Moreover, the feeding approach, i.e., continuous or discontinuous feeding, can be determining for community changes [173]. Changes in OLR/HRT have been shown to cause a response in most phyla dominating in AD, such as Actinobacteria, Bacteroidetes, Firmicutes, Chloroflexi, Thermotogae, Cloacimonetes, and Euryarchaeota [172–176].

In the case of increasing load, bacteria associated with hydrolytic and acidogenetic activity, such as members of the Firmicutes or Bacteroidetes, have been shown to be enriched, in parallel with accumulation of accumulation of fatty acids [172, 176–178]. Typically, acetate accumulates first and propionate accumulates if the process disturbance continues, which is assumed to be caused by limited methanogenesis and excess levels of hydrogen [5, 172, 179]. In high-solid mesophilic AD, an increase in OLR has been found to decrease the relative abundance of Firmicutes and increase that of Bacteroidetes and Candidate division WS6 [174]. During increasing OLR of protein-rich waste (blood, casein) in mesophilic AD, the order Thermoanaerobacteriales, harboring several known SAOBs (e.g., *Caldanaerobacter* and *Alkaliphilius*), has been shown to increase, while the relative abundance of *Bacillus* (Bacteroidetes) decreases [10]. In thermophilic AD of lignocellulose, decreasing the retention time from 20 to 3 days has also been shown to increase the levels of Firmicutes, while Thermotogae and Chloroflexi decrease in abundance [175]. During mesophilic AD of food waste at increasing OLR (3–7 g volatile solids L<sup>−</sup><sup>1</sup> d<sup>−</sup><sup>1</sup> ) and HRT (15–20 days), a dynamic succession has been seen in different bacterial phyla (Firmicutes and Actinobacteria), while the abundance of Euryarchaeota, specifically families Methanosarcinaceae and Methanosaetaceae, increases [172].

The frequently reported increase in the genus *Methanosarcina* in response to increasing OLR has been attributed to its efficient acetate degradation capacity and robustness to stress [94]. Several studies also suggest that members of the *Methanosarcina* are important for maintained and efficient methane production under increasing OLR [172, 180]. However, members of the Methanobacteria, Methanomicrobiales, and/or Methanomassiliicoccaceae have also been observed in certain processes with a high load, depending on prevailing conditions [5, 120, 176, 179– 181]. During loading by pulsed feeding, the hydrogenotrophic Methanomicrobiales have been shown to increase, favoring the consumption of propionate, most likely through hydrogen utilization. These methanogens have also been detected in highammonia processes operating at high OLR [179]. Ferm et al. [182] and Xu et al. [172] suggest that acetate-utilizing methanogens are critical for efficient methane production during stable performance at increasing OLR. However, with "overload" and acidification, hydrogenotrophic methanogens, such as representatives of the orders Methanomicrobiales and Methanobacteriales, become more important and dominant.

### **5.5 Changes in substrate composition and feeding strategies**

Substrate composition is another parameter that strongly impacts the microbial community. It is well-known that co-digestion of different materials often achieves a more balanced nutrient level and improves the process performance and biogas yield

**23**

*Microbial Responses to Different Operating Practices for Biogas Production Systems*

[3, 183, 184]. However, the substrate availability for a commercial biogas plant may not always be optimal and the availability can also change over time. When changing substrate composition or choosing a substrate for a new AD process, the estimated energy yield and the nutrient value of the digestate generated have to be balanced against possible problems associated with different substrates, such as ammonia inhibition, acidification, and foaming. This section reviews the microbial communities commonly observed in processes fed with protein-, carbohydrate- or fat-rich material and the microbial responses to operating challenges that often occur in these processes.

Proteins are energy-rich and contribute nutrients to the digestate, but a possible effect of ammonia inhibition has to be considered in the processing. Ammonia (NH3)

in equilibrium and higher temperature and pH shift the ratio toward a higher level of ammonia. Thus, in addition to the nitrogen content, temperature and pH should be taken into account in prediction of inhibition following a change in substrate composition [186]. The aceticlastic methanogens (*Methanosaeta* sp. and certain *Methanosarcina* sp.) are considered to be most sensitive to ammonia, but if an ammonia-tolerant community is allowed to persist in the digester, the process can cope with substantially higher ammonia levels than an unadapted process [19]. An ammonia-tolerant community often includes methane formation from acetate via SAO [120, 187–193]. In SAO, acetate-oxidizing bacteria and hydrogenotrophic methanogens work in a syntrophic manner to generate methane. Bacteria species currently known to be capable of SAO belong to the genera *Thermacetogenium* [194], *Pseudothermotoga* [145, 195], *Tepidanaerobacter acetatoxydans* [196], *Clostridium* [197], and *Syntrophaceticus* [198]. Methanogenic partners in SAO are suggested to be members of the hydrogenotrophic Methanobacteriales and Methanomicrobiales (often the genus *Methanoculleus*) [81]. *Methanosarcina* is moderately ammonia-tolerant and can use both the hydrogenotrophic and aceticlastic pathways for methane formation, and can thus possibly act as a hydrogen scavenger in SAO [81, 94] or mediate the entire process, i.e., both acetate oxidation and subsequent methanogenesis [199, 200]. An increased level of protein can also affect degradation steps other than the syntrophic and methanogenic steps. For example, an increased level of protein in AD of food waste has been demonstrated to increase the abundance of the families Porphyromonadaceae, Actinomytaceae, Lactobacillaceae, and Caldicoprobacteraceae, suggesting their direct or indirect involvement in protein hydrolysis [82]. In AD of animal manure, higher protein content has been shown to increase the genera *Desulfotomaculum* and *Eubacterium* [82, 201]. High levels of ammonia have also been shown to be negatively correlated with

particularly the unionized NH3 is toxic to microorganisms [185]. NH3 and NH4

degradation of cellulose and with some potential cellulose degraders [112].

Carbohydrate-rich materials are difficult to use in mono-digestion for biogas, since the C/N ratio becomes too high for microbial activity. Carbohydrates are thus typically co-digested with more nitrogen-rich materials. However, complex carbohydrates can pose additional challenges, such as low degradability of lignocellulosic materials, while easily accessible carbohydrates undergo fast acidogenesis that can cause acidification [202, 203]. Animal manure and sludge are commonly used in co-digestion with straw (corn, rice, tobacco, wheat) and in these processes the two orders Clostridiales (phylum Firmicutes) and Bacteroidales (phylum Bacteroidetes) often dominate. However, the phyla Proteobacter, Chloroflexi, and Fibrobacteres also often increase in response

*5.5.2 Carbohydrate-rich substrate*

) are formed by the microbial degradation of proteins and in

+ exist

*DOI: http://dx.doi.org/10.5772/intechopen.82815*

+

*5.5.1 Protein-rich substrate*

and ammonium (NH4

*Microbial Responses to Different Operating Practices for Biogas Production Systems DOI: http://dx.doi.org/10.5772/intechopen.82815*

[3, 183, 184]. However, the substrate availability for a commercial biogas plant may not always be optimal and the availability can also change over time. When changing substrate composition or choosing a substrate for a new AD process, the estimated energy yield and the nutrient value of the digestate generated have to be balanced against possible problems associated with different substrates, such as ammonia inhibition, acidification, and foaming. This section reviews the microbial communities commonly observed in processes fed with protein-, carbohydrate- or fat-rich material and the microbial responses to operating challenges that often occur in these processes.

### *5.5.1 Protein-rich substrate*

*Anaerobic Digestion*

allow for high-quantity waste treatment and high biogas production (if the AD can maintain efficiency). However, SRT should exceed the microbial doubling time of prevailing microorganisms, in order to avoid washout of the consortium and thus process collapse. Immobilization of microorganisms through inclusion of support material or by allowing the formation of granular sludge, flocks, or biofilms is a strategy used in high HRT systems to support and maintain organisms with lower

The response by the microbial community to change in OLR and HRT has been shown to vary depending on operating conditions such as temperature and composition of substrate [5, 6]. The prevailing microbial community at the time of OLR/ HRT change is also important for the overall response [172]. Moreover, the feeding approach, i.e., continuous or discontinuous feeding, can be determining for community changes [173]. Changes in OLR/HRT have been shown to cause a response in most phyla dominating in AD, such as Actinobacteria, Bacteroidetes, Firmicutes,

In the case of increasing load, bacteria associated with hydrolytic and acidogenetic activity, such as members of the Firmicutes or Bacteroidetes, have been shown to be enriched, in parallel with accumulation of accumulation of fatty acids [172, 176–178]. Typically, acetate accumulates first and propionate accumulates if the process disturbance continues, which is assumed to be caused by limited methanogenesis and excess levels of hydrogen [5, 172, 179]. In high-solid mesophilic AD, an increase in OLR has been found to decrease the relative abundance of Firmicutes and increase that of Bacteroidetes and Candidate division WS6 [174]. During increasing OLR of protein-rich waste (blood, casein) in mesophilic AD, the order Thermoanaerobacteriales, harboring several known SAOBs (e.g., *Caldanaerobacter* and *Alkaliphilius*), has been shown to increase, while the relative abundance of *Bacillus* (Bacteroidetes) decreases [10]. In thermophilic AD of lignocellulose, decreasing the retention time from 20 to 3 days has also been shown to increase the levels of Firmicutes, while Thermotogae and Chloroflexi decrease in abundance [175]. During mesophilic AD of food waste at increasing OLR (3–7 g volatile

) and HRT (15–20 days), a dynamic succession has been seen in different

bacterial phyla (Firmicutes and Actinobacteria), while the abundance of Euryarchaeota,

Methanomicrobiales, and/or Methanomassiliicoccaceae have also been observed in certain processes with a high load, depending on prevailing conditions [5, 120, 176, 179– 181]. During loading by pulsed feeding, the hydrogenotrophic Methanomicrobiales have been shown to increase, favoring the consumption of propionate, most likely through hydrogen utilization. These methanogens have also been detected in highammonia processes operating at high OLR [179]. Ferm et al. [182] and Xu et al. [172] suggest that acetate-utilizing methanogens are critical for efficient methane production during stable performance at increasing OLR. However, with "overload" and acidification, hydrogenotrophic methanogens, such as representatives of the orders Methanomicrobiales and Methanobacteriales, become more important and dominant.

Substrate composition is another parameter that strongly impacts the microbial community. It is well-known that co-digestion of different materials often achieves a more balanced nutrient level and improves the process performance and biogas yield

specifically families Methanosarcinaceae and Methanosaetaceae, increases [172]. The frequently reported increase in the genus *Methanosarcina* in response to increasing OLR has been attributed to its efficient acetate degradation capacity and robustness to stress [94]. Several studies also suggest that members of the *Methanosarcina* are important for maintained and efficient methane production under increasing OLR [172, 180]. However, members of the Methanobacteria,

**5.5 Changes in substrate composition and feeding strategies**

Chloroflexi, Thermotogae, Cloacimonetes, and Euryarchaeota [172–176].

growth rate than the solid retention time [170, 171].

**22**

solids L<sup>−</sup><sup>1</sup>

d<sup>−</sup><sup>1</sup>

Proteins are energy-rich and contribute nutrients to the digestate, but a possible effect of ammonia inhibition has to be considered in the processing. Ammonia (NH3) and ammonium (NH4 + ) are formed by the microbial degradation of proteins and in particularly the unionized NH3 is toxic to microorganisms [185]. NH3 and NH4 + exist in equilibrium and higher temperature and pH shift the ratio toward a higher level of ammonia. Thus, in addition to the nitrogen content, temperature and pH should be taken into account in prediction of inhibition following a change in substrate composition [186]. The aceticlastic methanogens (*Methanosaeta* sp. and certain *Methanosarcina* sp.) are considered to be most sensitive to ammonia, but if an ammonia-tolerant community is allowed to persist in the digester, the process can cope with substantially higher ammonia levels than an unadapted process [19]. An ammonia-tolerant community often includes methane formation from acetate via SAO [120, 187–193]. In SAO, acetate-oxidizing bacteria and hydrogenotrophic methanogens work in a syntrophic manner to generate methane. Bacteria species currently known to be capable of SAO belong to the genera *Thermacetogenium* [194], *Pseudothermotoga* [145, 195], *Tepidanaerobacter acetatoxydans* [196], *Clostridium* [197], and *Syntrophaceticus* [198]. Methanogenic partners in SAO are suggested to be members of the hydrogenotrophic Methanobacteriales and Methanomicrobiales (often the genus *Methanoculleus*) [81]. *Methanosarcina* is moderately ammonia-tolerant and can use both the hydrogenotrophic and aceticlastic pathways for methane formation, and can thus possibly act as a hydrogen scavenger in SAO [81, 94] or mediate the entire process, i.e., both acetate oxidation and subsequent methanogenesis [199, 200]. An increased level of protein can also affect degradation steps other than the syntrophic and methanogenic steps. For example, an increased level of protein in AD of food waste has been demonstrated to increase the abundance of the families Porphyromonadaceae, Actinomytaceae, Lactobacillaceae, and Caldicoprobacteraceae, suggesting their direct or indirect involvement in protein hydrolysis [82]. In AD of animal manure, higher protein content has been shown to increase the genera *Desulfotomaculum* and *Eubacterium* [82, 201]. High levels of ammonia have also been shown to be negatively correlated with degradation of cellulose and with some potential cellulose degraders [112].

### *5.5.2 Carbohydrate-rich substrate*

Carbohydrate-rich materials are difficult to use in mono-digestion for biogas, since the C/N ratio becomes too high for microbial activity. Carbohydrates are thus typically co-digested with more nitrogen-rich materials. However, complex carbohydrates can pose additional challenges, such as low degradability of lignocellulosic materials, while easily accessible carbohydrates undergo fast acidogenesis that can cause acidification [202, 203]. Animal manure and sludge are commonly used in co-digestion with straw (corn, rice, tobacco, wheat) and in these processes the two orders Clostridiales (phylum Firmicutes) and Bacteroidales (phylum Bacteroidetes) often dominate. However, the phyla Proteobacter, Chloroflexi, and Fibrobacteres also often increase in response

to addition of lignocellulosic materials, with some variation depending on codigestion material and prevailing environmental conditions [118, 202, 204–208]. The microbial community structure in AD of rice straw has been shown to be influenced by temperature, with a higher ratio of Firmicutes to Bacteroidetes being reported at higher temperature [208]. In mesophilic AD of rice straw, Bacteroidetes is reported to be the most prevalent group and the abundance is not influenced by increased OLR, whereas the second most abundant Firmicutes decreases slightly [209]. Metagenomic studies have confirmed the involvement of the phyla Proteobacteria, Firmicutes, Chloroflexi, and Bacteroidetes, but also Actinomycetes, in the degradation of lignocellulose by demonstrating the existence of CAZymes (Carbohydrate-Active Enzymes) in consortia adapted to lignocellulosic materials [59, 202].

Interestingly, similar community profiles as described above are often seen in AD of material containing comparatively high levels of easily accessible carbohydrates. For example, in co-digestion of fruit and vegetable waste with pig manure, the phyla Firmicutes, Bacteroidetes, Chloroflexi, Proteobacteria, and Actinobacteria have been found to dominate, but the numbers of Firmicutes decrease when the fraction of fruit and vegetable waste (with the highest levels of carbohydrates) decreases [210]. In mesophilic AD of potato and cabbage waste (alone or in combination), members of the phyla Spirochaete, Bacteroidetes, Firmicutes, and Proteobacteria vary in numbers depending on the substrate combination [203]. In a study examining addition of cellulose and xylan to wastewater sludge, it was found that this increased the relative abundance of the bacterial genus *Clostridium* (phylum Firmicutes), whereas the levels of the bacterial phyla Thermotogae and Bacteroidetes decreased [211]. In thermophilic AD of cattle manure involving addition of easily degraded carbohydrates in the form of glucose, the genus *Lactobacillus* (class Bacilli) has been shown to increase [201]. The methanogenic communities identified in various studies on carbohydrate-rich material show diverging structures and appear to be primarily shaped by the co-substrate and prevailing environmental conditions. For example, during straw co-digestion with cow manure or digestion of straw alone, *Methanosarcina* or *Methanosaeta* often dominate [204–207, 208, 209]. However, with increasing nitrogen level, temperature, OLR, and/or carbohydrate accessibility, the contribution of hydogenotropic methanogenesis increases, involving *Methanoculleus, Methanothermobacter*, and *Methanobacterium* [201–203, 208, 209].

### *5.5.3 Lipid-rich substrate*

Lipids are energy-rich and different fat-rich substrates are often used to boost biogas production from sewage and manure [212–215]. Degradation of fat results in glycerol and LCFA, with the latter being a known microbial inhibitor [23]. The bacteria *Syntrophomonas* (family Syntrophomonadaceae) is commonly enriched in mesophilic co-digestion of lipid-rich materials [216–224] and even represents as much as 30–40% of the total bacterial community during degradation of LCFA [218, 225]. Moreover, it has been reported [218] that pulse feeding of oleate, instead of continuous feeding of oleate, increases the conversion rates of oleate and acetate and induces greater metabolic flexibility within the LCFA-degrading community dominated by *Syntrophomonas* population [76]. In thermophilic degradation of animal manure, addition of oleate has been shown to increase the relative abundance of the glycerol- and inositol-fermenting *Megamonas* (phylum Firmicutes) [201], whereas in mesophilic AD increased levels of glycerol/glycerin enrich the phyla Cloacamonas [226] and Thermotogae in AD of wastewater sludge [227] and the genus *Trichococcus* and family Syntrophomonadaceae in AD of brewery wastewater [228]. *Methanoculleus*, *Methanobacterium*, and *Methanospirillum* have been proposed as important hydrogen-utilizing partners for LCFA-degrading bacteria, whereas *Methanosarcina* has been suggested to act both

**25**

**5.7 Bioaugmentation**

and reduce the time following overload [250].

*Microbial Responses to Different Operating Practices for Biogas Production Systems*

as a hydrogen and acetate consumer [216, 229, 230]. However, in pulse feeding of oleate, *Methanosaeta* increases in importance relative to *Methanosarcina*, along with higher abundance of *Methanoculleus* compared with *Methanobacterium*. This was suggested by the authors to be a consequence of higher acetate affinity and tolerance for LCFA by *Methanosaeta* and higher affinity for hydrogen by *Methanoculleu*s [218]. In another study, an increased level of the hydrogenotrophic *Methanoculleus* and *Methanobrevibacter* was linked to increased methane production from oleate, driven by enhanced concentration of sulfide [224]. In addition to aceticlastic methanogensis, acetate degradation has also been shown to proceed via syntrophic acetate oxidation

during LCFA conversion, which is likely linked to high-ammonia level [216].

acetate oxidizers *S. schinkii*, *T. acetatoxydanse*, and *C. ultunense* [120, 200].

The approach of adding microorganisms to the anaerobic process is based on the belief that slow degradation is due to the absence or low abundance of efficient populations responsible for the particular degradation step. Bioaugmentation could thus shorten the time of microbial adaptation to certain environmental conditions/ inhibitors and/or improve methane yield from specific substrates. Since the hydrolytic and methanogenic steps generally appear to be bottlenecks in AD systems, bioaugmentation efforts to date have most commonly been directed at enhancing these two steps. However, bioaugmentation has also been evaluated for improving the transition to psychrophilic temperature, to overcome inhibition of ammonia

Trace element deficiency can severely limit microbial activity and cause accumulation of fatty acids, process instability and decreased methane yield from food waste [21, 120, 231, 232], slaughterhouse waste [233, 234], crop material [235], stillage [236], and animal manure, when used as a single substrate or as co-substrate [237, 238]. In this regard, it is important to consider the level of sulfide, which is primarily formed through protein degradation. Sulfide forms complexes with metals, which decreases the bioavailability of trace elements essential for microbial activity [239–241]. In addition, temperature has been suggested to impact nutrient bioavailability and nutrient requirements [242, 243]. However, the actual impact of different temperatures on the availability of trace metals has yet to be established. The trace elements such as cobalt, nickel, iron, molybdenum, and tungsten are essential trace elements, especially for acetogenic and methanogenic microorganisms [244–246]. So far, mainly methanogenic abundance has been shown to be influenced by trace element addition in AD, while less is known about the response in the bacterial community. Thus, it is not clear whether the improved degradation of LCFA and VFA with trace element addition is caused solely by improved activity of methanogens or also improved activity of the syntrophic community. Trace elements have demonstrated to have a pronounced effect on the methanogenic community, including increased abundance or predicted stimulatory effects on the genus *Methanoculleus* [120, 247] and increased abundance of the order Methanosarcinales [200] and the genus *Methanobrevibacter* (order Methanobacteriales), all in mesophilic AD [247]. *Methanoculleus* has also been proposed to have a more efficient strategy than *Methanosarcina* for stabilizing its energy balance, and thus can cope more successfully with trace element limitation [248, 249]. Interestingly, despite improved VFA conversion following trace element addition, SAO-dominated AD processes are reported to show no or decreased abundance of the known syntrophic

*DOI: http://dx.doi.org/10.5772/intechopen.82815*

**5.6 Addition of trace elements**

*Microbial Responses to Different Operating Practices for Biogas Production Systems DOI: http://dx.doi.org/10.5772/intechopen.82815*

as a hydrogen and acetate consumer [216, 229, 230]. However, in pulse feeding of oleate, *Methanosaeta* increases in importance relative to *Methanosarcina*, along with higher abundance of *Methanoculleus* compared with *Methanobacterium*. This was suggested by the authors to be a consequence of higher acetate affinity and tolerance for LCFA by *Methanosaeta* and higher affinity for hydrogen by *Methanoculleu*s [218]. In another study, an increased level of the hydrogenotrophic *Methanoculleus* and *Methanobrevibacter* was linked to increased methane production from oleate, driven by enhanced concentration of sulfide [224]. In addition to aceticlastic methanogensis, acetate degradation has also been shown to proceed via syntrophic acetate oxidation during LCFA conversion, which is likely linked to high-ammonia level [216].

### **5.6 Addition of trace elements**

*Anaerobic Digestion*

to addition of lignocellulosic materials, with some variation depending on co-

in consortia adapted to lignocellulosic materials [59, 202].

*Methanothermobacter*, and *Methanobacterium* [201–203, 208, 209].

Lipids are energy-rich and different fat-rich substrates are often used to boost biogas production from sewage and manure [212–215]. Degradation of fat results in glycerol and LCFA, with the latter being a known microbial inhibitor [23]. The bacteria *Syntrophomonas* (family Syntrophomonadaceae) is commonly enriched in mesophilic co-digestion of lipid-rich materials [216–224] and even represents as much as 30–40% of the total bacterial community during degradation of LCFA [218, 225]. Moreover, it has been reported [218] that pulse feeding of oleate, instead of continuous feeding of oleate, increases the conversion rates of oleate and acetate and induces greater metabolic flexibility within the LCFA-degrading community dominated by *Syntrophomonas* population [76]. In thermophilic degradation of animal manure, addition of oleate has been shown to increase the relative abundance of the glycerol- and inositol-fermenting *Megamonas* (phylum Firmicutes) [201], whereas in mesophilic AD increased levels of glycerol/glycerin enrich the phyla Cloacamonas [226] and Thermotogae in AD of wastewater sludge [227] and the genus *Trichococcus* and family Syntrophomonadaceae

in AD of brewery wastewater [228]. *Methanoculleus*, *Methanobacterium*, and *Methanospirillum* have been proposed as important hydrogen-utilizing partners for LCFA-degrading bacteria, whereas *Methanosarcina* has been suggested to act both

*5.5.3 Lipid-rich substrate*

digestion material and prevailing environmental conditions [118, 202, 204–208]. The microbial community structure in AD of rice straw has been shown to be influenced by temperature, with a higher ratio of Firmicutes to Bacteroidetes being reported at higher temperature [208]. In mesophilic AD of rice straw, Bacteroidetes is reported to be the most prevalent group and the abundance is not influenced by increased OLR, whereas the second most abundant Firmicutes decreases slightly [209]. Metagenomic studies have confirmed the involvement of the phyla Proteobacteria, Firmicutes, Chloroflexi, and Bacteroidetes, but also Actinomycetes, in the degradation of lignocellulose by demonstrating the existence of CAZymes (Carbohydrate-Active Enzymes)

Interestingly, similar community profiles as described above are often seen in AD of material containing comparatively high levels of easily accessible carbohydrates. For example, in co-digestion of fruit and vegetable waste with pig manure, the phyla Firmicutes, Bacteroidetes, Chloroflexi, Proteobacteria, and Actinobacteria have been found to dominate, but the numbers of Firmicutes decrease when the fraction of fruit and vegetable waste (with the highest levels of carbohydrates) decreases [210]. In mesophilic AD of potato and cabbage waste (alone or in combination), members of the phyla Spirochaete, Bacteroidetes, Firmicutes, and Proteobacteria vary in numbers depending on the substrate combination [203]. In a study examining addition of cellulose and xylan to wastewater sludge, it was found that this increased the relative abundance of the bacterial genus *Clostridium* (phylum Firmicutes), whereas the levels of the bacterial phyla Thermotogae and Bacteroidetes decreased [211]. In thermophilic AD of cattle manure involving addition of easily degraded carbohydrates in the form of glucose, the genus *Lactobacillus* (class Bacilli) has been shown to increase [201]. The methanogenic communities identified in various studies on carbohydrate-rich material show diverging structures and appear to be primarily shaped by the co-substrate and prevailing environmental conditions. For example, during straw co-digestion with cow manure or digestion of straw alone, *Methanosarcina* or *Methanosaeta* often dominate [204–207, 208, 209]. However, with increasing nitrogen level, temperature, OLR, and/or carbohydrate accessibility, the contribution of hydogenotropic methanogenesis increases, involving *Methanoculleus,* 

**24**

Trace element deficiency can severely limit microbial activity and cause accumulation of fatty acids, process instability and decreased methane yield from food waste [21, 120, 231, 232], slaughterhouse waste [233, 234], crop material [235], stillage [236], and animal manure, when used as a single substrate or as co-substrate [237, 238]. In this regard, it is important to consider the level of sulfide, which is primarily formed through protein degradation. Sulfide forms complexes with metals, which decreases the bioavailability of trace elements essential for microbial activity [239–241]. In addition, temperature has been suggested to impact nutrient bioavailability and nutrient requirements [242, 243]. However, the actual impact of different temperatures on the availability of trace metals has yet to be established.

The trace elements such as cobalt, nickel, iron, molybdenum, and tungsten are essential trace elements, especially for acetogenic and methanogenic microorganisms [244–246]. So far, mainly methanogenic abundance has been shown to be influenced by trace element addition in AD, while less is known about the response in the bacterial community. Thus, it is not clear whether the improved degradation of LCFA and VFA with trace element addition is caused solely by improved activity of methanogens or also improved activity of the syntrophic community. Trace elements have demonstrated to have a pronounced effect on the methanogenic community, including increased abundance or predicted stimulatory effects on the genus *Methanoculleus* [120, 247] and increased abundance of the order Methanosarcinales [200] and the genus *Methanobrevibacter* (order Methanobacteriales), all in mesophilic AD [247]. *Methanoculleus* has also been proposed to have a more efficient strategy than *Methanosarcina* for stabilizing its energy balance, and thus can cope more successfully with trace element limitation [248, 249]. Interestingly, despite improved VFA conversion following trace element addition, SAO-dominated AD processes are reported to show no or decreased abundance of the known syntrophic acetate oxidizers *S. schinkii*, *T. acetatoxydanse*, and *C. ultunense* [120, 200].

### **5.7 Bioaugmentation**

The approach of adding microorganisms to the anaerobic process is based on the belief that slow degradation is due to the absence or low abundance of efficient populations responsible for the particular degradation step. Bioaugmentation could thus shorten the time of microbial adaptation to certain environmental conditions/ inhibitors and/or improve methane yield from specific substrates. Since the hydrolytic and methanogenic steps generally appear to be bottlenecks in AD systems, bioaugmentation efforts to date have most commonly been directed at enhancing these two steps. However, bioaugmentation has also been evaluated for improving the transition to psychrophilic temperature, to overcome inhibition of ammonia and reduce the time following overload [250].

For the degradation of lignocellulosic material in the biogas process, bioaugmentation with cellulose-degrading bacteria, hydrolytic enzymes, and anaerobic fungi has been suggested as a promising method to increase methane production from lignocellulosic materials [251–254]. Microorganisms that have so far shown positive results on methane yield include the cellulolytic bacteria *Clostridium cellulolyticulm*, *Acetobacteroides hydrogenigenes*, and *Caldicellulosiruptor lactoaceticus* (*Caldicellulosiruptor*) and the fungus *Piromyces rhizinflata*. A mix of cultures of different *Clostridium* sp. and different hemicellulose and cellulolytic bacteria has also been shown to produce positive results [250], while a mixed consortium with high endoglucanase activity has been found to result in increased biogas production from maize silage [255]. For addition of enzymes, investigations have shown mixed effects, ranging from no effect at all on rate or yield, to increased biogas yield only, or increased rate only (summarized in [252]). A likely explanation for the nonconclusive results from addition of enzyme/organisms is differences in the environmental conditions prevailing in the digester, such pH and ammonia level, which vary greatly depending on substrate. For example, a clear correlation between inefficient cellulose degradation and high-ammonia levels has been demonstrated [53]. The amount of added microorganisms has also been suggested to be of critical importance [250]. For enzyme addition, another possible reason behind the variation in results is that the hydrolytic enzymes investigated so far have mainly originated from nonbiogas environments and have a very short activity lifetime (<24 h) in the biogas process, which restricts the hydrolytic activity within these systems [256]. However, a study investigating the effects of addition of enzymes or microbes retrieved from a specific biogas environment has found promising results [252]. In that study, these enzymes were found to be active and stable in the environment and had a profound effect on both the biogas production rate and yield from forage ley [252]. Moreover, Azman et al. [257] have demonstrated that addition of hydrolytic enzymes to a cellulose and xylan-fed digester operating at 30°C can counteract the inhibitory effects of humic acid on hydrolysis efficiency.

The degradation of fats has been shown to be stimulated by the addition of hydrolyzing enzymes (lipases) or fat-degrading bacteria (*Syntrophomonas zehnderi* and *Clostridium lundense*) [250, 258], whereas addition of a co-culture of *Syntrophomonas zehnderi* and *Methanobacterium formicicum* is reported to have no effect in AD of fat-rich wastewater [259]. For protein, bioaugmentation with *Coprothermobacter proteolyticus* has been shown to improve hydrolysis and fermentation in waste-activated sludge [260]. Another factor to consider when attempting to improve the degradation of fat and protein is increased release of LCFA and ammonia. For example, high concentrations of lipases have been shown to inhibit the process, probably due to the release of LCFA. Moreover, LCFA and ammonia have been shown to have additive effects, so that the process becomes more severely inhibited if both are present at relatively high concentrations [205].

Previous attempts to increase the stability and activity of the methanogenic community have included addition of *Methanosarcina* sp. during start-up [111]. Moreover, bioaugmentation with syntrophic-acetate degrading co-cultures and with ammonia-tolerant *Methanoculleus bourgensis* has been tested with the aim of preventing ammonia inhibition of the process [189, 261, 262]. Test results in that case revealed that addition of syntrophic co-cultures did not facilitate a dynamic transition from aceticlastic methanogenesis to SAO, whereas addition of ammoniatolerant *M. bourgensis* improved adaptation to gradually increased ammonia concentrations under mesophilic conditions.

**27**

provided the original work is properly cited.

Maria Westerholm and Anna Schnürer\*

\*Address all correspondence to: anna.schnurer@slu.se

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

Department of Molecular Sciences, Swedish University of Agricultural Sciences,

*Microbial Responses to Different Operating Practices for Biogas Production Systems*

community structure and operating parameters and performance.

Biogas production through anaerobic digestion enables recovery of renewable energy and of nutrients from various organic waste materials and is thus highly important for the transition to a more sustainable society. The performance and stability of the biodigestion process is highly dependent on an array of different microbial groups, and their networks and functions are in turn influenced by substrate characteristic and operating parameters. With recent advances in molecular techniques, knowledge about anaerobic microorganisms and their response to various operating conditions has increased tremendously. This knowledge has enabled the development of more controlled management and monitoring approaches, to ensure high process efficiency and stability. However, with increasing knowledge about the microbiology of biogas processes, it has also become evident that the microbiota involved is even more complicated and difficult to visualize than initially thought, particularly as many members within a particular genus are often able to degrade chemically very different compounds. Moreover, many organisms belong to candidate phyla or are even unknown, and remain to be isolated and characterized for full understanding of their role in the biogas system. Thus, in order to establish effective operating policies to achieve maximum biogas process performance, it is important to improve understanding about microorganisms and their functions and to further develop a predictive understanding of the interplay between microbial

*DOI: http://dx.doi.org/10.5772/intechopen.82815*

**6. Conclusions**

**Conflict of interest**

None declared.

**Author details**

Uppsala, Sweden

*Microbial Responses to Different Operating Practices for Biogas Production Systems DOI: http://dx.doi.org/10.5772/intechopen.82815*

### **6. Conclusions**

*Anaerobic Digestion*

humic acid on hydrolysis efficiency.

centrations under mesophilic conditions.

concentrations [205].

For the degradation of lignocellulosic material in the biogas process, bioaugmentation with cellulose-degrading bacteria, hydrolytic enzymes, and anaerobic fungi has been suggested as a promising method to increase methane production from lignocellulosic materials [251–254]. Microorganisms that have so far shown positive results on methane yield include the cellulolytic bacteria *Clostridium cellulolyticulm*, *Acetobacteroides hydrogenigenes*, and *Caldicellulosiruptor lactoaceticus* (*Caldicellulosiruptor*) and the fungus *Piromyces rhizinflata*. A mix of cultures of different *Clostridium* sp. and different hemicellulose and cellulolytic bacteria has also been shown to produce positive results [250], while a mixed consortium with high endoglucanase activity has been found to result in increased biogas production from maize silage [255]. For addition of enzymes, investigations have shown mixed effects, ranging from no effect at all on rate or yield, to increased biogas yield only, or increased rate only (summarized in [252]). A likely explanation for the nonconclusive results from addition of enzyme/organisms is differences in the environmental conditions prevailing in the digester, such pH and ammonia level, which vary greatly depending on substrate. For example, a clear correlation between inefficient cellulose degradation and high-ammonia levels has been demonstrated [53]. The amount of added microorganisms has also been suggested to be of critical importance [250]. For enzyme addition, another possible reason behind the variation in results is that the hydrolytic enzymes investigated so far have mainly originated from nonbiogas environments and have a very short activity lifetime (<24 h) in the biogas process, which restricts the hydrolytic activity within these systems [256]. However, a study investigating the effects of addition of enzymes or microbes retrieved from a specific biogas environment has found promising results [252]. In that study, these enzymes were found to be active and stable in the environment and had a profound effect on both the biogas production rate and yield from forage ley [252]. Moreover, Azman et al. [257] have demonstrated that addition of hydrolytic enzymes to a cellulose and xylan-fed digester operating at 30°C can counteract the inhibitory effects of

The degradation of fats has been shown to be stimulated by the addition of hydrolyzing enzymes (lipases) or fat-degrading bacteria (*Syntrophomonas zehnderi* and *Clostridium lundense*) [250, 258], whereas addition of a co-culture of *Syntrophomonas zehnderi* and *Methanobacterium formicicum* is reported to have no effect in AD of fat-rich wastewater [259]. For protein, bioaugmentation with *Coprothermobacter proteolyticus* has been shown to improve hydrolysis and fermentation in waste-activated sludge [260]. Another factor to consider when attempting to improve the degradation of fat and protein is increased release of LCFA and ammonia. For example, high concentrations of lipases have been shown to inhibit the process, probably due to the release of LCFA. Moreover, LCFA and ammonia have been shown to have additive effects, so that the process becomes more severely inhibited if both are present at relatively high

Previous attempts to increase the stability and activity of the methanogenic community have included addition of *Methanosarcina* sp. during start-up [111]. Moreover, bioaugmentation with syntrophic-acetate degrading co-cultures and with ammonia-tolerant *Methanoculleus bourgensis* has been tested with the aim of preventing ammonia inhibition of the process [189, 261, 262]. Test results in that case revealed that addition of syntrophic co-cultures did not facilitate a dynamic transition from aceticlastic methanogenesis to SAO, whereas addition of ammoniatolerant *M. bourgensis* improved adaptation to gradually increased ammonia con-

**26**

Biogas production through anaerobic digestion enables recovery of renewable energy and of nutrients from various organic waste materials and is thus highly important for the transition to a more sustainable society. The performance and stability of the biodigestion process is highly dependent on an array of different microbial groups, and their networks and functions are in turn influenced by substrate characteristic and operating parameters. With recent advances in molecular techniques, knowledge about anaerobic microorganisms and their response to various operating conditions has increased tremendously. This knowledge has enabled the development of more controlled management and monitoring approaches, to ensure high process efficiency and stability. However, with increasing knowledge about the microbiology of biogas processes, it has also become evident that the microbiota involved is even more complicated and difficult to visualize than initially thought, particularly as many members within a particular genus are often able to degrade chemically very different compounds. Moreover, many organisms belong to candidate phyla or are even unknown, and remain to be isolated and characterized for full understanding of their role in the biogas system. Thus, in order to establish effective operating policies to achieve maximum biogas process performance, it is important to improve understanding about microorganisms and their functions and to further develop a predictive understanding of the interplay between microbial community structure and operating parameters and performance.

### **Conflict of interest**

None declared.

### **Author details**

Maria Westerholm and Anna Schnürer\* Department of Molecular Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden

\*Address all correspondence to: anna.schnurer@slu.se

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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**28**

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[233] Bayr S, Pakarinene O, Korppoo A, Liuksia S, Väisänen A, Kaparaju P, et al. Effect of additives on process stability of mesophilic anaerobic monodigestion of pig slaughterhouse waste. Bioresource Technology. 2012;**120**:106-113. DOI: 10.1016/j.biortech.2012.06.009

[234] Eftaxias A, Diamantis V, Aivasidis A. Anaerobic digestion of thermal pre-treated emulsified slaughterhouse wastes (TESW): Effect of trace element limitation on process efficiency and sludge metabolic properties. Waste Management. 2018;**76**:357-363. DOI: 10.1016/j.wasman.2018.02.032

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[255] Poszytek K, Ciezkowska M, Sklodowska A, Drewniak L. Microbial Consortium with High Cellulolytic Activity (MCHCA) for enhanced biogas production. Frontiers in Microbiology. 2016;**7**:324. DOI: 10.3389/fmicb.2016.00324

[256] Odnell A, Recktenwald M, Stensén K, Jonsson B, Karlsson M. Activity, life time and effect of hydrolytic enzymes for enhanced biogas production from sludge anaerobic digestion. Water Research. 2016;**103**:462-471. DOI: 10.1016/j. watres.2016.07.064

[257] Azman S, Khadem AF, Plugge CM, Stams AJM, Bec S, Zeeman G. Effect of humic acid on anaerobic digestion of cellulose and xylan in completely stirred tank reactors: inhibitory effect, mitigation of the inhibition and the dynamics of the microbial communities. Applied Microbiology and Biotechnology. 2017;**101**:889-901. DOI: 10.1007/s00253-016-8010-x

[258] Cirne DG, Paloumet X, Bjornsson L, Alves MM, Mattiasson B. Anaerobic digestion of lipid-rich waste—Effects of lipid concentration. Renewable Energy. 2007;**32**:965-975. DOI: 10.1016/j. renene.2006.04.003

[259] Silva SA, Cavaleiro AJ, Pereira MA, Stams AJM, Alves MM, Sousa DZ. Longterm acclimation of anaerobic sludges for high-rate methanogenesis from LCFA. Biomass and Bioenergy. 2014;**67**:297-303. DOI: 10.1016/j. biombioe.2014.05.012

[260] Sasaki K, Morita M, Sasaki D, Nagaoka J, Matsumoto N, POhmura N, et al. Syntrophic degradation of proteinaceous materials by the thermophilic strains *Coprothermobacter proteolyticus* and *Methanothermobacter thermautotrophicus*. Journal of Bioscience and Bioengineering. 2011;**112**:469-472. DOI: 10.1016/j. jbiosc.2011.07.003

[261] Fotidis IA, Karakashev D, Angelidaki I. Bioaugmentation with an acetate-oxidising consortium as a tool to tackle ammonia inhibition of anaerobic digestion. Bioresource Technology. 2013;**146**:57-62. DOI: 10.1016/j. biortech.2013.07.041

[262] Fotidis IA, Wang H, Fiedel N-R, Luo G, Karakashev DB. Bioaugmentation as a solution

to increase methane production from an ammonia-rich substrate. Environmental Science & Technology. 2014;**48**:7669-7676. DOI: 10.1021/ es5017075

Chapter 3

Abstract

of anaerobic digestion research.

1. Introduction

alternative energy source.

49

methanogenesis, syntrophy, metabolic pathways

Searching for Metabolic Pathways

The general scheme of anaerobic digestion is well known. It is a complex process promoted by the interaction of many groups of microorganisms and has four major steps: hydrolysis, acidogenesis, acetogenesis, and methanogenesis. The aim of the study was to prepare a systematized list of the selected enzymes responsible for the key pathways of anaerobic digestion based on the Kyoto Encyclopedia of Genes and Genomes database resource. The list contains (i) key groups of hydrolases involved in the process of degradation of organic matter; (ii) the enzymes catalyzing reactions leading to pyruvate formation; (iii) the enzymes of metabolic pathways of further pyruvate transformations; (iv) the enzymes of glycerol transformations; (v) the enzymes involved in transformation of gaseous or nongaseous products of acidic fermentations resulting from nonsyntrophic nutritional interactions between

microbes; (vi) the enzymes of amino acid fermentations; (vii) the enzymes involved in acetogenesis; and (viii) the enzymes of the recognized pathways of methanogenesis. Searching for the presence and activity of the enzymes as well as linking structure and function of microbial communities allows to develop a fundamental understanding of the processes, leading to methane production. In this contribution, the present study is believed to be a piece to the enzymatic road map

Keywords: anaerobic digestion, enzymes, hydrolysis, acidogenesis, acetogenesis,

Anaerobic digestion (AD), whose final products are methane and carbon dioxide, is a common process in natural anoxic environments such as water sediments, wetlands, or marshlands. The environments have to be rich in organic matter and poor with other electron acceptors such as nitrate, compounds containing oxidized forms of metals, and sulfate. AD is also common in landfills and wastewater treatment plants and was used by man to produce biogas from waste biomass as an

AD is a complex process that requires the metabolic interaction of many groups of microorganisms responsible for four closely related major steps. The first one is hydrolysis of complex organic polymers (e.g., polysaccharides, lipids, proteins) to

of Anaerobic Digestion: A Useful

List of the Key Enzymes

Anna Sikora, Anna Detman, Damian Mielecki,

Aleksandra Chojnacka and Mieczysław Błaszczyk

### Chapter 3

*Anaerobic Digestion*

watres.2016.07.064

renene.2006.04.003

biombioe.2014.05.012

enhanced biogas production from sludge anaerobic digestion. Water Research. 2016;**103**:462-471. DOI: 10.1016/j.

to increase methane production from an ammonia-rich substrate. Environmental Science & Technology. 2014;**48**:7669-7676. DOI: 10.1021/

es5017075

[257] Azman S, Khadem AF, Plugge CM, Stams AJM, Bec S, Zeeman G. Effect of humic acid on anaerobic digestion of cellulose and xylan in completely stirred tank reactors: inhibitory effect, mitigation of the inhibition and the dynamics of the microbial communities. Applied Microbiology and Biotechnology. 2017;**101**:889-901. DOI: 10.1007/s00253-016-8010-x

[258] Cirne DG, Paloumet X, Bjornsson L, Alves MM, Mattiasson B. Anaerobic digestion of lipid-rich waste—Effects of lipid concentration. Renewable Energy. 2007;**32**:965-975. DOI: 10.1016/j.

[259] Silva SA, Cavaleiro AJ, Pereira MA, Stams AJM, Alves MM, Sousa DZ. Longterm acclimation of anaerobic sludges for high-rate methanogenesis from LCFA. Biomass and Bioenergy. 2014;**67**:297-303. DOI: 10.1016/j.

[260] Sasaki K, Morita M, Sasaki D, Nagaoka J, Matsumoto N, POhmura N, et al. Syntrophic degradation of proteinaceous materials by the

*thermautotrophicus*. Journal of Bioscience and Bioengineering. 2011;**112**:469-472. DOI: 10.1016/j.

[261] Fotidis IA, Karakashev D,

Angelidaki I. Bioaugmentation with an acetate-oxidising consortium as a tool to tackle ammonia inhibition of anaerobic digestion. Bioresource Technology. 2013;**146**:57-62. DOI: 10.1016/j.

[262] Fotidis IA, Wang H, Fiedel N-R,

jbiosc.2011.07.003

biortech.2013.07.041

Luo G, Karakashev DB. Bioaugmentation as a solution

thermophilic strains *Coprothermobacter proteolyticus* and *Methanothermobacter* 

**48**

## Searching for Metabolic Pathways of Anaerobic Digestion: A Useful List of the Key Enzymes

Anna Sikora, Anna Detman, Damian Mielecki, Aleksandra Chojnacka and Mieczysław Błaszczyk

### Abstract

The general scheme of anaerobic digestion is well known. It is a complex process promoted by the interaction of many groups of microorganisms and has four major steps: hydrolysis, acidogenesis, acetogenesis, and methanogenesis. The aim of the study was to prepare a systematized list of the selected enzymes responsible for the key pathways of anaerobic digestion based on the Kyoto Encyclopedia of Genes and Genomes database resource. The list contains (i) key groups of hydrolases involved in the process of degradation of organic matter; (ii) the enzymes catalyzing reactions leading to pyruvate formation; (iii) the enzymes of metabolic pathways of further pyruvate transformations; (iv) the enzymes of glycerol transformations; (v) the enzymes involved in transformation of gaseous or nongaseous products of acidic fermentations resulting from nonsyntrophic nutritional interactions between microbes; (vi) the enzymes of amino acid fermentations; (vii) the enzymes involved in acetogenesis; and (viii) the enzymes of the recognized pathways of methanogenesis. Searching for the presence and activity of the enzymes as well as linking structure and function of microbial communities allows to develop a fundamental understanding of the processes, leading to methane production. In this contribution, the present study is believed to be a piece to the enzymatic road map of anaerobic digestion research.

Keywords: anaerobic digestion, enzymes, hydrolysis, acidogenesis, acetogenesis, methanogenesis, syntrophy, metabolic pathways

### 1. Introduction

Anaerobic digestion (AD), whose final products are methane and carbon dioxide, is a common process in natural anoxic environments such as water sediments, wetlands, or marshlands. The environments have to be rich in organic matter and poor with other electron acceptors such as nitrate, compounds containing oxidized forms of metals, and sulfate. AD is also common in landfills and wastewater treatment plants and was used by man to produce biogas from waste biomass as an alternative energy source.

AD is a complex process that requires the metabolic interaction of many groups of microorganisms responsible for four closely related major steps. The first one is hydrolysis of complex organic polymers (e.g., polysaccharides, lipids, proteins) to

monomers (sugars, fatty acids, amino acids). The second step is acidogenesis that results in formation of hydrogen and carbon dioxide as well as nongaseous fermentation products, that is, low-molecular-weight organic acids and alcohols. These products are further oxidized to hydrogen, carbon dioxide, and acetate in acetogenic step that involves mainly syntrophic degradation of nongaseous fermentation products. The fourth step is methanogenesis. Three groups of substrates for methane production and three types of methanogenic pathways are known: splitting of acetate (aceticlastic/acetotrophic methanogenesis); reduction of CO2 with H2 or formate and rarely ethanol or secondary alcohols as electron donors (hydrogenotrophic methanogenesis); and reduction of methyl groups of methylated compounds such as methanol, methylated amines, or methylated sulfides (hydrogen-dependent and hydrogen-independent methylotrophic methanogenesis). The two last steps, acetogenesis and methanogenesis, are closely related and involve syntrophic associations between hydrogen-producing acetogenic bacteria and hydrogenotrophic methanogens (Figure 1) [1–5].

biogas production and increase the share of renewable energy in total energy

Searching for Metabolic Pathways of Anaerobic Digestion: A Useful List of the Key Enzymes

Analysis of many studies on metagenomes of microbial communities from anaerobic digesters shows that (i) contribution of methanogens in the methaneyielding microbial communities is relatively small, below 20%; (ii) the most abundant phyla of bacteria are usually Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria; (iii) methanogenic archaea are dominated by acetotrophs or hydrogenotrophs with a certain contribution of methylotrophs; (iv) substrate, operational conditions such as temperature, pH, ammonia concentration, etc. shape the structure, percentage distribution of specific taxons, and functioning of the community of microorganisms; (v) it is important to describe interactions within microbial communities and assign functions in AD steps to specific groups of microbes; and (vi) the majority of sequences are not classified at the genus level confirming that most of the microorganisms are still unrecognized

In this contribution, the purpose of the study was to prepare a list of the selected enzymes and their catalyzed reactions, being a specific enzymatic road map of AD metabolic pathways, useful in molecular studies. The available metabolic pathway databases such as KEGG PATHWAY Database [16–18], MetaCyc Metabolic Pathway Database, BioCyc Database Collection [19], and BRENDA—The Comprehensive Enzyme Information System [20] were used to

Figure 1 shows a scheme of AD and Tables 1–4 present a summary of the selected enzymes and enzymatic reactions involved in decomposition of organic matter to methane and carbon dioxide. Tables 1–4 are an extension of Figure 1, and

The key groups of hydrolases involved in the process of degradation of organic matter are esterases, glycosidases, and peptidases, which catalyze the cleavage of ester bonds, glycoside bonds, and peptide bonds, respectively (Table 1). Table 1 also includes other classes of hydrolases such as acting on carbon-nitrogen bonds,

In the acidogenic stage of AD, the key step is pyruvate formation from carbohydrates (Table 2, Part A) or other compounds and further pyruvate transformations toward short-chain fatty acids and ethanol (Table 2, Part B). The Part C of the Table 2 also considers transformation of gaseous and nongaseous products of acidic fermentations, resulting from nonsyntrophic nutritional interaction between bacteria. The Parts D and E present the enzymes of glycerol and amino acid transformations, respectively. The latter requires syntrophic cooperation between

The enzymes catalyzing oxidation of nongaseous products of acidogenesis mainly butyrate, propionate, acetate, lactate, ethanol including the enzymes of reverse electron transfer (process responsible for energy conservation in

The enzymes of the three recognized pathways of methanogenesis such as acetotrophic, hydrogenotrophic, and methylotrophic are listed in Table 4. The data were prepared on the basis of detailed analysis of AD research. The enzyme nomenclature comes from the Kyoto Encyclopedia of Genes and Genomes

syntrophically growing acetogens) are shown in Table 3.

select metabolic pathways dedicated only to AD from hydrolysis to

methanogenic steps exerted by microbes.

2. Selected enzymes of anaerobic digestion

in Figure 1, there are the links to Tables 1–4.

other than peptide bonds.

(KEGG) database resource.

microorganisms.

51

consumption [6–9].

DOI: http://dx.doi.org/10.5772/intechopen.81256

[6, 10–15].

Recently, there has been a rapid development in culture-independent techniques (meta-omics approaches such as metagenomics, metatranscriptomics, metaproteomics, metabolomics) for exploring microbial communities, which have led to a new insight into their structure and function in both natural environments and anaerobic digesters. The current trends involve the combined use of meta-omic approaches and detailed reactor performance data as well as isotope labeling techniques that allow us to develop a fundamental understanding of the processes occurring in AD. Those activities are aimed to improve

### Figure 1.

A scheme of anaerobic digestion of organic matter. Enzymes catalysing specific reactions of AD are presented in Tables 1–4. Thus in Figure 1 there are the links to Tables 1–4. Furthermore, background colours in the Figure correspond to the background colours of the title rows in the Tables 1–4: hydrolysis is indicated in green, acidogenesis in orange, acetogenesis in blue and methanogenesis in yellow. A, B, C, D, E refer to the title rows in Table 2; F, G refer to the title rows in Table 3.

Searching for Metabolic Pathways of Anaerobic Digestion: A Useful List of the Key Enzymes DOI: http://dx.doi.org/10.5772/intechopen.81256

biogas production and increase the share of renewable energy in total energy consumption [6–9].

Analysis of many studies on metagenomes of microbial communities from anaerobic digesters shows that (i) contribution of methanogens in the methaneyielding microbial communities is relatively small, below 20%; (ii) the most abundant phyla of bacteria are usually Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria; (iii) methanogenic archaea are dominated by acetotrophs or hydrogenotrophs with a certain contribution of methylotrophs; (iv) substrate, operational conditions such as temperature, pH, ammonia concentration, etc. shape the structure, percentage distribution of specific taxons, and functioning of the community of microorganisms; (v) it is important to describe interactions within microbial communities and assign functions in AD steps to specific groups of microbes; and (vi) the majority of sequences are not classified at the genus level confirming that most of the microorganisms are still unrecognized [6, 10–15].

In this contribution, the purpose of the study was to prepare a list of the selected enzymes and their catalyzed reactions, being a specific enzymatic road map of AD metabolic pathways, useful in molecular studies. The available metabolic pathway databases such as KEGG PATHWAY Database [16–18], MetaCyc Metabolic Pathway Database, BioCyc Database Collection [19], and BRENDA—The Comprehensive Enzyme Information System [20] were used to select metabolic pathways dedicated only to AD from hydrolysis to methanogenic steps exerted by microbes.

### 2. Selected enzymes of anaerobic digestion

Figure 1 shows a scheme of AD and Tables 1–4 present a summary of the selected enzymes and enzymatic reactions involved in decomposition of organic matter to methane and carbon dioxide. Tables 1–4 are an extension of Figure 1, and in Figure 1, there are the links to Tables 1–4.

The key groups of hydrolases involved in the process of degradation of organic matter are esterases, glycosidases, and peptidases, which catalyze the cleavage of ester bonds, glycoside bonds, and peptide bonds, respectively (Table 1). Table 1 also includes other classes of hydrolases such as acting on carbon-nitrogen bonds, other than peptide bonds.

In the acidogenic stage of AD, the key step is pyruvate formation from carbohydrates (Table 2, Part A) or other compounds and further pyruvate transformations toward short-chain fatty acids and ethanol (Table 2, Part B). The Part C of the Table 2 also considers transformation of gaseous and nongaseous products of acidic fermentations, resulting from nonsyntrophic nutritional interaction between bacteria. The Parts D and E present the enzymes of glycerol and amino acid transformations, respectively. The latter requires syntrophic cooperation between microorganisms.

The enzymes catalyzing oxidation of nongaseous products of acidogenesis mainly butyrate, propionate, acetate, lactate, ethanol including the enzymes of reverse electron transfer (process responsible for energy conservation in syntrophically growing acetogens) are shown in Table 3.

The enzymes of the three recognized pathways of methanogenesis such as acetotrophic, hydrogenotrophic, and methylotrophic are listed in Table 4.

The data were prepared on the basis of detailed analysis of AD research. The enzyme nomenclature comes from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database resource.

monomers (sugars, fatty acids, amino acids). The second step is acidogenesis that results in formation of hydrogen and carbon dioxide as well as nongaseous fermentation products, that is, low-molecular-weight organic acids and alcohols. These products are further oxidized to hydrogen, carbon dioxide, and acetate in acetogenic step that involves mainly syntrophic degradation of nongaseous fermentation products. The fourth step is methanogenesis. Three groups of substrates for methane production and three types of methanogenic pathways are known: splitting of acetate (aceticlastic/acetotrophic methanogenesis); reduction of CO2 with H2 or formate and rarely ethanol or secondary alcohols as electron donors (hydrogenotrophic methanogenesis); and reduction of methyl groups of methylated compounds such as methanol, methylated amines, or methylated sulfides

methanogenesis). The two last steps, acetogenesis and methanogenesis, are closely

Recently, there has been a rapid development in culture-independent techniques (meta-omics approaches such as metagenomics, metatranscriptomics, metaproteomics, metabolomics) for exploring microbial communities, which have led to a new insight into their structure and function in both natural environments and anaerobic digesters. The current trends involve the combined use of meta-omic approaches and detailed reactor performance data as well as isotope labeling techniques that allow us to develop a fundamental understanding of the processes occurring in AD. Those activities are aimed to improve

A scheme of anaerobic digestion of organic matter. Enzymes catalysing specific reactions of AD are presented in Tables 1–4. Thus in Figure 1 there are the links to Tables 1–4. Furthermore, background colours in the Figure correspond to the background colours of the title rows in the Tables 1–4: hydrolysis is indicated in green, acidogenesis in orange, acetogenesis in blue and methanogenesis in yellow. A, B, C, D, E refer to the title rows in

(hydrogen-dependent and hydrogen-independent methylotrophic

Anaerobic Digestion

Figure 1.

50

Table 2; F, G refer to the title rows in Table 3.

related and involve syntrophic associations between hydrogen-producing acetogenic bacteria and hydrogenotrophic methanogens (Figure 1) [1–5].


Enzyme Reaction/process EC number A. Pyruvate formation from carbohydrates [23] Glycolysis (the Embden-Meyerhof-Parnas pathway) Hexose kinase D-Glucose + ATP \$ D-glucose-6-phosphate + ADP EC 2.7.1.1 Phosphoglucose isomerase D-Glucose 6-phosphate \$ D-fructose 6-phosphate EC 5.3.1.9

Searching for Metabolic Pathways of Anaerobic Digestion: A Useful List of the Key Enzymes

fructose 1,6-bisphosphate

phosphoglycerate + ATP

Phosphoglycerate mutase 3-Phosphoglycerate \$ 2-phosphoglycerate EC 5.4.2.1

Pyruvate kinase Phosphoenolpyruvate + ADP \$ pyruvate + ATP EC 2.7.1.40 2-Keto-3-deoxy-6-phosphogluconate (the Entner-Doudoroff pathway)

B. Further transformations of pyruvate—glycolytic fermentations [23–27] Lactate dehydrogenase Pyruvate + NADH \$ lactate + NAD<sup>+</sup> EC 1.1.1.27

Phosphotransacetylase CoA + acetyl phosphate \$ acetyl-CoA + phosphate EC 2.3.1.8 Acetate kinase ATP + acetate \$ ADP + acetyl phosphate EC 2.7.2.1

CoA + NADH + H<sup>+</sup>

Acetyl-CoA acetyltransferase 2-acetyl-CoA \$ CoA + acetoacetyl-CoA EC 2.3.1.9

3-Acetoacetyl-CoA + NADPH + H<sup>+</sup> \$ 3-hydroxybutanoyl-CoA + NADP<sup>+</sup>

glucono-1,5-lactone + NADPH + H<sup>+</sup>

2-dehydro-D-gluconate + NAD(P)H + H<sup>+</sup>

Pyruvate + CoA + oxidized Fd \$ acetyl-CoA + reduced Fd + CO2 + H<sup>+</sup>

Acetaldehyde + NADH + H<sup>+</sup> \$ ethanol + NAD<sup>+</sup> An aldehyde + NADH + H+ \$ a primary alcohol +

Oxidized Fd + NADH \$ reduced Fd + NAD<sup>+</sup> + H<sup>+</sup> EC 1.18.1.3

3-Hydroxybutanoyl-CoA \$ crotonoyl-CoA + H2O EC 4.2.1.55

2-Dehydro-3-deoxy-6-phospho-D-gluconate \$ pyruvate + D-glyceraldehyde 3-phosphate

phosphate + glyceraldehyde-3-phosphate

D-Glyceraldehyde 3-phosphate + phosphate + NAD<sup>+</sup> \$ 1,3-bisphosphoglycerate + NADH + H+

D-glucose 6-phosphate + NADP+ \$ 6-phospho-D-

EC 2.7.1.11

EC 4.1.2.13

EC 5.3.1.1

EC 1.2.1.12

EC 2.7.2.3

EC 4.2.1.11

EC 1.1.1.49

EC 1.1.1.43

EC 4.1.2.14

EC 1.2.7.1

EC 1.12.7.2

EC 1.1.1.1

EC 1.2.1.10

EC 1.1.1.157

Phosphofructose kinase ATP + D-fructose 6-phosphate \$ ADP + D-

Fructose-bisphosphate aldolase Fructose-1,6-bisphosphate \$ dihydroxyacetone

Triose phosphate isomerase Glyceraldehyde 3-phosphate \$ dihydroxyacetone phosphate

Enolase 2-Phospho-D-glycerate \$ phosphoenolpyruvate + H2O

Phosphogluconate dehydrogenase 6-Phospho-D-gluconate + NAD(P)<sup>+</sup> \$ 6-phospho-

Ferredoxin hydrogenase 2 reduced ferredoxin + 2 H<sup>+</sup> \$ H2 + 2 oxidized ferredoxin

NAD<sup>+</sup>

Acetaldehyde dehydrogenase Acetaldehyde + CoA + NAD<sup>+</sup> \$ acetyl-

Phosphoglycerate kinase 1,3-Bisphosphoglycerate + ADP \$ 3-

Glyceraldehyde-3-phosphate

DOI: http://dx.doi.org/10.5772/intechopen.81256

dehydrogenase

Glucose-6-phosphate dehydrogenase

2-Keto-3-deoxy-6 phosphogluconate aldolase

Pyruvate:ferredoxin oxidoreductase, PFOR

NFOR

NAD<sup>+</sup>

dehydrogenase

Crotonase

53

3-Hydroxybutyryl-CoA dehydrogenase

3-OH-butyryl-CoA dehydratase

NADH:ferredoxin oxidoreductase,


### Table 1.

The selected enzymes of hydrolytic step of anaerobic digestion [21, 22].

Searching for Metabolic Pathways of Anaerobic Digestion: A Useful List of the Key Enzymes DOI: http://dx.doi.org/10.5772/intechopen.81256


Hydrolytic enzyme Reaction/process EC number Esterases Acting on ester bonds EC 3.1 Glycosidases Acting on glycoside bonds EC 3.2 Acting on cellulose

Beta-glucosidase Hydrolysis of terminal, nonreducing beta-D-glucosyl

Endo-1,4-beta-xylanase Endohydrolysis of (1 ! 4)-beta-D-xylosidic linkages in

Xylan 1,4-beta-xylosidase Hydrolysis of (1 ! 4)-beta-D-xylans, to remove successive

Beta-mannosidase Hydrolysis of terminal, nonreducing beta-D-mannose

Alpha-galactosidase Hydrolysis of terminal, nonreducing alpha-D-galactose

Alpha-glucuronidase An alpha-D-glucuronoside + H2O ! an alcohol + D-

Hydrolases acting on carbon-nitrogen bonds, other than peptide

The selected enzymes of hydrolytic step of anaerobic digestion [21, 22].

Hydrolases acting on phosphorus-nitrogen

Hydrolases acting on sulfur-nitrogen

Hydrolases acting on sulfur-sulfur

Hydrolases acting on carbon-phosphorus

Peptidases Acting on peptide bonds EC 3.4 Other hydrolases

Hydrolases acting on ether bonds EC 3.3 Hydrolases acting on carbon-carbon bonds EC 3.7 Hydrolases acting on halide bonds EC 3.8

Hydrolases acting on carbon-sulfur bonds EC 3.13 Hydrolases acting on acid anhydrides EC 3.6

Endohydrolysis of (1 ! 4)-beta-D-glucosidic linkages in cellulose, lichenin, and cereal beta-D-glucans

Hydrolysis of (1 ! 4)-beta-D-glucosidic linkages in cellulose and cellotetraose, releasing cellobiose from the nonreducing ends of the chains

residues with release of beta-D-glucose

xylans

D-xylose residues from the nonreducing termini

Random hydrolysis of (1 ! 4)-beta-D-mannosidic linkages in mannans, galactomannans, and glucomannans

residues in beta-D-mannosides

residues in alpha-D-galactosides, including galactose oligosaccharides, galactomannans, and galactolipids

glucuronate

Acting on hemicellulose

EC 3.2.1.4

EC 3.2.1.91

EC 3.2.1.21

EC 3.2.1.8

EC 3.2.1.37

EC 3.2.1.78

EC 3.2.1.25

EC 3.2.1.22

EC 3.2.1.139

EC 3.5

EC 3.9

EC 3.10

EC 3.11

EC 3.12

Cellulase; endo-1,4-beta-D-

glucanase

Anaerobic Digestion

end)

Cellulose 1,4-betacellobiosidase (nonreducing

Mannan endo-1,4-beta-

mannosidase

bonds

bonds

bonds

bonds

bonds

Table 1.

52


Enzyme Reaction/process EC number

Searching for Metabolic Pathways of Anaerobic Digestion: A Useful List of the Key Enzymes

Reductive carbon monoxide dehydrogenase/acetyl-CoA synthase pathway (reductive CODH/ACS) [30]

CO2 + NADPH \$ formate + NADP<sup>+</sup> EC 1.17.1.10

ADP + phosphate + 10-formyltetrahydrofolate

5,10-Methenyltetrahydrofolate + NADPH + H<sup>+</sup> \$ 5,10-Methylenetetrahydrofolate + NADP<sup>+</sup>

5,10-Methylenetetrahydrofolate + 2 reduced Fd + 2 <sup>H</sup><sup>+</sup> \$ 5-methyltetrahydrofolate + 2 oxidized Fd

5,10-Methylenetetrahydrofolate + NAD(P)H + H<sup>+</sup> \$ 5-methyltetrahydrofolate + NAD(P)<sup>+</sup>

10-Formyltetrahydrofolate \$ 5,10 methenyltetrahydrofolate + H2O

[Co(I) corrinoid Fe-S protein] + 5 methyltetrahydrofolate \$ [methyl-Co(III) corrinoid Fe-S protein] + tetrahydrofolate

D. Glycerol transformations [31, 32] Oxidative pathway

Dihydroxyacetone kinase ATP + glycerone \$ ADP + glycerone phosphate EC 2.7.1.29 For further reactions, see Part A: Pyruvate formation Reductive pathway

E. Amino acids fermentations [33–37] Syntrophy with H2-scavenging microorganism: amino acid degradation involves NAD(P)- or FADdependent deamination of amino acids to the corresponding α-keto acids by amino acid

conversion of α-keto acids via oxidative decarboxylation to fatty acids: RCOCOO� + H2O ! RCOO� + CO2 + H2 [33] Without syntrophy with H2-scavenging microorganism: Stickland Reaction—coupled oxidationreduction reactions between suitable amino acids (coupled deamination of amino acids); one member of the pair is oxidized (dehydrogenated) and the other is reduced (hydrogenated) [34], for example,

)COO� + H2O ! RCOCOO� + NH4

(dihydroxyacetone) + NADH + H+

CO + CoA + [methyl-Co(III) corrinoid Fe-S protein] \$ acetyl-CoA + [Co(I) corrinoid Fe-S EC 2.8.3.8

EC 6.3.4.3

EC 3.5.4.9

EC 1.5.1.5

EC 1.5.7.1

EC 1.5.1.20

EC 2.1.1.258

EC 1.2.7.4

EC 2.3.1.169

EC 1.1.1.6

EC 4.2.1.30

EC 1.1.1.202

<sup>+</sup> + H2 and further

� + H<sup>+</sup>

� + H<sup>+</sup>

� + H<sup>+</sup>

<sup>+</sup> + HCO3

<sup>+</sup> + HCO3

<sup>+</sup> + HCO3

Acetate CoA-transferase Acyl-CoA + acetate \$ a fatty acid anion + acetyl-CoA

Formyltetrahydrofolate synthetase ATP + formate + tetrahydrofolate \$

Carbon monoxide dehydrogenase CO2 + 2 reduced Fd + 2 H<sup>+</sup> \$ CO + H2O+2 oxidized Fd

protein]

Glycerol dehydrogenase Glycerol + NAD<sup>+</sup> \$ glycerone

dehydrogenases (EC 1.4.1.X): RCH(NH4

Glycerol dehydratase Glycerol \$ 3-hydroxypropionaldehyde + H2O

1,3-Propanediol dehydrogenase 3-Hydroxypropionaldehyde + NADH + H<sup>+</sup> \$ 1,3 propanediol + NAD<sup>+</sup>

+

Alanine and glycine: alanine + 2 glycine + 3H2O ! 3 acetate� + 3NH4

Valine and glycine: valine + 2 glycine + 3H2O ! isobutyrate� + 2 acetate� + 3NH4

Leucine and glycine: leucine + 2 glycine + 3H2O ! isovalerate� + 2 acetate� + 3NH4

NADP-dependent formate

DOI: http://dx.doi.org/10.5772/intechopen.81256

Methenyltetrahydrofolate

dehydrogenase

cyclohydrolase

dehydrogenase

reductase

reductase

synthase

55

NADP-dependent methylenetetrahydrofolate

Ferredoxin-dependent methylenetetrahydrofolate

5,10-Methylenetetrahydrofolate

5-Methyltetrahydrofolate: corrinoid/iron–sulfur protein Co-

CO-methylating acetyl-CoA

methyltransferase

Pyruvate is oxidized to acetyl coenzyme A, which is further routed to acetate and butyrate with hydrogen release. See Part B: Further transformations of pyruvate—glycolytic fermentations


Searching for Metabolic Pathways of Anaerobic Digestion: A Useful List of the Key Enzymes DOI: http://dx.doi.org/10.5772/intechopen.81256


Syntrophy with H2-scavenging microorganism: amino acid degradation involves NAD(P)- or FADdependent deamination of amino acids to the corresponding α-keto acids by amino acid dehydrogenases (EC 1.4.1.X): RCH(NH4 + )COO� + H2O ! RCOCOO� + NH4 <sup>+</sup> + H2 and further conversion of α-keto acids via oxidative decarboxylation to fatty acids: RCOCOO� + H2O ! RCOO� + CO2 + H2 [33]

Without syntrophy with H2-scavenging microorganism: Stickland Reaction—coupled oxidationreduction reactions between suitable amino acids (coupled deamination of amino acids); one member of the pair is oxidized (dehydrogenated) and the other is reduced (hydrogenated) [34], for example,

Alanine and glycine: alanine + 2 glycine + 3H2O ! 3 acetate� + 3NH4 <sup>+</sup> + HCO3 � + H<sup>+</sup> Valine and glycine: valine + 2 glycine + 3H2O ! isobutyrate� + 2 acetate� + 3NH4 <sup>+</sup> + HCO3 � + H<sup>+</sup> Leucine and glycine: leucine + 2 glycine + 3H2O ! isovalerate� + 2 acetate� + 3NH4 <sup>+</sup> + HCO3 � + H<sup>+</sup>

Enzyme Reaction/process EC number 2NADH+ oxidized Fd + crotonyl-CoA ! 2 NAD+ reduced Fd + butyryl-CoA catalyzed by butyryl CoA dehydrogenase/electron-transfer flavoprotein complex

flavoprotein \$ a short-chain trans-

2,3-dehydroacyl-CoA + reduced electron-transfer

� \$ ADP + phosphate +

Fumarate + NADH \$ succinate + NAD<sup>+</sup> EC 1.3.1.6

(R)-lactate + NAD<sup>+</sup> \$ pyruvate + NADH + H+ EC 1.1.1.28

Lactate + 2 NAD<sup>+</sup> + 2 reduced Fd \$ pyruvate + 2

Butanoyl-CoA + 2 NAD<sup>+</sup> + 2 reduced Fd \$ Crotonoyl-CoA + 2 NADH + 2 oxidized Fd

EC 1.3.8.1

EC 1.3.1.109

EC 2.3.1.19

EC 6.4.1.1

EC 6.2.1.4

EC 5.1.99.1

EC 2.8.3.1

EC 1.3.1.110

EC 2.7.2.1

EC 1.1.1.157

EC 1.3.1.109

Butyryl-CoA dehydrogenase A short-chain acyl-CoA + electron-transfer

Butyryl-CoA dehydrogenase/Etf

3-Hydroxybutyryl-CoA dehydrogenase

complex

54

Butyryl-CoA dehydrogenase/Etf

complex

Anaerobic Digestion

flavoprotein

Phosphotransbutyrylase Butanoyl-CoA + phosphate \$ CoA + butanoyl phosphate

oxaloacetate

Succinyl-CoA synthetase GTP + succinate + CoA = GDP + phosphate + succinyl-CoA

Methylmalonyl CoA epimerase (R)-methylmalonyl-CoA \$ (S)-methylmalonyl-CoA

Propionate-CoA transferase Acetate + propanoyl-CoA \$ acetyl-

Pyruvate carboxylase ATP + pyruvate + HCO3

Butyrate kinase Butanoyl phosphate + ADP \$ butanoate + ATP EC 2.7.2.7 PFL—pyruvate formate lyase Pyruvate + CoA \$ acetyl-CoA + formate EC 2.3.1.54 FHL—formate hydrogen lyase Formate ! H2 + CO2 EC 1.17.99.7

Malate dehydrogenase Malate + NAD<sup>+</sup> \$ oxaloacetate + NADH + H+ EC 1.1.1.37 Fumarate hydratase Malate \$ fumarate + H2O EC 4.2.1.2 Fumarate reductase Fumarate + a quinol \$ succinate + a quinone EC 1.3.5.4

Methylmalonyl CoA mutase Succinyl-CoA \$ (R)-methylmalonyl-CoA EC 5.4.99.2

Methylmalonyl-CoA decarboxylase (S)-methylmalonyl-CoA \$ propanoyl-CoA + CO2 EC 4.1.1.41

C. Transformation of gaseous and nongaseous products of acidic fermentations (the selected examples) Transformation of lactate and acetate to butyrate, hydrogen, and carbon dioxide ([28] and cited therein) Lactate dehydrogenases (S)-lactate + NAD<sup>+</sup> \$ pyruvate + NADH + H+ EC 1.1.1.27

NADH + 2 oxidized Fd

Pyruvate is oxidized to acetyl coenzyme A, which is further routed to acetate and butyrate with hydrogen release. See Part B: Further transformations of pyruvate—glycolytic fermentations Transformation of ethanol and acetate to butyrate and hydrogen in Clostridium kluyveri [29]

glycolytic fermentations

Acetyl-CoA acetyltransferase EC 2.3.1.9

3-Hydroxyacyl-CoA dehydratase EC 4.2.1.55

See Table 3

Acetate kinase See Part B. Further transformations of pyruvate—

CoA + propanoate


Enzyme Reaction/process EC number

Searching for Metabolic Pathways of Anaerobic Digestion: A Useful List of the Key Enzymes

The selected enzymes of acidogenic step of anaerobic digestion. A, B, C, D, and E refer to the processes indicated

Enzyme Reaction/process EC number F. Acetogenesis dependent on syntrophic relations between microorganisms Acetate oxidation by, for example, Clostridium ultunense—oxidative carbon monoxide dehydrogenase/acetyl-CoA synthase pathway (oxidative CODH/ACS):

= �31.0 kJ/mol [38]

Reverse electron transfer during acetate oxidation has yet to be confirmed. Direct interspecies electron transfer (DIET) is not excluded (Westerholm et al., 2016) Acetate oxidation by Geobacter sulfurreducens: Acetate oxidation coupled to reduction of fumarate to succinate (ΔG°<sup>0</sup> = �249 kJ per mol acetate), acetate metabolism proceeds via reactions of the citric acid cycle [39]

Citric acid cycle

Aconitase Citrate \$ isocitrate (overall reaction) EC 4.2.1.3

Citrate synthase Acetyl-CoA + H2O + oxaloacetate \$ citrate

2-Oxoglutarate:ferredoxin oxidoreductase 2-Oxoglutarate + CoA + 2 oxidized

Succinyl-CoA:acetate CoA-transferase Succinyl-CoA + acetate \$ acetyl-


NADP-dependent formate dehydrogenase See Table 2, Part C Formyltetrahydrofolate synthetase

> Acetate kinase See Table 2, Part B Phosphotransacetylase

> > + CoA

Isocitrate + NADP<sup>+</sup> \$ 2-oxoglutarate + CO2 + NADPH + H+

Fd = succinyl-CoA + CO2 + 2 reduced Fd + 2 H+

CoA + succinate

glutaconyl-1-CoA

CoA + H2O

Glutaconyl-CoA decarboxylase 4-Carboxybut-2-enoyl-CoA \$ but-2-enoyl-CoA + CO2

� + 4H2 + H+

, ΔG0'

Methenyltetrahydrofolate cyclohydrolase NADP-dependent methylenetetrahydrofolate dehydrogenase Ferredoxin-dependent methylenetetrahydrofolate reductase 5,10-Methylenetetrahydrofolate reductase 5-Methyltetrahydrofolate:corrinoid/iron-sulfur protein Co-methyltransferase Carbon monoxide dehydrogenase CO-methylating acetyl-CoA synthase

ΔG0'

oxoglutarate + reduced acceptor

Acetyl-CoA + (E)-glutaconate \$ acetate +

(R)-2-hydroxyglutaryl-CoA \$ (E)-glutaconyl-

= + 104.6 kJ/mol, with the H2 consuming methanogen,

1.1.99.2

2.8.3.12

4.1.1.70

EC 2.3.3.1

EC1.1.1.42

EC 1.2.7.3

EC 2.8.3.18

EC 4.2.1.167

2-Hydroxyglutarate dehydrogenase (S)-2-hydroxyglutarate + acceptor \$ 2-

Glutaconate (2-hydroxyglutarate)

DOI: http://dx.doi.org/10.5772/intechopen.81256

CoA-transferase

dehydratase

Table 2.

in Figure 1.

2-Hydroxyglutaryl-CoA

Acetate� + 4H2O ! 2 HCO3

Isocitrate dehydrogenase (NADP<sup>+</sup>

dependent)

57

Searching for Metabolic Pathways of Anaerobic Digestion: A Useful List of the Key Enzymes DOI: http://dx.doi.org/10.5772/intechopen.81256


### Table 2.

Enzyme Reaction/process EC number Examples of amino acid dehydrogenases catalyzing deamination of amino acids to the corresponding α-keto acids [33]

EC 1.4.1.21

EC 1.4.1.8

EC 1.4.1.1

EC 1.4.1.9

EC 1.21.4.2

EC 1.21.4.1

EC 4.3.1.17

EC 4.3.1.19

EC 4.3.1.2

4.1.3.22

1.4.1.2

NH3 + NAD(P)H + H+

NH3 + NADH + H+

Glycine reductase Glycine + phosphate + reduced thioredoxin + H+ \$

D-proline reductase (dithiol) D-proline + dihydrolipoate \$5-aminopentanoate

Serine dehydratase L-serine \$ pyruvate + NH3 (overall reaction)

(spontaneous)

(spontaneous)

(spontaneous)

NH3 (spontaneous)

Detailed pathways of glutamate fermentation via 3-methylaspartate [37]

methylfumarate) + NH3

(S)-2-methylmalate = 2-hydroxy-2-

Threonine dehydratase L-threonine \$ 2-oxobutanoate + NH3 (overall reaction)

Methyl aspartase L-threo-3-methylaspartate \$ mesaconate (2-

Citramalate lyase (2S)-2-hydroxy-2-methylbutanedioate \$ acetate + pyruvate

Glutamate dehydrogenase L-glutamate + H2O + NAD+ \$ 2-oxoglutarate + NH3 + NADH + H+

methylbutanedioate

For further transformations of pyruvate to acetate and butyrate, see Part B. For further transformations of pyruvate to propionate, see Part B. Detailed pathway of glutamate fermentation via 2-hydroxyglutarate [37]

Glutamate mutase (methylaspartate mutase)

Anaerobic Digestion

dehydratase)

56

Mesaconase (2-methylmalate

oxobutanoate + NH3 + NADPH + H<sup>+</sup>

oxopentanoate + NH3 + NADH + H+

acetyl phosphate + NH3 + oxidized thioredoxin +

(1a) L-serine \$ 2-aminoprop-2-enoate + H2O (1b) 2-Aminoprop-2-enoate \$ 2-iminopropanoate

(1c) 2-Iminopropanoate + H2O \$ pyruvate + NH3

(1a) L-threonine \$ 2-aminobut-2-enoate + H2O; (1b) 2-Aminobut-2-enoate \$ 2-iminobutanoate

(1c) 2-Iminobutanoate + H2O \$ 2-oxobutanoate +

L-glutamate \$ L-threo-3-methylaspartate EC 5.4.99.1

2-Methylfumarate + H2O \$ (S)-2-methylmalate 4.2.1.34

Key enzymes of Stickland reaction [34–36] Glycine reductase GR pathway (grd operon)

Acetate kinase Acetyl phosphate + ADP \$ acetate + ATP EC 2.7.2.1 Proline reductase PR pathway (prd operon)

(5-aminovalerate) + lipoate

Others examples [33]

Aspartate dehydrogenase L-aspartate + H2O + NAD(P)<sup>+</sup> \$ oxaloacetate +

Valine dehydrogenase L-valine + H2O + NADP<sup>+</sup> \$ 3-methyl-2-

Alanine dehydrogenase L-alanine + H2O + NAD<sup>+</sup> \$ pyruvate +

Leucine dehydrogenase L-leucine + H2O + NAD+ \$ 4-methyl-2-

H2O

The selected enzymes of acidogenic step of anaerobic digestion. A, B, C, D, and E refer to the processes indicated in Figure 1.



Enzyme Reaction/process EC number

Searching for Metabolic Pathways of Anaerobic Digestion: A Useful List of the Key Enzymes

Cytoplasmic FDH IPR027467, IPR006655, IPR006478, IPR019575,

Fe-Fe hydrogenase IPR004108, IPR009016, IPR003149, IPR013352

Extracytoplasmic FDH IPR006443 Formate transporter IPR000292, IPR024002

NiFe hydrogenase IPR001501, IPR018194

DUF224 protein complex IPR003816, IPR004017, IPR023234

ΔG0'

Pyruvate:ferredoxin oxidoreductase Phosphate acetyltransferase Acetate kinase Alcohol dehydrogenase

Lactate oxidation by Desulfovibrio vulgaris:

Lactate oxidation coupled with a reverse electron transfer that involves the membrane-bound Qmo complex, cytochromes, hydrogenases (Coo, Hyn, Hyd, Hys), formate dehydrogenases, menaquinone, membrane-bound Qrc complex [43, 44]

= �74.2 kJ/mol [43]

IPR006452

IPR019079

IPR018365

IPR020539

InterPro number

IPR001949

IPR007202, IPR010207, IPR026902, IPR010208, IPR004338, IPR011303, IPR007329

IPR001750, IPR001516, IPR001694, IPR006137, IPR001268, IPR012179, IPR001135

IPR014731, IPR012255, IPR006089, IPR009075, IPR006092, IPR006091, IPR013786, IPR009100

> IPR023155, IPR024673 IPR020942, IPR002322 IPR016174, IPR000516 IPR001199

= �8.8 kJ/mol with the H2 consuming methanogen,

Part B

Lactate dehydrogenase See Table 2,

FDH accessory protein—tightly connected

DOI: http://dx.doi.org/10.5772/intechopen.81256

CapA—a membrane-bound complex, a protein involved in capsule or biofilm formation that may facilitate syntrophic growth (also present in acetate-oxidizers)

FtsW, RodA, SpoVE—membraneintegrated proteins involved in membrane integration, cell division, sporulation, and

Ribonuclease P involved in tRNA

transfer identified by [42]

Functional domains involved in electron

Rnf complex: 2 reduced Fd + NAD<sup>+</sup> + H<sup>+</sup> + Na+ \$ 2 oxidized Fd + NADH + Na+ (EC

Ech complex: 2 reduced Fd + NADP<sup>+</sup> + H+ \$ 2 oxidized Fd + NADPH (EC 1.18.1.2)

Lactate� + H2O ! acetate� + CO2 +4H2, <sup>Δ</sup>G0'

Etf alpha, Etf beta, Bcd (Butyryl-CoA dehydrogenase): see Table 2, Part B (EC

shape determination

maturation

1.18.1.8)

1.3.1.109)

c cIII b561 b5

59

Cytochromes:

with FDH

Searching for Metabolic Pathways of Anaerobic Digestion: A Useful List of the Key Enzymes DOI: http://dx.doi.org/10.5772/intechopen.81256


Enzyme Reaction/process EC number

Fumarate hydratase (S)-malate \$ fumarate + H2O EC 4.2.1.2

Butyrate oxidation by Syntrophomonas wolfei:

Butyrate oxidation coupled with a reverse electron transfer that involves electron transfer flavoprotein EtfAB, membrane-anchored electron carrier DUF224 protein, the menaquinone pool in the membrane, a membrane-bound cytochrome, NADH:hydrogenase/formate-dehydrogenase complex (NDH/HYD1/FDH-1 complex), Rnf (proton-translocating ferredoxin:NAD<sup>+</sup> oxidoreductase) [40] Propionate oxidation by Syntrophobacter wolinii:

� + H<sup>+</sup> + 3H2, ΔG0'

methanogen, ΔG0'

Fumarate hydratase Fumarate reductase Succinate dehydrogenase Succinate + a quinone \$ fumarate + a

> Methylmalonyl CoA epimerase Methylmalonyl-CoA decarboxylase Propionate-CoA transferase

Propionate oxidation coupled with a reverse electron transfer that involves menaquinone, proteins encoded by cytochrome c homologous genes, cytochrome b:quinone oxidoreductases, formate dehydrogenases, hydrogenases including confurcating [FeFe]-hydrogenases [41]

quinol

NADH + H<sup>+</sup>

CoA + acetate

Butyryl-CoA dehydrogenase See Table 2,

= �17.3 kJ/mol [4]

Part B Crotonase-3-OH-butyryl-CoA dehydratase

= �22.4 kJ/mol [4]

Pyruvate carboxylase See Table 2, Part B Malate dehydrogenase

quinol

Succinyl-CoA synthetase See Table 2, Part B Methylmalonyl CoA mutase

InterPro number

IPR006443

IPR024064

= + 48.3 kJ/mol, with the H2 consuming

= + 76.0 kJ/mol, with the H2 consuming

EC 1.3.5.1

EC 1.1.1.37

EC 2.8.3.9

EC 1.3.5.1

Succinate dehydrogenase succinate + a quinone \$ fumarate + a

Malate dehydrogenase (S)-malate + NAD<sup>+</sup> \$ oxaloacetate +

methanogen, ΔG0'

CoA transferase Butyrate + acetyl-CoA \$ butyryl-

3-Acetyl-CoA acetyltransferase Hydroxybutyryl-CoA dehydrogenase Phosphotransacetylase Acetate kinase

Butyrate� + 2H2O ! 2 acetate� + 2H<sup>+</sup> + 2H2, <sup>Δ</sup>G0'

Anaerobic Digestion

Propionate� + 3H2O ! acetate� + HCO3

Six syntrophy-specific functional domains found in the genomes of the butyrate- or propionate-oxidizing

Extra-cytoplasmic formate dehydrogenase (FDH) alpha subunit, EC 1.17.1.9

FdhE-like protein—tightly connected with

syntrophs [42]

FDH

58


Enzyme Reaction/process EC number MFR—methanofuran, H-S-CoM—coenzyme M, H-S-CoB—coenzyme B, H4MPT tetrahydromethanopterin, F420—5'deazaflavin, H4SPT—tetrahydrosarcinapterin Hydrogenotrophic pathway

Searching for Metabolic Pathways of Anaerobic Digestion: A Useful List of the Key Enzymes

MFR + H2O + 2 oxidized Fd

Formyl-MFR + H4MPT \$ MFR + formyl-H4MPT

H4MPT + H2O

Methenyl-H4MPT + reduced F420 \$ methylene-H4MPT + oxidized F420

Methenyl-H4MPT + H2 \$ methylene-H4MPT + H+

Methylene-H4MPT + reduced F420 \$ CH3- H4MPT + oxidized F420

Coenzyme M + methyl-H4MPT + 2 Na+/in \$ 2-methyl-coenzyme M + 2 Na+/out + H4MPT

CH4

\$ CoB + CoM + methanophenazine

protein] \$ CO + CoA + [methyl-Co(III) corrinoid Fe-S protein]

[Methyl-Co(III) corrinoid Fe-S protein] + tetrahydrosarcinapterin \$ a [Co (I) corrinoid Fe-S protein] + 5 methyltetrahydrosarcinapterin

CO + H2O + 2 oxidized Fd \$ CO2 + 2 reduced Fd + 2 H+

> CH3 H4SPT + H-S-CoM \$ CH3-S-CoM + H4SPT

> > CH4

\$ CoB + CoM + methanophenazine

Methanol + Co(I) corrinoid protein \$ Methyl-Co(III) corrinoid protein + H2O

Coenzyme M + Methyl-Co(III) corrinoid protein \$ 2-(methylthio)ethanesulfonate + Co(I) corrinoid protein

Acetate + CoA \$ acetyl-CoA + H2O EC 2.7.2.1

EC 1.2.7.12

EC 2.3.1.101

EC 3.5.4.27

EC 1.5.98.1

EC 1.12.98.2

EC 1.5.98.2

EC 2.1.1.86

EC 2.8.4.1

EC 1.8.98.1

EC 2.3.1.8 EC 6.2.1.1

EC 2.3.1.169

EC 2.1.1.245

EC 1.2.7.4

EC 2.1.1.-

EC 2.8.4.1

EC 1.8.98.1

EC 2.1.1.90

EC 2.1.1.246

Formylmethanofuran dehydrogenase CO2 + MFR + 2 reduced Fd + 2H<sup>+</sup> \$ formyl-

Methenyl-H4MPT cyclohydrolase Formyl-H4MPT + H<sup>+</sup> \$ methenyl-

Methyl-CoM reductase CH3-S-CoM + H-S-CoB \$ CoM-S-S-CoB +

Heterodisulfide reductase CoM-S-S-CoB + dihydromethanophenazine

CO-methylating acetyl-CoA synthase Acetyl-CoA + a [Co(I) corrinoid Fe-S

Methyl-CoM reductase CH3-S-CoM + H-S-CoB \$ CoM-S-S-CoB +

Heterodisulfide reductase CoM-S-S-CoB + dihydromethanophenazine

Methylotrophic pathway

Acetotrophic pathway

Formylmethanofuran-H4MPT

DOI: http://dx.doi.org/10.5772/intechopen.81256

F420-dependent methylene-H4MPT

F420-dependent methylene-H4MPT

Methyl-H4MPT:coenzyme M methyl-

Acetate kinase-phosphotransacetylase system in Methanosarcina; acetate thiokinase in Methanosaeta

5-Methyltetrahydrosarcinapterin: corrinoid/iron-sulfur protein Co-

Anaerobic carbon monoxide

Methyl H4SPT: coenzyme M methyltransferase

Methanol:corrinoid protein Co-

[Methyl-Co(III) corrinoid protein]: coenzyme M methyltransferase

methyltransferase

61

methyltransferase

dehydrogenase

H2-forming methylene-H4MPT

formyltransferase

dehydrogenase

dehydrogenase

reductase

transferase

### Table 3.

The selected enzymes of acetogenic step of anaerobic digestion. F and G refer to the processes indicated in Figure 1.

Searching for Metabolic Pathways of Anaerobic Digestion: A Useful List of the Key Enzymes DOI: http://dx.doi.org/10.5772/intechopen.81256


Enzyme Reaction/process EC number Ethanol oxidation by Pelobacter carbinolicus

= � 56 kJ/mol [4]

ΔG0'

Acetaldehyde dehydrogenase (acetylating)

Acetate kinase

Reduction of ferredoxin by NADH by reverse electron flow in a reaction catalyzed by Rnf complex

Lactate dehydrogenase Lactate + 2 NAD<sup>+</sup> + 2 reduced Fd \$

Reduction of ferredoxin by NADH by reverse electron flow in a reaction catalyzed by Rnf complex

Ethanol oxidation coupled with a reverse electron transfer that involves membrane-bound ion-translocating ferredoxin:NAD<sup>+</sup> oxidoreductase, formate dehydrogenases, and confurcating hydrogenases [1, 45] G. Acetogenesis independent on syntrophic relations between microorganisms Ethanol oxidation by Acetobacterium woodii: 2 ethanol + 2 CO2 ! 3 acetate—75.4 kJ/mol [46]

= + 9.6 kJ/mol with the H2 consuming methanogen,

Part B

EC 1.2.1.3

Part B

[EC:1.2.1.10 1.1.1.1]

Part B

See Part F

Part C

EC 1.3.1.110

Part B

See Part F

Part C


An aldehyde + NAD<sup>+</sup> + H2O \$ <sup>a</sup> carboxylate + NADH + H+

Phosphotransacetylase See Table 2,

Ethanol + NAD+ ! acetaldehyde + NADH + H<sup>+</sup> acetaldehyde + NAD<sup>+</sup> + CoA ! acetyl-CoA + 2 NADH + H+ Ethanol is oxidized to acetyl-CoA in a twostep reaction by a bifunctional acetylating ethanol/aldehyde dehydrogenase

pyruvate + 2 NADH + 2 oxidized Fd The enzyme uses flavin-based electron confurcation to drive endergonic lactate oxidation with NAD+ as oxidant at the expense of simultaneous exergonic electron flow from reduced ferredoxin to NAD<sup>+</sup>

Acetyl-CoA is transformed to acetate with the release of ATP See Table 2,

Carbon dioxide is reduced to acetate via the Wood-Ljungdahl pathway See Table 2,

Lactate oxidation by Acetobacterium woodii: 2 lactate ! 3 acetate—61 kJ/mol [47]

Pyruvate is transformed to acetyl-CoA and further to acetate with the release of ATP See Table 2,

The selected enzymes of acetogenic step of anaerobic digestion. F and G refer to the processes indicated in

Carbon dioxide is reduced to acetate via the Wood-Ljungdahl pathway See Table 2,

Ethanol + H2O ! acetate� + H<sup>+</sup> + 2H2, <sup>Δ</sup>G0'

NAD+

Nonacetylating acetaldehyde

Bifunctional acetaldehyde-CoA/alcohol

dehydrogenase

Anaerobic Digestion

dehydrogenase

Table 3.

Figure 1.

60


Table 4.

The selected enzymes of methanogenic step of anaerobic digestion [48, 49].

### 3. Conclusion

Biomass conversion to methane and carbon dioxide is the effect of complex interactions between microorganisms. These processes occur due to the microbial enzymatic machinery involved in specific metabolic pathways. Meta-omic analyses of microbial communities involved in AD reveal (i) dependence of microbial communities on the type of feedstock and operational conditions and (ii) describe interactions within microbial communities and ecophysiological functions of the specific taxa. Searching for the gene presence, gene expression, and protein expression, as well as linking structure and function of microbial communities, allows to develop a fundamental understanding of AD. This chapter is believed to contribute to the studies on the enzymatic road map of anaerobic digestion. However, it is only the tip of the iceberg of processes occurring in the microbial cells/microbial communities.

Author details

and Mieczysław Błaszczyk<sup>2</sup>

\*, Anna Detman<sup>1</sup>

\*Address all correspondence to: annaw@ibb.waw.pl

provided the original work is properly cited.

, Damian Mielecki<sup>1</sup>

Searching for Metabolic Pathways of Anaerobic Digestion: A Useful List of the Key Enzymes

DOI: http://dx.doi.org/10.5772/intechopen.81256

1 Institute of Biochemistry and Biophysics—Polish Academy of Sciences, Warsaw,

2 Faculty of Agriculture and Biology, Warsaw University of Life Sciences, Warsaw,

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

, Aleksandra Chojnacka<sup>1</sup>

Anna Sikora<sup>1</sup>

Poland

Poland

63

### Acknowledgements

We acknowledge the support of The National Science Centre, Poland, through grant UMO-2015/17/B/NZ9/01718 and The National Centre for Research and Development, Poland, through grant BIOSTRATEG2/297310/13/NCBiR/2016.

### Conflict of interest

The authors declare that there are no conflicts of interest.

Searching for Metabolic Pathways of Anaerobic Digestion: A Useful List of the Key Enzymes DOI: http://dx.doi.org/10.5772/intechopen.81256

### Author details

3. Conclusion

Table 4.

Methylamine:corrinoid protein Co-

Dimethylamine:corrinoid protein Co-

Trimethylamine:corrinoid protein Co-

[Methyl-Co(III) methylamine-specific corrinoid protein]:coenzyme M

methyltransferase

Anaerobic Digestion

methyltransferase

methyltransferase

methyltransferase

communities.

Acknowledgements

Conflict of interest

62

Biomass conversion to methane and carbon dioxide is the effect of complex interactions between microorganisms. These processes occur due to the microbial enzymatic machinery involved in specific metabolic pathways. Meta-omic analyses of microbial communities involved in AD reveal (i) dependence of microbial communities on the type of feedstock and operational conditions and (ii) describe interactions within microbial communities and ecophysiological functions of the specific taxa. Searching for the gene presence, gene expression, and protein expression, as well as linking structure and function of microbial communities, allows to develop a fundamental understanding of AD. This chapter is believed to contribute to the studies on the enzymatic road map of anaerobic digestion. However, it is only

Methyl-CoM reductase CH3-S-CoM + H-S-CoB \$ CoM-S-S-CoB +

Heterodisulfide reductase CoM-S-S-CoB + dihydromethanophenazine

The selected enzymes of methanogenic step of anaerobic digestion [48, 49].

Enzyme Reaction/process EC number

Methylamine + [Co(I) methylamine-specific corrinoid protein] \$ a [methyl-Co(III) methylamine-specific corrinoid protein] + NH3

Dimethylamine + [Co(I) dimethylaminespecific corrinoid protein] \$ a [methyl-Co(III) dimethylamine-specific corrinoid protein] + methylamine

Trimethylamine + a [Co(I) trimethylaminespecific corrinoid protein] \$ a [methyl-Co(III) trimethylamine-specific corrinoid protein] + dimethylamine

[Methyl-Co(III) methylamine-specific corrinoid protein] + CoM \$ methyl-CoM + a [Co(I) methylamine-specific corrinoid protein]

CH4

\$ CoB + CoM + methanophenazine

EC 2.1.1.248

EC 2.1.1.249

EC 2.1.1.249

EC 2.1.1.247

EC 2.8.4.1

EC 1.8.98.1

the tip of the iceberg of processes occurring in the microbial cells/microbial

We acknowledge the support of The National Science Centre, Poland, through

grant UMO-2015/17/B/NZ9/01718 and The National Centre for Research and Development, Poland, through grant BIOSTRATEG2/297310/13/NCBiR/2016.

The authors declare that there are no conflicts of interest.

Anna Sikora<sup>1</sup> \*, Anna Detman<sup>1</sup> , Damian Mielecki<sup>1</sup> , Aleksandra Chojnacka<sup>1</sup> and Mieczysław Błaszczyk<sup>2</sup>

1 Institute of Biochemistry and Biophysics—Polish Academy of Sciences, Warsaw, Poland

2 Faculty of Agriculture and Biology, Warsaw University of Life Sciences, Warsaw, Poland

\*Address all correspondence to: annaw@ibb.waw.pl

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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10.1128/JB.01492-12

Jurkowski M, Zielenkiewicz U. Lactic acid bacteria in hydrogen producing

coincidence. In: Kongo M, editor. Lactic Acid Bacteria—R & D for Food, Health and Livestock Purposes. Rijeka, InTech; 2013. pp. 487-514. DOI: 5772/50364

[29] Li F, Hinderberger J, Seedorf H, Zhang J, Buckel W, Thauer RK. Coupled ferredoxin and crotonyl coenzyme A (CoA) reduction with NADH catalyzed by the butyryl-CoA dehydrogenase/Etf complex from Clostridium klyuveri. Journal of Bacteriology. 2008;190: 843-850. DOI: 10.1128/JB.01417-07

[30] Diekert G, Wohlfarth G.

DOI: 10.1007/BF00871640

2012;1:81-92. DOI: 10.1080/ 09593330.2012.692723

cofs.2015.03.001

[32] Ganzle MG. Lactic metabolism revisited: Metabolism of lactic acid bacteria in food fermentations and food spoilage. Current Opinion in Food Science. 2015;2:106-117. DOI: 10.1016/j.

[33] Schink B, Stams AJM. Syntrophism among prokaryotes. In: Dworkin M, editor. The Prokaryotes. 3rd ed. New York: Springer; 2006. pp. 309-335. DOI:

[34] Nisman B. The Stickland reaction. Bacteriology Reviews. 1954;18:16-42

Sonensheina AL. Proline-dependent

10.1007/978-3-642-30123-0\_59

[35] Bouillaut L, Self WT,

66

Metabolism of homoacetogens. Antonie Van Leeuwenhoek. 1994;66:209-221.

[31] Viana MB, Freitas AV, Leitão RC, Pinto GAS, Santaella ST. Anaerobic digestion of crude glycerol: A review. Environmental Technology Reviews.

[43] Walker CB, He Z, Yang ZK, Ringbauer JA, He Q, et al. The electron transfer system of syntrophically grown Desulfovibrio vulgaris. Journal of Bacteriology. 2009;191:5793-5801. DOI: 10.1128/JB.00356-09

[44] Meyer B, Kuehl J, Deutschbauer AM, Price MN, Arkin AP, et al. Variation among Desulfovibrio species in electron transfer systems used for syntrophic growth. Journal of Bacteriology. 2013;195(5):990-1004. DOI: 10.1128/JB.01959-12

[45] Schmidt A, Frensch M, Schleheck D, Schink B, Muller N. Degradation of acetaldehyde and its precursors by Pelobacter carbinolicus and P. acetylenicus. PLoS One. 2014;9(9, 12): e115902. DOI: 10.1371/journal. pone.011590

[46] Bertsch J, Siemund AL, Kremp F, Muller V. A novel route for ethanol oxidation in the acetogenic bacterium Acetobacterium woodii: The acetaldehyde/ethanol dehydrogenase pathway. Environmental Microbiology. 2016;18:2913-2922. DOI: 10.1111/ 1462-2920.13082

[47] Weghoff MC, Bertsch J, Muller V. A novel mode of lactate metabolism in strictly anaerobic bacteria. Environmental Microbiology. 2015;17: 670-6777. DOI: 10.1111/1462-2920.12493

[48] Thauer RK. Biochemistry of methanogenesis: A tribute to Marjory Stephenson. Microbiology. 1998;144: 2377-2406. DOI: 10.1099/00221287-144- 9-2377

[49] Thauer RK, Kaster AK, Seedorf H, Buckel W, Hedderich R. Methanogenic Archaea: Ecologically relevant differences in energy conservation.

Nature Reviews Microbiology. 2008;6: 579-591. DOI: 10.1038/nrmicro1931

Chapter 4

Process

Abstract

1. Introduction

69

diversity of raw materials, and influential factors.

Review of Mathematical Models

for the Anaerobic Digestion

Borja Velázquez-Martí, Orlando W. Meneses-Quelal,

To describe anaerobic fermentation, many mathematical models have been suggested. A commonly accepted hypothesis in microbial growth is the speed of cellular reproduction, which is proportional to the concentration of cells at that instant. The constant of proportionality between the speed of growth and cell concentration is called cell growth rate, μ. In many occasions, the cell growth rate is considered constant. This leads to conclude that the concentration of cells versus time presents an exponential function. The consideration of this equation provides a good adjustment in the beginning of central phase of the anaerobic fermentation process. However, it moves away from the measurements when there is a limited reproduction due to lack of nutrients and competition between the cells in the environment. This produces a sigmoidal variation in concentration. To find a suitable fit function for all phases of the process, Gompertz proposes a model that considers the cell growth rate as variable. In this chapter, the Gompertz model, kinetic models, transference, and cone models are evaluated. Different adaptations to fit the variables to the obtained values in the experiments have been reviewed.

Keywords: mathematical model, Gompertz, fermentation, kinetic model, methane

Anaerobic digestion is a biological process in which the organic matter in the absence of oxygen, and through the action of a group of specific bacteria, is broken down into a set of gaseous products, called biogas, formed by CH4, CO2, H2, H2S, etc. and in a digestate, which is a mixture of mineral substances (N, P, K, Ca, etc.) and compounds of difficult degradation [1]. One of the objectives of anaerobic digestion is the production of methane, which can be used as fuel. Anaerobic digestion is considered one of the most important and advantageous processes in the treatment of livestock manure and sludge residues. It represents a possibility to reduce its environmental impact while at the same time, providing a biofuel for local energy needs [2]. This process has been known for hundreds of years; however, it is still the object of research due to the great variability of the conditions in which it can be produced,

Table 1 shows some of the most recent researches. In recent years, there has

been an increasing interest in new raw fermentation materials, mainly

Juan Gaibor-Chavez and Zulay Niño-Ruiz

### Chapter 4

## Review of Mathematical Models for the Anaerobic Digestion Process

Borja Velázquez-Martí, Orlando W. Meneses-Quelal, Juan Gaibor-Chavez and Zulay Niño-Ruiz

### Abstract

To describe anaerobic fermentation, many mathematical models have been suggested. A commonly accepted hypothesis in microbial growth is the speed of cellular reproduction, which is proportional to the concentration of cells at that instant. The constant of proportionality between the speed of growth and cell concentration is called cell growth rate, μ. In many occasions, the cell growth rate is considered constant. This leads to conclude that the concentration of cells versus time presents an exponential function. The consideration of this equation provides a good adjustment in the beginning of central phase of the anaerobic fermentation process. However, it moves away from the measurements when there is a limited reproduction due to lack of nutrients and competition between the cells in the environment. This produces a sigmoidal variation in concentration. To find a suitable fit function for all phases of the process, Gompertz proposes a model that considers the cell growth rate as variable. In this chapter, the Gompertz model, kinetic models, transference, and cone models are evaluated. Different adaptations to fit the variables to the obtained values in the experiments have been reviewed.

Keywords: mathematical model, Gompertz, fermentation, kinetic model, methane

### 1. Introduction

Anaerobic digestion is a biological process in which the organic matter in the absence of oxygen, and through the action of a group of specific bacteria, is broken down into a set of gaseous products, called biogas, formed by CH4, CO2, H2, H2S, etc. and in a digestate, which is a mixture of mineral substances (N, P, K, Ca, etc.) and compounds of difficult degradation [1]. One of the objectives of anaerobic digestion is the production of methane, which can be used as fuel. Anaerobic digestion is considered one of the most important and advantageous processes in the treatment of livestock manure and sludge residues. It represents a possibility to reduce its environmental impact while at the same time, providing a biofuel for local energy needs [2]. This process has been known for hundreds of years; however, it is still the object of research due to the great variability of the conditions in which it can be produced, diversity of raw materials, and influential factors.

Table 1 shows some of the most recent researches. In recent years, there has been an increasing interest in new raw fermentation materials, mainly


lignocellulosic materials from agriculture, or waste such as paper and cardboard. So, co-digestion processes are being analyzed, which consist of improving methane production by mixing materials that ferment better together than separated due to

New inocula, such as the rumen, and its interaction with the raw material are also being examined, together with nutritional requirements. Pretreatment studies are being carried out along with thermal sequences in the processes, alternating thermophilic and mesophilic stages and evaluating the productivity, kinetics, and net energy balance. The microbiological identification involved in the fermentation according to the substrate and the followed thermal process also acquire interest. One of the most discussed aspects is mathematical modeling. The objective of the modeling is to be able to establish characteristic parameters of the raw material and process conditions to predict the system's evolution over time, the performance obtained, and fermentation speed. In this study the most important models are

Anaerobic digestion comprises a decomposition mechanism of organic matter based on three stages [3]: first a hydrolytic phase, in which polymers of long carbon chains are broken obtaining shorter acid chains, subsequently, an acetogenic phase, in which the short-chain acids obtained in the previous phase are transformed into

Each of these stages is provided by a differentiated microbiological group. Each group takes as a substrate to the product generated in the previous phase. When the evolution of a microbial group is analyzed in a batch-type reactor, in batches, the

Initially, the concentration of microorganisms responsible of digestion is small and evolves very slowly in this stage because it needs time to adapt. This phase is called lag phase, or lethargy. Subsequently, there is a very rapid increase in cell concentration called the growth phase. The growth phase ends when cell compete for substrate, causing a number of cell replications to equal deaths, so the number of living cells is stabilized. This phase is called the stationary phase. The stationary

acetic acid, and finally, a methanogenic phase, in which the acetic acid is

variation of cell concentration varies, as shown in Figure 1.

the enriched microbial load; in this way, their nutritional needs are better

Review of Mathematical Models for the Anaerobic Digestion Process

DOI: http://dx.doi.org/10.5772/intechopen.80815

complemented.

evaluated.

Figure 1.

71

Variation of cell concentration over time in a batch reactor.

transformed into methane.

\*Inoculum is material obtained from the effluent of a previous biogas plant that ferments raw materials, such as manure from pigs, cows, sheep, chickens, and other animals, at mesophilic ranges.

### Table 1.

Values obtained from methane potential in various co-digestion processes.

### Review of Mathematical Models for the Anaerobic Digestion Process DOI: http://dx.doi.org/10.5772/intechopen.80815

Author Material Pretreatment Methane

Bayrakdar et al. [4] Chicken manure 0.272 Franco et al. [5] Wheat straw + inoculum 0.229

Guo et al. [6] Excessively withered corn straw + glucose 0.282 Li et al. [7] Parton + sheep manure 0.152 Li et al. [7] Paper + sheep manure 0.199

Microalgae + pig manure Alkaline

Wheat straw + sewage 0.314

Cassava pulp with pig manure 0.380

Food waste + livestock manure 0.467

Vinasse and chicken manure (chicken dung) 0.650

inoculum\*

Mancini et al. [8] Lignocellulose in general N-methylmorpholine

Mustafa et al. [10] Bagasse of sugarcane + inoculum\* Hydrothermal

Xu et al. [12] Corn straw + Bacillus Subtilis Microaerobic

or straw husk) with yogurt serum

manure

Sheep litter (mixture of rice husk with feces and urine) + cattle manure

vegetable waste

Fu et al. [22] Corn straw + inoculum \* Thermophilic

Fu et al. [23] Corn straw + inoculum \* Secondary

Dennehy et al. [15] Food waste and pig manure 0.521

Aboudi et al. [18] Dry beet granules of sugar beet + cow dung 0.280 Belle et al. [19] Fodder radish with cow dung 0.200

\*Inoculum is material obtained from the effluent of a previous biogas plant that ferments raw materials, such as

Zahan et al. [13] Gallinaza (sawdust, wood shavings, and rice

Aboudi et al. [14] Dry sediment of sugar beet tails + pig

Di Maria et al. [21] Sludge from wastewater with fruit and

manure from pigs, cows, sheep, chickens, and other animals, at mesophilic ranges.

Values obtained from methane potential in various co-digestion processes.

Franco et al. [5] Wheat straw + glucose + ac. Formic +

Martín Juárez et al.

Anaerobic Digestion

Vazifehkhoran et al. [11]

Glanpracha and Annachhatre [16]

Marin Batista et al.

Cestonaro et al.

Agyeman and Tao

[17]

[20]

[24]

Table 1.

70

[9]

potential m3 kg<sup>1</sup> SV

0.276

0.304

0.377

0.318

0.270

0.670

0.260

0.171

0.216

0.326

0.381

N-oxide

pretreatment with NAOH

pretreatment

mesolithic

microaerobic

thermophilic microaerobic lignocellulosic materials from agriculture, or waste such as paper and cardboard. So, co-digestion processes are being analyzed, which consist of improving methane production by mixing materials that ferment better together than separated due to the enriched microbial load; in this way, their nutritional needs are better complemented.

New inocula, such as the rumen, and its interaction with the raw material are also being examined, together with nutritional requirements. Pretreatment studies are being carried out along with thermal sequences in the processes, alternating thermophilic and mesophilic stages and evaluating the productivity, kinetics, and net energy balance. The microbiological identification involved in the fermentation according to the substrate and the followed thermal process also acquire interest.

One of the most discussed aspects is mathematical modeling. The objective of the modeling is to be able to establish characteristic parameters of the raw material and process conditions to predict the system's evolution over time, the performance obtained, and fermentation speed. In this study the most important models are evaluated.

Anaerobic digestion comprises a decomposition mechanism of organic matter based on three stages [3]: first a hydrolytic phase, in which polymers of long carbon chains are broken obtaining shorter acid chains, subsequently, an acetogenic phase, in which the short-chain acids obtained in the previous phase are transformed into acetic acid, and finally, a methanogenic phase, in which the acetic acid is transformed into methane.

Each of these stages is provided by a differentiated microbiological group. Each group takes as a substrate to the product generated in the previous phase. When the evolution of a microbial group is analyzed in a batch-type reactor, in batches, the variation of cell concentration varies, as shown in Figure 1.

Initially, the concentration of microorganisms responsible of digestion is small and evolves very slowly in this stage because it needs time to adapt. This phase is called lag phase, or lethargy. Subsequently, there is a very rapid increase in cell concentration called the growth phase. The growth phase ends when cell compete for substrate, causing a number of cell replications to equal deaths, so the number of living cells is stabilized. This phase is called the stationary phase. The stationary

Figure 1. Variation of cell concentration over time in a batch reactor.

phase ends when this battle for substrate causes a higher number of deaths than the number of reproductions, resulting in cell concentration to fall sharply. This phase is called the cell death phase.

From the practical point of view, it is only interesting to analyze the period between the beginnings of the fermentation to the stationary phase, appearing a curve similar to the sigmoid one. However, the sigmoid equation does not correctly fit the experimental results obtained.

### 2. Exponential model

A model widely used to describe the variation of cell concentration in the growth phase has been the exponential model. This model is based on the hypothesis that the speed of growth in an instant is proportional to the concentration of cells existing at that moment. This is expressed mathematically by Eq. (1), where X is the concentration of cells and μ is the constant of proportionality called cell growth rate:

$$\frac{dX}{dt} = \mu \cdot X \tag{1}$$

defined conditions. The maximum growth rate is the one that occurs initially in the growth phase exponentially. When the substrate begins to be scarce, the rate

Along with the Monod model, there are others with the same style that can be observed in Table 2. In all of them, it can be seen that the maximum rate value considered in the exponential phase is minorized when the substrate concentration

The relationship between the variations of cell concentration is always proportional to substrate consumption. The proportionality constant is called the biomass/substrate yield Yx/s and is defined by Eq. (4), where S0 and S1 are the initial and final substrate concentrations and X0 and X1 are the initial and final cell

> Yx=<sup>s</sup> <sup>¼</sup> <sup>X</sup><sup>1</sup> � <sup>X</sup><sup>0</sup> S<sup>0</sup> � S<sup>1</sup>

value allows calculating the time retention (TR) in a bioreactor batch.

Ks þ S<sup>1</sup>

<sup>¼</sup> <sup>μ</sup>max � TR � tlag ! TR <sup>¼</sup> tlag <sup>þ</sup>

The amount of product generated per unit volume and time (P) and methane in this case (M) are proportional to the variation of cell concentration (X). The pro-

> Yp=<sup>x</sup> <sup>¼</sup> <sup>P</sup><sup>1</sup> � <sup>P</sup><sup>0</sup> X<sup>1</sup> � X<sup>0</sup>

Type of model Author Model Kinetic models without inhibition Tessier <sup>μ</sup> <sup>¼</sup> <sup>μ</sup>max � <sup>1</sup> � <sup>e</sup>�S=Ks

Kinetic models with inhibition Andrews and Noak <sup>μ</sup> <sup>¼</sup> <sup>μ</sup>max <sup>1</sup>

Webb

<sup>z</sup> � <sup>μ</sup>max <sup>¼</sup> <sup>μ</sup>maxS<sup>1</sup>

Yx=<sup>s</sup> <sup>¼</sup> <sup>X</sup><sup>1</sup> � <sup>X</sup><sup>0</sup> S<sup>0</sup> � S<sup>1</sup>

portionality constant Yp/x is called product/biomass yield:

ln <sup>X</sup><sup>1</sup> Xo

If the initial concentration of substrate (So) is known, the variation of cell mass during the process is obtained from the biomass/substrate ratio of the process Yx/s. Limiting the decrease in the growth rate to a certain percentage of its maximum

ð Þ! 0<z<1 <sup>S</sup><sup>1</sup> <sup>¼</sup> <sup>z</sup>

! X<sup>1</sup> ¼ X<sup>0</sup> þ Yx=<sup>s</sup> � ð Þ S<sup>0</sup> � S<sup>1</sup>

1 � z

1 μmax

Moser <sup>μ</sup> <sup>¼</sup> <sup>μ</sup>max Sn

Contois <sup>μ</sup> <sup>¼</sup> <sup>μ</sup>max <sup>S</sup>

Aiba et al. <sup>μ</sup> <sup>¼</sup> <sup>μ</sup>max <sup>S</sup>

Tseng and Wymann <sup>μ</sup> <sup>¼</sup> <sup>μ</sup>max <sup>S</sup>

Teissier <sup>μ</sup> <sup>¼</sup> <sup>μ</sup>max <sup>e</sup>�S=Ksi � <sup>e</sup>�S=Ks

� Ks

ln <sup>X</sup><sup>1</sup> Xo

Ks�aþSn

BXþS

KsþSþS<sup>2</sup> Kis

<sup>S</sup>� <sup>1</sup>þβ�<sup>S</sup> Kis KsþSþS<sup>2</sup> Kis

Ksþ<sup>S</sup> <sup>e</sup>�S=Ksi

Ksþ<sup>S</sup> � Ksið Þ <sup>s</sup> � sc

μ ¼ μmax

(4)

decreases with respect to the maximum.

DOI: http://dx.doi.org/10.5772/intechopen.80815

Review of Mathematical Models for the Anaerobic Digestion Process

is low.

Table 2.

73

Variation models of the cell growth rate [25].

concentrations:

The development of Eq. (1) shows that, in the growth phase, the variation of cells follows an exponential curve:

$$\frac{dX}{X} = \mu \cdot dt$$

$$\int\_{X\_1}^{X\_2} \frac{dX}{X} = \int\_{t\_{\rm lag}}^{t} \mu \cdot dt$$

$$\ln \frac{X\_2}{X\_1} = \mu \cdot \left(t - t\_{\rm lag}\right)$$

$$X\_2 = X \cdot\_1 e^{\mu \cdot \left(t - t\_{\rm lag}\right)}$$

tlag is the lag time. The cell growth rate has as unit the inverse of time (d�<sup>1</sup> ) and can be calculated experimentally with Eq. (2):

$$\mu = \frac{X\_2 - X\_1}{X\_1 \cdot \left(t - t\_{\text{lag}}\right)} \tag{2}$$

This model is not completely satisfactory because it has been verified that μ is not constant and it varies as time goes by. As competition for the substrate increases, the curve in Figure 1 moves away from the exponential. To achieve a better fit, Monod proposed a model for calculating the cell growth rate as a function of the substrate concentration according to Eq. (3), where S is the substrate concentration at a given time, μmax is the maximum rate of cell growth, and Ks is a constant called saturation:

$$
\mu = \frac{\mu\_{\text{max}} \cdot \text{S}}{K\_{\text{s}} + \text{S}} \tag{3}
$$

The Monod model proposes the existence of a maximum cell growth rate and a saturation constant that are characteristics of microbial species growing under

Review of Mathematical Models for the Anaerobic Digestion Process DOI: http://dx.doi.org/10.5772/intechopen.80815

phase ends when this battle for substrate causes a higher number of deaths than the number of reproductions, resulting in cell concentration to fall sharply. This phase

From the practical point of view, it is only interesting to analyze the period between the beginnings of the fermentation to the stationary phase, appearing a curve similar to the sigmoid one. However, the sigmoid equation does not correctly

A model widely used to describe the variation of cell concentration in the growth phase has been the exponential model. This model is based on the hypothesis that the speed of growth in an instant is proportional to the concentration of cells existing at that moment. This is expressed mathematically by Eq. (1), where X is the concentration of cells and μ is the constant of proportionality called cell

dX

dX

dX X ¼

X<sup>2</sup> ¼ X�1e

tlag is the lag time. The cell growth rate has as unit the inverse of time (d�<sup>1</sup>

<sup>μ</sup> <sup>¼</sup> <sup>X</sup><sup>2</sup> � <sup>X</sup><sup>1</sup> X<sup>1</sup> � t � tlag

not constant and it varies as time goes by. As competition for the substrate increases, the curve in Figure 1 moves away from the exponential. To achieve a better fit, Monod proposed a model for calculating the cell growth rate as a function of the substrate concentration according to Eq. (3), where S is the substrate concentration at a given time, μmax is the maximum rate of cell growth, and Ks is a

This model is not completely satisfactory because it has been verified that μ is

<sup>μ</sup> <sup>¼</sup> <sup>μ</sup>max � <sup>S</sup>

The Monod model proposes the existence of a maximum cell growth rate and a

saturation constant that are characteristics of microbial species growing under

ð<sup>X</sup><sup>2</sup> X1

ln <sup>X</sup><sup>2</sup> X1

The development of Eq. (1) shows that, in the growth phase, the variation of

<sup>X</sup> <sup>¼</sup> <sup>μ</sup> � dt

ðt tlag μ � dt

¼ μ � t � tlag � �

<sup>μ</sup>� <sup>t</sup>�<sup>t</sup> ð Þ lag

dt <sup>¼</sup> <sup>μ</sup> � <sup>X</sup> (1)

� � (2)

Ks <sup>þ</sup> <sup>S</sup> (3)

) and

is called the cell death phase.

Anaerobic Digestion

2. Exponential model

growth rate:

fit the experimental results obtained.

cells follows an exponential curve:

can be calculated experimentally with Eq. (2):

constant called saturation:

72

defined conditions. The maximum growth rate is the one that occurs initially in the growth phase exponentially. When the substrate begins to be scarce, the rate decreases with respect to the maximum.

Along with the Monod model, there are others with the same style that can be observed in Table 2. In all of them, it can be seen that the maximum rate value considered in the exponential phase is minorized when the substrate concentration is low.

The relationship between the variations of cell concentration is always proportional to substrate consumption. The proportionality constant is called the biomass/substrate yield Yx/s and is defined by Eq. (4), where S0 and S1 are the initial and final substrate concentrations and X0 and X1 are the initial and final cell concentrations:

$$Y\_{\mathbf{x}/\mathbf{s}} = \frac{\mathbf{X}\_1 - \mathbf{X}\_0}{\mathbf{S}\_0 - \mathbf{S}\_1} \tag{4}$$

If the initial concentration of substrate (So) is known, the variation of cell mass during the process is obtained from the biomass/substrate ratio of the process Yx/s. Limiting the decrease in the growth rate to a certain percentage of its maximum value allows calculating the time retention (TR) in a bioreactor batch.

$$z \cdot \mu\_{\text{max}} = \frac{\mu\_{\text{max}} S\_1}{K\_s + S\_1} (0 \text{-} z \cdot 1) \to S\_1 = \frac{z}{1 - z} \cdot K\_s$$

$$Y\_{\text{x}/\text{s}} = \frac{X\_1 - X\_0}{S\_0 - S\_1} \to X\_1 = X\_0 + Y\_{\text{x}/\text{s}} \cdot (S\_0 - S\_1)$$

$$\ln \frac{X\_1}{X\_o} = \mu\_{\text{max}} \cdot (TR - t\_{\text{lag}}) \to TR = t\_{\text{lag}} + \frac{1}{\mu\_{\text{max}}} \ln \frac{X\_1}{X\_o}$$

The amount of product generated per unit volume and time (P) and methane in this case (M) are proportional to the variation of cell concentration (X). The proportionality constant Yp/x is called product/biomass yield:

$$Y\_{p/\mathbf{x}} = \frac{P\_1 - P\_0}{X\_1 - X\_0}.$$


### Table 2. Variation models of the cell growth rate [25].

Anaerobic Digestion

$$\frac{dM}{dt} = Y\_{p/\mathbf{x}} \cdot \frac{dX}{dt}$$

lim X!0

Review of Mathematical Models for the Anaerobic Digestion Process

DOI: http://dx.doi.org/10.5772/intechopen.80815

dμ dt <sup>¼</sup> <sup>c</sup>

ðX X0

� ln ln <sup>a</sup>

lim <sup>X</sup>!<sup>∞</sup> <sup>μ</sup> <sup>¼</sup> lim

μ ¼ lim X!0

> X <sup>a</sup> � �<sup>a</sup> X2 � �

must solve Eq. (5), which is a differential equation of separable variables:

dX

dX <sup>X</sup> � ln ð Þ <sup>a</sup>=<sup>X</sup> <sup>¼</sup>

� � � ln ln <sup>a</sup>

ln <sup>a</sup> X<sup>0</sup> ln <sup>a</sup> X

� �

� � � �

X

ln

ln <sup>a</sup> X<sup>0</sup> ect <sup>¼</sup> ln <sup>a</sup>

Since a and X0 are constants, the following consideration can be made:

ln <sup>a</sup> X<sup>0</sup>

> e e�ctþ<sup>b</sup>

c � ln ð Þ¼ a=X ∞

<sup>¼</sup> �<sup>c</sup> X

ðt 0 c � dt

X<sup>0</sup>

¼ ct

X

¼ B ¼ e b

> ¼ a X<sup>0</sup>

Therefore, Eq. (7) is obtained, which describes the cellular concentration in the reactor for each instant. This equation is the true contribution of the Gompertz:

�<sup>B</sup> <sup>¼</sup> <sup>a</sup> � <sup>e</sup>

lim<sup>t</sup>!<sup>∞</sup> <sup>X</sup> <sup>¼</sup> <sup>a</sup>

If we accept the Gompertz model, Zwietering et al. [28] suggest modifications providing physical meaning to these variables. The rate of growth can be redefined

ln <sup>X</sup><sup>0</sup> <sup>a</sup> ¼ X<sup>0</sup>

�ctþ<sup>b</sup> � � � �<sup>c</sup> <sup>¼</sup> <sup>a</sup> � <sup>c</sup> � <sup>e</sup> �e�ctþ<sup>b</sup> ½ � � <sup>e</sup>

�ctþb

When analyzing the limits in zero and infinity, we observe that the initial concentration of cells is X1 and that a represents an asymptote corresponding to the

maximum cell potential, which would occur in the steady state:

X ¼ a � e

limt!0

3.1 Considerations to the Gompertz model

dt <sup>¼</sup> <sup>a</sup> � <sup>e</sup> �e�ctþ<sup>b</sup> ½ � � �<sup>e</sup>

dX

as Eq. (8):

75

¼ ct

<sup>X</sup> <sup>¼</sup> <sup>a</sup> � <sup>e</sup> �e�ctþ<sup>b</sup> ½ � (7)

<sup>X</sup>!<sup>∞</sup> <sup>c</sup> � ln ð Þ¼� <sup>a</sup>=<sup>X</sup> <sup>∞</sup>

To obtain the function of cell concentration in time according to Gompertz, we

<sup>X</sup> � ln ð Þ <sup>a</sup>=<sup>X</sup> <sup>¼</sup> <sup>c</sup> � dt

Since the variation of cell concentration is proportional to the concentration of cells at a given time, we have to.

$$\frac{dM}{dt} = Y\_{p/s} \cdot \mu X$$

By developing the variation of cell concentration over time, it has been demonstrated that the amount of product obtained (methane) follows an exponential growth during the exponential growth of microorganisms. That is the reason because working in this phase with batch-type bioreactors is preferred for optimum performance. To do this, you must adjust the retention time to the duration of this stage.

X<sup>0</sup> represents the initial cell concentration in the reactor; X represents cell concentration at a time t, and tlag is the time of lethargy or cellular adaptation:

$$\frac{dM}{dt} = Y\_{p/s} \cdot \mu X\_0 \cdot \mathbf{e}^{\mu(t - t \text{lag})}$$

$$M = Y\_{p/s} \cdot X\_0 \cdot \left(\mathbf{e}^{\mu(t - t \text{lag})} - 1\right)$$

whereas the value of Yp=<sup>s</sup> � X<sup>0</sup> is negligible compared to the exponential, that is Yp=<sup>s</sup> � <sup>X</sup>0<<<Yp=<sup>s</sup> � <sup>X</sup><sup>0</sup> � <sup>e</sup>μð Þ <sup>t</sup>�tlag , the accumulated volume obtained in each experiment can be graphically represented with the model of Eq. (1), calculating the cell growth rate, the productivity of the substrate, and the optimum retention time for a greater use of energy:

$$\mathcal{M} = Y\_{p/s} \cdot X\_0 \cdot e^{\mu(t - t \text{lag})}$$

### 3. Model of Gompertz

Despite the practicality of the exponential model when complemented by the Monod equation, it is not completely satisfactory because it does not describe well the variation of cell concentration as the substrate is being consumed and the stationary phase approaches. Knowing how cell growth behaves in this area is significantly relevant if you want to use high retention times.

To find an adequate adjustment function for all phases of the process, Winsor [26] proposes to use an equation developed by Gompertz [27] in human demography. This proposes a model that considers the variable cell growth rate, as shown in Eqs. (5) and (6), where a and c are constants:

$$\frac{dX}{dt} = c \cdot \ln\left(a/X\right) \cdot X \tag{5}$$

$$
\mu = \mathfrak{c} \cdot \ln \left( a / X \right) \tag{6}
$$

According to Eq. (6), Gompertz moves radically away from the Monod approach, since the cell growth rate has no maximum. If there was a maximum, the derivative of Eq. (6) would be canceled at some point, something that does not happen:

Review of Mathematical Models for the Anaerobic Digestion Process DOI: http://dx.doi.org/10.5772/intechopen.80815

dM

dM

cells at a given time, we have to.

of this stage.

Anaerobic Digestion

greater use of energy:

3. Model of Gompertz

happen:

74

dt <sup>¼</sup> Yp=<sup>x</sup> �

Since the variation of cell concentration is proportional to the concentration of

dt <sup>¼</sup> Yp=<sup>s</sup> � <sup>μ</sup><sup>X</sup>

By developing the variation of cell concentration over time, it has been demonstrated that the amount of product obtained (methane) follows an exponential growth during the exponential growth of microorganisms. That is the reason because working in this phase with batch-type bioreactors is preferred for optimum performance. To do this, you must adjust the retention time to the duration

X<sup>0</sup> represents the initial cell concentration in the reactor; X represents cell concentration at a time t, and tlag is the time of lethargy or cellular adaptation:

dt <sup>¼</sup> Yp=<sup>s</sup> � <sup>μ</sup>X<sup>0</sup> � <sup>e</sup>

M ¼ Yp=<sup>s</sup> � X<sup>0</sup> � e

whereas the value of Yp=<sup>s</sup> � X<sup>0</sup> is negligible compared to the exponential, that is Yp=<sup>s</sup> � <sup>X</sup>0<<<Yp=<sup>s</sup> � <sup>X</sup><sup>0</sup> � <sup>e</sup>μð Þ <sup>t</sup>�tlag , the accumulated volume obtained in each experiment can be graphically represented with the model of Eq. (1), calculating the cell growth rate, the productivity of the substrate, and the optimum retention time for a

Despite the practicality of the exponential model when complemented by the Monod equation, it is not completely satisfactory because it does not describe well the variation of cell concentration as the substrate is being consumed and the stationary phase approaches. Knowing how cell growth behaves in this area is

To find an adequate adjustment function for all phases of the process, Winsor [26] proposes to use an equation developed by Gompertz [27] in human demography. This proposes a model that considers the variable cell growth rate, as shown in

M ¼ Yp=<sup>s</sup> � X<sup>0</sup> � e

μð Þ t�tlag

<sup>μ</sup>ð Þ <sup>t</sup>�tlag � <sup>1</sup> 

μð Þ t�tlag

dt <sup>¼</sup> <sup>c</sup> � ln ð Þ� <sup>a</sup>=<sup>X</sup> <sup>X</sup> (5) μ ¼ c � ln ð Þ a=X (6)

dM

significantly relevant if you want to use high retention times.

dX

According to Eq. (6), Gompertz moves radically away from the Monod approach, since the cell growth rate has no maximum. If there was a maximum, the derivative of Eq. (6) would be canceled at some point, something that does not

Eqs. (5) and (6), where a and c are constants:

dX dt

$$\lim\_{X \to 0} \mu = \lim\_{X \to 0} c \cdot \ln \left( a/X \right) = \infty$$

$$\lim\_{X \to \infty} \mu = \lim\_{X \to \infty} c \cdot \ln \left( a/X \right) = -\infty$$

$$\frac{d\mu}{dt} = c \frac{X}{a} \cdot \left( \frac{-a}{X^2} \right) = \frac{-c}{X}$$

To obtain the function of cell concentration in time according to Gompertz, we must solve Eq. (5), which is a differential equation of separable variables:

$$\frac{dX}{X \cdot \ln\left(a/X\right)} = c \cdot dt$$

$$\int\_{X0}^{X} \frac{dX}{X \cdot \ln\left(a/X\right)} = \int\_{0}^{t} c \cdot dt$$

$$-\left[\ln\left(\ln\frac{a}{X}\right) - \ln\left(\ln\frac{a}{X\_{0}}\right)\right] = ct$$

$$\ln\left(\frac{\ln\frac{a}{X\_{0}}}{\ln\frac{a}{X}}\right) = ct$$

$$\frac{\ln\frac{a}{X\_{0}}}{e^{ct}} = \ln\frac{a}{X}$$

Since a and X0 are constants, the following consideration can be made:

$$
\ln \frac{a}{X\_0} = B = e^b$$

$$
e^{\epsilon^{-a+b}} = \frac{a}{X\_0}$$

Therefore, Eq. (7) is obtained, which describes the cellular concentration in the reactor for each instant. This equation is the true contribution of the Gompertz:

$$X = \mathfrak{a} \cdot e^{\left[-\mathfrak{e}^{-\mathfrak{e}t + b}\right]} \tag{7}$$

When analyzing the limits in zero and infinity, we observe that the initial concentration of cells is X1 and that a represents an asymptote corresponding to the maximum cell potential, which would occur in the steady state:

$$\lim\_{t \to 0} X = a \cdot e^{-B} = a \cdot e^{\ln \frac{\chi\_0}{a}} = X\_0$$

$$\lim\_{t \to \infty} X = a$$

### 3.1 Considerations to the Gompertz model

If we accept the Gompertz model, Zwietering et al. [28] suggest modifications providing physical meaning to these variables. The rate of growth can be redefined as Eq. (8):

$$\frac{dX}{dt} = a \cdot e^{\left[-\epsilon^{-\epsilon+b}\right]} \cdot \left(-e^{-\epsilon t + b}\right) \cdot -c = a \cdot c \cdot e^{\left[-\epsilon^{-\epsilon+b}\right]} \cdot e^{-\epsilon t + b}$$

Anaerobic Digestion

$$\frac{dX}{dt} = a \cdot c \cdot e^{\left[-\epsilon^{-at+b}\right]} \cdot e^{-ct+b} \tag{8}$$

tlag <sup>¼</sup> ð Þ <sup>b</sup> � <sup>1</sup> c

b ¼ c � tlag þ 1

<sup>e</sup> ,the result

(9)

<sup>a</sup> � tlag <sup>þ</sup> <sup>1</sup>

Obtaining the Gompertz equation is Eq. (9). This equation has become popular-

<sup>v</sup>max�<sup>e</sup> <sup>a</sup> � <sup>t</sup> lag ð Þ �<sup>t</sup> <sup>þ</sup><sup>1</sup> h i

This equation has been used in current research, such as Bah et al. [29], Capson-Tojo et al. [3], Bayrakdar et al. [4], Mancini et al. [8], Martín Juárez et al. [9], and Li

t þ 1 þ

<sup>¼</sup> dM dX

�ctþb

� e �vmax�<sup>e</sup> <sup>a</sup> <sup>t</sup>þvmax�<sup>e</sup>

� e vmax�e

� e vmax�e <sup>a</sup> <sup>t</sup>ð Þ lag�<sup>t</sup> <sup>þ</sup><sup>1</sup>

dt (10)

<sup>a</sup> �tlagþ1

dt

<sup>a</sup> <sup>t</sup>ð Þ lag�<sup>t</sup> <sup>þ</sup><sup>1</sup>

dX

The latency time and the maximum speed of cellular reproduction will be char-

vmax � e a tlag � �

To experimentally obtain the maximum reproduction speed and the latency time, X is measured as well as the reactor time. Next by defining the value of a as the maximum cell concentration obtainable, Eq. (9) then can be linearized:

And <sup>v</sup>max <sup>¼</sup> <sup>a</sup> � <sup>c</sup>

<sup>X</sup> <sup>¼</sup> <sup>a</sup> � <sup>e</sup> �<sup>e</sup>

¼ � <sup>v</sup>max � <sup>e</sup> a

3.2 Cumulative production curve of methane applying Gompertz

Yp=<sup>x</sup> <sup>¼</sup> <sup>P</sup><sup>1</sup> � <sup>P</sup><sup>0</sup> X<sup>1</sup> � X<sup>0</sup>

dt <sup>¼</sup> Yp=<sup>x</sup> � <sup>a</sup> � <sup>c</sup> � <sup>e</sup> �e�ctþ<sup>b</sup> ½ � � <sup>e</sup>

�vmax�<sup>e</sup> <sup>a</sup> <sup>t</sup>þvmax�<sup>e</sup> <sup>a</sup> �<sup>t</sup> lagþ<sup>1</sup> � �

> <sup>v</sup>max�<sup>e</sup> <sup>a</sup> <sup>t</sup> lag ð Þ �<sup>t</sup> <sup>þ</sup><sup>1</sup> h i

From Eq. (10), we obtain the cumulative methane production Eq. (11):

<sup>v</sup>max�<sup>e</sup> <sup>a</sup> <sup>t</sup> ð Þ lag�<sup>t</sup> <sup>þ</sup><sup>1</sup> h i

dM dt <sup>¼</sup> Yp=<sup>x</sup>

<sup>b</sup> <sup>¼</sup> <sup>v</sup>max � <sup>e</sup>

From this equation, b can also be expressed as.

Review of Mathematical Models for the Anaerobic Digestion Process

ized as the modified Gompertz equation:

DOI: http://dx.doi.org/10.5772/intechopen.80815

ln ln <sup>X</sup> a � �

acteristics of the microbial group in certain conditions.

If we consider the product/biomass yield, we have.

dM

dt <sup>¼</sup> Yp=<sup>x</sup> � <sup>a</sup> � <sup>c</sup> � <sup>e</sup> �<sup>e</sup>

dt <sup>¼</sup> Yp=<sup>x</sup> � <sup>a</sup> � <sup>c</sup> � <sup>e</sup> �<sup>e</sup>

Yp=<sup>x</sup> � <sup>a</sup> � <sup>c</sup> � <sup>e</sup> �<sup>e</sup>

dM

dM

M ¼ ðt 0

77

et al. [7].

The instant in which the maximum growth velocity tm occurs would be calculated from the first derivative of the velocity equal to zero, which is the same as the second derivative of the Gompertz Eq. (7). This implies that at that point where the growth speed is at maximum, the Gompertz function has a turning point:

$$\frac{d^2X}{dt^2} = a \cdot c^2 \cdot e^{\left[-\epsilon^{-a+b}\right]} \cdot \left(e^{-ct+b}\right)^2 - a \cdot c^2 \cdot e^{\left[-\epsilon^{-a+b}\right]} \cdot \left(e^{-ct+b}\right)$$

$$\frac{d^2X}{dt^2} = a \cdot c^2 \cdot e^{\left[-\epsilon^{-a+b}\right]} \cdot \left(e^{-ct+b}\right) \cdot \left[\left(e^{-ct+b}\right) - 1\right]$$

$$\frac{d^2X}{dt^2} = a \cdot c^2 \cdot e^{\left[-\epsilon^{-a\_m+b}\right]} \cdot \left(e^{-ct\_m+b}\right) \cdot \left[\left(e^{-ct\_m+b}\right) - 1\right] = 0$$

$$-ct\_m + b = 0$$

$$t\_m = \frac{b}{c}$$

The concentration of cells where the maximum reproduction speed occurs is calculated by entering the value of tm in Eq. (7), and it is shown that the growth rate where the reproduction speed is at maximum equals c:

$$X = a \cdot e^{\left[-\epsilon^{-\epsilon t\_m + b}\right]} = a \cdot e^{\left[-\epsilon^{-\frac{b}{\epsilon} + b}\right]} = \frac{a}{\epsilon}$$

$$\mu\_m = c \cdot \ln\left(a/(a/\epsilon)\right) = c$$

The maximum reproduction speed value is obtained by substituting tm in Eq. (8):

$$\upsilon\_{\max} = \frac{dX\_{tm}}{dt} = a \cdot c \cdot e^{\left[-e^{-ct\_m+b}\right]} \cdot e^{-ct+b} = a \cdot c \cdot e^{\left[-e^{-\frac{b}{c}+b}\right]} \cdot e^{-c\_c^{\mathbf{k}}+b} = \frac{a \cdot c}{e}$$

According to the previous thing, the curve tangent X in the point of inflection tm has the form:

$$X = \frac{a \cdot c}{e}t + k$$

$$\text{Given the } t = t\_m = \frac{b}{c} \text{ y } X\_{tm} = \frac{a}{e}, \text{ so } :$$

$$\frac{a}{e} = \frac{a \cdot c}{e} \cdot \frac{b}{c} + k \to k = \frac{a}{e} - \frac{a \cdot b}{e} = \frac{a}{e}(1 - b),$$

$$X = \frac{a \cdot c}{e}t + \frac{a}{e}(1 - b) = \frac{a}{e} \cdot (ct + (1 - b))$$

If we define the latency time, tlag , as the time in which the tangent line at the curve inflection point (point that coincides with maximum velocity) cuts the axis of the abscissa, we have that the latency time is in X ¼ 0:

$$\mathbf{0} = \mathbf{c}\mathbf{t}\_{\text{lag}} + (\mathbf{1} - b)$$

Review of Mathematical Models for the Anaerobic Digestion Process DOI: http://dx.doi.org/10.5772/intechopen.80815

$$t\_{\text{lag}} = \frac{(b-1)}{c}$$

From this equation, b can also be expressed as.

dX

<sup>2</sup> � <sup>e</sup> �e�ctþ<sup>b</sup> ½ � � <sup>e</sup>

d2 X dt<sup>2</sup> <sup>¼</sup> <sup>a</sup> � <sup>c</sup>

Anaerobic Digestion

d2 X dt<sup>2</sup> <sup>¼</sup> <sup>a</sup> � <sup>c</sup>

Eq. (8):

has the form:

76

<sup>v</sup>max <sup>¼</sup> dXtm

a <sup>e</sup> <sup>¼</sup> <sup>a</sup> � <sup>c</sup> e � b c

> <sup>X</sup> <sup>¼</sup> <sup>a</sup> � <sup>c</sup> e t þ a e

the abscissa, we have that the latency time is in X ¼ 0:

d2 X dt<sup>2</sup> <sup>¼</sup> <sup>a</sup> � <sup>c</sup>

dt <sup>¼</sup> <sup>a</sup> � <sup>c</sup> � <sup>e</sup> �e�ctþ<sup>b</sup> ½ � � <sup>e</sup>

�ctþ<sup>b</sup> <sup>2</sup>

� a � c

�ctþ<sup>b</sup> � <sup>e</sup>

�ctmþ<sup>b</sup> � <sup>e</sup>

�ctm þ b ¼ 0

tm <sup>¼</sup> <sup>b</sup> c

The concentration of cells where the maximum reproduction speed occurs is calculated by entering the value of tm in Eq. (7), and it is shown that the growth rate

μ<sup>m</sup> ¼ c � ln ða=ð Þ¼ a=e c

According to the previous thing, the curve tangent X in the point of inflection tm

t þ k

e

ð Þ¼ <sup>1</sup> � <sup>b</sup> <sup>a</sup>

If we define the latency time, tlag , as the time in which the tangent line at the curve inflection point (point that coincides with maximum velocity) cuts the axis of

0 ¼ ctlag þ ð Þ 1 � b

<sup>c</sup> <sup>y</sup> Xtm <sup>¼</sup> <sup>a</sup>

� <sup>a</sup> � <sup>b</sup> <sup>e</sup> <sup>¼</sup> <sup>a</sup> e ð Þ 1 � b

e

e, so :

� ð Þ ct þ ð Þ 1 � b

The maximum reproduction speed value is obtained by substituting tm in

<sup>X</sup> <sup>¼</sup> <sup>a</sup> � <sup>c</sup> e

<sup>þ</sup> <sup>k</sup> ! <sup>k</sup> <sup>¼</sup> <sup>a</sup>

Given the <sup>t</sup> <sup>¼</sup> tm <sup>¼</sup> <sup>b</sup>

<sup>X</sup> <sup>¼</sup> <sup>a</sup> � <sup>e</sup> �e�ctmþ<sup>b</sup> ½ � <sup>¼</sup> <sup>a</sup> � <sup>e</sup> �e�c<sup>b</sup>

<sup>2</sup> � <sup>e</sup> �e�ctþ<sup>b</sup> ½ � � <sup>e</sup>

�ctþ<sup>b</sup> � <sup>1</sup>

�ctmþ<sup>b</sup> � <sup>1</sup> <sup>¼</sup> <sup>0</sup>

cþb 

�ctþ<sup>b</sup> <sup>¼</sup> <sup>a</sup> � <sup>c</sup> � <sup>e</sup> �e�<sup>c</sup>

¼ a e

b cþb 

� e �cb c

<sup>þ</sup><sup>b</sup> <sup>¼</sup> <sup>a</sup> � <sup>c</sup> e

growth speed is at maximum, the Gompertz function has a turning point:

<sup>2</sup> � <sup>e</sup> �e�ctþ<sup>b</sup> ½ � � <sup>e</sup>

<sup>2</sup> � <sup>e</sup> �e�ctmþ<sup>b</sup> ½ � � <sup>e</sup>

where the reproduction speed is at maximum equals c:

dt <sup>¼</sup> <sup>a</sup> � <sup>c</sup> � <sup>e</sup> �e�ctmþ<sup>b</sup> ½ � � <sup>e</sup>

The instant in which the maximum growth velocity tm occurs would be calculated from the first derivative of the velocity equal to zero, which is the same as the second derivative of the Gompertz Eq. (7). This implies that at that point where the

�ctþ<sup>b</sup> (8)

�ctþb

$$b = c \cdot t\_{\text{lag}} + \mathbf{1}$$

$$\text{And } v\_{\text{max}} = \frac{a \cdot c}{e} \text{, the result}$$

$$b = \frac{v\_{\text{max}} \cdot e}{a} \cdot t\_{\text{lag}} + \mathbf{1}$$

Obtaining the Gompertz equation is Eq. (9). This equation has become popularized as the modified Gompertz equation:

$$X = \mathfrak{a} \cdot \mathfrak{e} \left[ {}^{\frac{\text{argmax}}{\mathfrak{a}} \left( {}^{t\_{\text{lkg}}} \right)^{+1}} \right] \tag{9}$$

This equation has been used in current research, such as Bah et al. [29], Capson-Tojo et al. [3], Bayrakdar et al. [4], Mancini et al. [8], Martín Juárez et al. [9], and Li et al. [7].

To experimentally obtain the maximum reproduction speed and the latency time, X is measured as well as the reactor time. Next by defining the value of a as the maximum cell concentration obtainable, Eq. (9) then can be linearized:

$$\ln\left(\ln\frac{X}{a}\right) = -\frac{\upsilon\_{\max}\cdot\boldsymbol{e}}{a}t + \left(\mathbf{1} + \frac{\upsilon\_{\max}\cdot\boldsymbol{e}}{a}t\_{\log}\right)\hat{\imath}$$

The latency time and the maximum speed of cellular reproduction will be characteristics of the microbial group in certain conditions.

### 3.2 Cumulative production curve of methane applying Gompertz

If we consider the product/biomass yield, we have.

$$Y\_{p/x} = \frac{P\_1 - P\_0}{X\_1 - X\_0} = \frac{dM}{dX}$$

$$\frac{dM}{dt} = Y\_{p/x} \frac{dX}{dt} \tag{10}$$

$$\frac{dM}{dt} = Y\_{p/x} \cdot a \cdot c \cdot e^{\left[-e^{-a^+t}\right]} \cdot e^{-ct + b}$$

$$\frac{dM}{dt} = Y\_{p/x} \cdot a \cdot c \cdot e^{\left[-e^{-\frac{\text{max}\cdot\epsilon}{d} + \frac{\text{max}\cdot\epsilon}{d}\log\_e + 1\right]}} \cdot e^{-\frac{\text{sum\\_c}}{d}\cdot t + \frac{v\_{\text{max}} \cdot t}{d} \cdot t\_{\text{lg}} + 1}$$

$$\frac{dM}{dt} = Y\_{p/x} \cdot a \cdot c \cdot e \cdot e^{\left[-e^{-\frac{\text{max}\cdot\epsilon}{d}\left(t\_{\text{lg}} - t\right)}\right]} \cdot e^{\frac{\text{max}\cdot\epsilon}{d}\left(t\_{\text{lg}} - t\right) + 1}$$

$$M = \int\_0^t Y\_{p/x} \cdot a \cdot c \cdot e^{-\left[-e^{\frac{\text{max}\cdot\epsilon}{d}\left(t\_{\text{lg}} - t\right)}\right]} \cdot e^{\frac{v\_{\text{max}\cdot\epsilon}}{d}\left(t\_{\text{lg}} - t\right) + 1} dt$$

From Eq. (10), we obtain the cumulative methane production Eq. (11):

$$\mathbf{M} = Y\_{p/\mathbf{x}} \cdot \mathbf{a} \cdot \mathbf{e} \begin{bmatrix} -\epsilon^{\frac{p\max\sigma}{d}\left(t\_{\text{leg}} - t\right) + 1} \\ \end{bmatrix} \tag{11}$$

To define this equation, it is necessary to obtain the value of three constants: a is

the maximum cellular concentration, b is a constant that depends on the initial concentration of cells and a, and c is the value of the cell growth rate where the growth velocity is at maximum, that is, at the inflection point of the curve. The Gompertz model implies that there is no maximum cell growth rate.

The complexity of the Gompertz model and the problems that exist when applying the derivatives of the Monod and Contois equation have led some researchers to suggest models that do not focus on the growth rate but on the kinetics of substrate degradation or product formation. Brulé et al. [31] classify the

4. Kinetic models

case substrate. So

calculated as

equation:

79

and k is the kinetic constant.

kinetic models into four groups:

a. Reaction in a single step with first-order kinetics.

Review of Mathematical Models for the Anaerobic Digestion Process

DOI: http://dx.doi.org/10.5772/intechopen.80815

c. Reaction in two speeds of a single step with first-order kinetics.

This model shows reaction rate is proportional to the amount of reagent, in this

where S is the amount of substrate at a time t, S0 is the initial substrate amount,

�k�<sup>t</sup>

¼ �k � t

),

�k�<sup>t</sup>

As the mass in the reaction is conserved, the mass of product M (methane) is

Angelidaki et al. [32] used this kinetic type, relating the concentration of methane that is generated in a reactor with the maximum potential through the following

M ¼ S<sup>0</sup> � 1 � e

ln Me � <sup>M</sup> Me 

methane production, and k is the constant of the hydrolysis rate.

M ¼ Me � 1 � e

where M is the methane produced at a given time t, Me is the value of the final

Díaz et al. [33] evaluated the digestion of cellulose with manure by comparing the first-order equation, including in the equation the latency time (13) and the modified Gompertz equation. They concluded that both models did not offer significant differences in the coefficient of determination obtained in the models (r2

�k�t

dt <sup>¼</sup> <sup>k</sup> � <sup>S</sup> ! <sup>S</sup> <sup>¼</sup> <sup>S</sup><sup>0</sup> � <sup>e</sup>

d.Reaction in two speeds of two steps with first-order kinetics.

dS

b.Two-step reaction with first-order kinetics.

4.1 One-step reaction with first-order kinetics

Taking limit when the time tends to infinity, it is shown that the methane potential produced is Yp=<sup>x</sup> � a:

$$\lim\_{t \to 0} M = Y\_{p/\mathbf{x}} \cdot a \cdot e^{-B} = Y\_{p/\mathbf{x}} \cdot a \cdot e^{\ln \frac{X\_1}{a}} = Y\_{p/\mathbf{x}} \cdot X\_0$$

$$\lim\_{t \to \infty} M = Y\_{p/\mathbf{x}} \cdot a$$

If we calculate the second derivative of the methane production curve and we equate to zero, then a maximum methane speed production point occurs:

$$\frac{d^2M}{dt^2} = 0$$

$$Y\_{p/\text{x}} \cdot a \cdot c \cdot e^{\left[-\epsilon^{\frac{\text{max}\cdot\epsilon}{2}\left(t\_{\text{lg}} - t\right)+1}\right]} \cdot \left(-\frac{v\_{\text{max}} \cdot e}{a}\right) \cdot e^{\frac{v\_{\text{max}}}{a}\left(t\_{\text{lg}}-t\right)+1} \cdot \left(\left(-\epsilon^{\frac{v\_{\text{max}} \cdot e}{a}\left(t\_{\text{lg}}-t\right)+1}\right)+1\right) = 0$$

$$\frac{v\_{\text{max}} \cdot e}{a} \left(t\_{\text{lg}} - t\right) + 1 = 0$$

$$t = \frac{a}{v\_{\text{max}} \cdot e} + t\_{\text{deg}} = \frac{b}{c}$$

The maximum methane production rate is vCH4max:

$$\upsilon\_{M\text{max}} = Y\_{p/\text{x}} \frac{a \cdot c}{e}$$

Lay et al. [30] proposed to modify the Gompertz Eq. (9) by applying the potential of producible methane, Me ¼ Yp=<sup>x</sup> � a, expressed as Eq. (12):

$$M = M\_{\varepsilon} \cdot e^{\left[-\frac{\nu\_{M\text{max}^{-\varepsilon}}{M\_{\varepsilon}} \left(t\_{\text{lg}} - t\right) + 1}\right]} \tag{12}$$

Table 1 shows the values obtained from the methane potential in various codigestion studies. All of them were carried out in mesophilic conditions, between 30 and 37°C. It can be observed that the production of methane in most cases ranges between 0.15 and 0.65 m<sup>3</sup> kg�<sup>1</sup> SV. Based on this calculation, we could classify the digestion processes into three groups: (a) low-production processes, the amount of methane produced is between 0.15 and 0.30 m<sup>3</sup> kg�<sup>1</sup> SV, (b) medium-production processes, the amount of methane produced is between 0.300 and 0.45 m<sup>3</sup> kg�<sup>1</sup> SV, and (c) high-production processes, the amount of methane produced is greater than 0.45 m<sup>3</sup> kg�<sup>1</sup> SV.

These types of productions and their energy equivalence mean that anaerobic digestion processes are considered more as a waste management and treatment process with a complementary energy product than as an alternative energy source to the problems derived from the limitation of fossil fuels.

### 3.3 Conclusions of the Gompertz model

The Gompertz model provides an equation that describes cell concentration over time in a fermentation process.

To define this equation, it is necessary to obtain the value of three constants: a is the maximum cellular concentration, b is a constant that depends on the initial concentration of cells and a, and c is the value of the cell growth rate where the growth velocity is at maximum, that is, at the inflection point of the curve.

The Gompertz model implies that there is no maximum cell growth rate.

### 4. Kinetic models

<sup>M</sup> <sup>¼</sup> Yp=<sup>x</sup> � <sup>a</sup> � <sup>e</sup> �<sup>e</sup>

potential produced is Yp=<sup>x</sup> � a:

Anaerobic Digestion

Yp=<sup>x</sup> � <sup>a</sup> � <sup>c</sup> � <sup>e</sup> �<sup>e</sup>

limt!0

<sup>v</sup>max�<sup>e</sup> <sup>a</sup> <sup>t</sup> lag ð Þ �<sup>t</sup> <sup>þ</sup><sup>1</sup> h i

between 0.15 and 0.65 m<sup>3</sup> kg�<sup>1</sup>

SV.

time in a fermentation process.

0.45 m<sup>3</sup> kg�<sup>1</sup>

78

M ¼ Yp=<sup>x</sup> � a � e

Taking limit when the time tends to infinity, it is shown that the methane

�<sup>B</sup> <sup>¼</sup> Yp=<sup>x</sup> � <sup>a</sup> � <sup>e</sup>

limt!<sup>∞</sup> <sup>M</sup> <sup>¼</sup> Yp=<sup>x</sup> � <sup>a</sup>

If we calculate the second derivative of the methane production curve and we

� e vmax�e

tlag � <sup>t</sup> � � <sup>þ</sup> <sup>1</sup> <sup>¼</sup> <sup>0</sup>

<sup>þ</sup> tlag <sup>¼</sup> <sup>b</sup> c

> a � c e

SV. Based on this calculation, we could classify the

SV, (b) medium-production

equate to zero, then a maximum methane speed production point occurs:

� � <sup>v</sup>max � <sup>e</sup> a � �

vmax � e a

The maximum methane production rate is vCH4max:

methane produced is between 0.15 and 0.30 m<sup>3</sup> kg�<sup>1</sup>

to the problems derived from the limitation of fossil fuels.

3.3 Conclusions of the Gompertz model

<sup>t</sup> <sup>¼</sup> <sup>a</sup> vmax � e

potential of producible methane, Me ¼ Yp=<sup>x</sup> � a, expressed as Eq. (12):

<sup>M</sup> <sup>¼</sup> Me � <sup>e</sup> �<sup>e</sup>

vMmax ¼ Yp=<sup>x</sup>

Lay et al. [30] proposed to modify the Gompertz Eq. (9) by applying the

vMmax�e Me � <sup>t</sup> lag ð Þ �<sup>t</sup> <sup>þ</sup><sup>1</sup> h i

Table 1 shows the values obtained from the methane potential in various codigestion studies. All of them were carried out in mesophilic conditions, between 30 and 37°C. It can be observed that the production of methane in most cases ranges

digestion processes into three groups: (a) low-production processes, the amount of

and (c) high-production processes, the amount of methane produced is greater than

These types of productions and their energy equivalence mean that anaerobic digestion processes are considered more as a waste management and treatment process with a complementary energy product than as an alternative energy source

The Gompertz model provides an equation that describes cell concentration over

processes, the amount of methane produced is between 0.300 and 0.45 m<sup>3</sup> kg�<sup>1</sup>

d2 M dt<sup>2</sup> <sup>¼</sup> <sup>0</sup>

<sup>v</sup>max�<sup>e</sup> <sup>a</sup> � <sup>t</sup> lag ð Þ �<sup>t</sup> <sup>þ</sup><sup>1</sup> h i

ln <sup>X</sup><sup>1</sup>

<sup>a</sup> <sup>t</sup>ð Þ lag�<sup>t</sup> <sup>þ</sup><sup>1</sup> � �<sup>e</sup>

<sup>a</sup> ¼ Yp=<sup>x</sup> � X<sup>0</sup>

vmax�e <sup>a</sup> <sup>t</sup>ð Þ lag�<sup>t</sup> <sup>þ</sup><sup>1</sup> � �

� �

(11)

þ 1

¼ 0

(12)

SV,

The complexity of the Gompertz model and the problems that exist when applying the derivatives of the Monod and Contois equation have led some researchers to suggest models that do not focus on the growth rate but on the kinetics of substrate degradation or product formation. Brulé et al. [31] classify the kinetic models into four groups:

a. Reaction in a single step with first-order kinetics.

b.Two-step reaction with first-order kinetics.

c. Reaction in two speeds of a single step with first-order kinetics.

d.Reaction in two speeds of two steps with first-order kinetics.

### 4.1 One-step reaction with first-order kinetics

This model shows reaction rate is proportional to the amount of reagent, in this case substrate. So

$$\frac{d\mathcal{S}}{dt} = k \cdot \mathcal{S} \to \mathcal{S} = \mathcal{S}\_0 \cdot e^{-k \cdot t}$$

where S is the amount of substrate at a time t, S0 is the initial substrate amount, and k is the kinetic constant.

As the mass in the reaction is conserved, the mass of product M (methane) is calculated as

$$\mathcal{M} = \mathbb{S}\_0 \cdot \left(\mathbb{1} - e^{-k \cdot t}\right),$$

Angelidaki et al. [32] used this kinetic type, relating the concentration of methane that is generated in a reactor with the maximum potential through the following equation:

$$
\ln\left(\frac{M\_\epsilon - M}{M\_\epsilon}\right) = -k \cdot t
$$

$$
M = M\_\epsilon \cdot \left(1 - e^{-k \cdot t}\right)
$$

where M is the methane produced at a given time t, Me is the value of the final methane production, and k is the constant of the hydrolysis rate.

Díaz et al. [33] evaluated the digestion of cellulose with manure by comparing the first-order equation, including in the equation the latency time (13) and the modified Gompertz equation. They concluded that both models did not offer significant differences in the coefficient of determination obtained in the models (r2 ), neither in the methane potential predicted Me nor between the constant kinetics k and vMmax. However, it shows that the first-order kinetic model provides a longer latency time. The maximum methane potential Me was between 0.30 and 0.33 m<sup>3</sup> / kg SV:

$$M = M\_{\varepsilon} \cdot \left(\mathbf{1} - e^{-k \cdot \left(t - t\_{\mathrm{lg}}\right)}\right) \tag{13}$$

SVFA ¼ k<sup>1</sup> � S<sup>0</sup> �

dM

dt <sup>¼</sup> <sup>k</sup><sup>2</sup> � <sup>k</sup><sup>1</sup> � <sup>S</sup><sup>0</sup> �

4.3 Reaction in two speeds of a single step with first-order kinetics

M ¼ Se � 1 � α � e

0.521 � 29 m<sup>3</sup> CH4 kg�<sup>1</sup> VS.

complicates its application.

81

<sup>M</sup> <sup>¼</sup> Se � <sup>α</sup> � <sup>1</sup> � kHFe�kMF�<sup>t</sup> � kMFe�kHF�<sup>t</sup>

dM

Review of Mathematical Models for the Anaerobic Digestion Process

DOI: http://dx.doi.org/10.5772/intechopen.80815

From this equation, the accumulated methane production is obtained as

dt <sup>¼</sup> <sup>k</sup><sup>2</sup> � SVFA

<sup>M</sup> <sup>¼</sup> <sup>S</sup><sup>0</sup> � <sup>1</sup> � <sup>k</sup>1e�k2�<sup>t</sup> � <sup>k</sup>2e�k1�<sup>t</sup>

The chemical composition of the substrates is generally heterogeneous and can be constituted by several fractions with different hydrolysis rates. This implies that we can consider the process as two parallel but independent mechanisms that occur simultaneously. If we define α as the relation between the amount of rapidly degradable substrate and the total a, kF as the first-order kinetic constant for degradation of rapidly degradable substrate, and kL as the first-order kinetic constant for the degradation of slowly degradable substrate, the amount of methane produced can be defined with the model used by Kusch et al. [36] or Luna del Risco [37]:

<sup>e</sup>�k2�<sup>t</sup> � <sup>e</sup>�k1�<sup>t</sup> k<sup>2</sup> � k<sup>1</sup>

> <sup>e</sup>�k2�<sup>t</sup> � <sup>e</sup>�k1�<sup>t</sup> k<sup>2</sup> � k<sup>1</sup>

k<sup>1</sup> � k<sup>2</sup> 

�kF�<sup>t</sup> � ð Þ� <sup>1</sup> � <sup>α</sup> <sup>e</sup> �kL�<sup>t</sup>

Dennehy et al. [15] compared three different kinetic models to determine the most suitable to describe the kinetics of the discontinuous co-digestion of food waste and pig manure at 37°C: (1) first order, (2) Gompertz, and (3) two-speed one-step reaction with first-order kinetics. They showed that the three models provide similar determination coefficients; however, the RMSE (root of the mean of the squares of the errors) is significantly reduced when the two-speed digestion is considered. The worst RMSE was for the Gompertz model. The first-order kinetic model reduced the RMSE by 39%, and the first-order kinetic model with two speeds

reduced the RMSE by 80%. The highest methane yields they obtained were

If we consider two steps in each of the fractions of which the substrate is composed, both for the rapidly degradable substrate fraction and for the slowly

> kHF � kMF <sup>þ</sup> ð Þ� <sup>1</sup> � <sup>α</sup> <sup>1</sup> � kHLe�kML�<sup>t</sup> � kMLe�kHL�<sup>t</sup> kHL � kML

Brulé et al. [31] evaluated the four kinetic models described, concluding that

reasonable estimate. In contrast, the model that considers two speeds with a single

the models that consider an easy speed in both a step and two steps yield a

step produces overestimates. Therefore, it is considered inadequate. This overestimation is corrected by applying the two-step model at two speeds but

4.4 Reaction in two speeds of two steps with first-order kinetics

degradable substrate fraction, we can obtain the following equation:

Zhang et al. [34] also compared the modified Gompertz equation and thefirstorder kinetic model according to Eq. (13). Zhang confirms that the first-order kinetic model provides longer latency times and methane potentials than Gompertz. However, it provides slightly lower coefficients of determination.

### 4.2 Two-step reaction with first-order kinetics

Shin and Song [35] considered anaerobic digestion as a two-step process that could work at different speeds. Although this comprises a complex hydrolytic, acetogenic, and methanogenic process, a more suitable kinetic model than the previous one would consist in first considering the formation of volatile fatty acids (VFAs) from the substrate Se and, subsequently, the conversion of these acids into methane (M).

The formation of volatile fatty acids depends on the substrate concentration, following first-order kinetics, where k1 is the kinetic constant of transformation of the substrate to VFA, S is the substrate concentration, and SVFA is the concentration of acid grades:

$$\frac{d S\_{VFA}}{dt} = k\_1 \cdot S$$

Given the <sup>S</sup> <sup>¼</sup> <sup>S</sup><sup>0</sup> � <sup>e</sup>�k1�<sup>t</sup> , you have the equation:

$$\frac{d\mathbb{S}\_{VFA}}{dt} = k\_1 \cdot \mathbb{S}\_0 \cdot e^{-k\_1 \cdot t}$$

On the other hand, the elimination of the fatty acids will depend on the concentration of the same, also following first-order kinetics, being k2 as the kinetic constant of transformation of the VFA to M.

According to the mass balance in the formation of the VFA, a differential equation of constant coefficients of first order (14) is obtained:

$$\frac{d\mathcal{S}\_{VFA}}{dt} = k\_1 \cdot \mathcal{S}\_0 \cdot e^{-k\_1 \cdot t} - k\_2 \cdot \mathcal{S}\_{VFA}$$

$$\frac{d\mathcal{S}\_{VFA}}{dt} + k\_2 \cdot \mathcal{S}\_{VFA} = k\_1 \cdot \mathcal{S}\_0 \cdot e^{-k\_1 \cdot t} \tag{14}$$

such as

$$\boldsymbol{y}' + a(\boldsymbol{\kappa}) \cdot \boldsymbol{y} = b(\boldsymbol{\kappa})$$

$$\boldsymbol{y} = \boldsymbol{e}^{-\int a(\boldsymbol{\kappa})d\boldsymbol{x}} \cdot \int b(\boldsymbol{\kappa}) \cdot \boldsymbol{e}^{\int a(\boldsymbol{\kappa})d\boldsymbol{x}} d\boldsymbol{\kappa} + \boldsymbol{C} \cdot \boldsymbol{e}^{-\int a(\boldsymbol{\kappa})d\boldsymbol{x}}$$

The solution to Eq. (14) results

Review of Mathematical Models for the Anaerobic Digestion Process DOI: http://dx.doi.org/10.5772/intechopen.80815

neither in the methane potential predicted Me nor between the constant kinetics k and vMmax. However, it shows that the first-order kinetic model provides a longer latency time. The maximum methane potential Me was between 0.30 and 0.33 m<sup>3</sup>

Zhang et al. [34] also compared the modified Gompertz equation and thefirstorder kinetic model according to Eq. (13). Zhang confirms that the first-order kinetic model provides longer latency times and methane potentials than Gompertz.

Shin and Song [35] considered anaerobic digestion as a two-step process that could work at different speeds. Although this comprises a complex hydrolytic, acetogenic, and methanogenic process, a more suitable kinetic model than the previous one would consist in first considering the formation of volatile fatty acids (VFAs) from the substrate Se and, subsequently, the conversion of these acids into

The formation of volatile fatty acids depends on the substrate concentration, following first-order kinetics, where k1 is the kinetic constant of transformation of the substrate to VFA, S is the substrate concentration, and SVFA is the concentration

dt <sup>¼</sup> <sup>k</sup><sup>1</sup> � <sup>S</sup>

dSVFA

, you have the equation:

tration of the same, also following first-order kinetics, being k2 as the kinetic

According to the mass balance in the formation of the VFA, a differential

dt <sup>þ</sup> <sup>k</sup><sup>2</sup> � SVFA <sup>¼</sup> <sup>k</sup><sup>1</sup> � <sup>S</sup><sup>0</sup> � <sup>e</sup>

þ a xð Þ� y ¼ b xð Þ

b xð Þ� e Ð

dt <sup>¼</sup> <sup>k</sup><sup>1</sup> � <sup>S</sup><sup>0</sup> � <sup>e</sup>

On the other hand, the elimination of the fatty acids will depend on the concen-

�k1�t

�k1�<sup>t</sup> � <sup>k</sup><sup>2</sup> � SVFA

a xð Þdxdx <sup>þ</sup> <sup>C</sup> � <sup>e</sup>

� Ð a xð Þdx

�k1�<sup>t</sup> (14)

dSVFA

equation of constant coefficients of first order (14) is obtained:

dt <sup>¼</sup> <sup>k</sup><sup>1</sup> � <sup>S</sup><sup>0</sup> � <sup>e</sup>

y 0

dSVFA

dSVFA

�k� <sup>t</sup>�<sup>t</sup> ð Þ lag � �

M ¼ Me � 1 � e

However, it provides slightly lower coefficients of determination.

4.2 Two-step reaction with first-order kinetics

kg SV:

Anaerobic Digestion

methane (M).

of acid grades:

such as

80

Given the <sup>S</sup> <sup>¼</sup> <sup>S</sup><sup>0</sup> � <sup>e</sup>�k1�<sup>t</sup>

constant of transformation of the VFA to M.

y ¼ e � Ð a xð Þdx � ð

The solution to Eq. (14) results

/

(13)

$$\mathcal{S}\_{\rm VFA} = k\_1 \cdot \mathcal{S}\_0 \cdot \frac{e^{-k\_2 \cdot t} - e^{-k\_1 \cdot t}}{k\_2 - k\_1}$$

From this equation, the accumulated methane production is obtained as

$$\frac{dM}{dt} = k\_2 \cdot \mathbf{S}\_{\text{VFA}}$$

$$\frac{dM}{dt} = k\_2 \cdot k\_1 \cdot \mathbf{S}\_0 \cdot \frac{e^{-k\_2 \cdot t} - e^{-k\_1 \cdot t}}{k\_2 - k\_1}$$

$$M = \mathbf{S}\_0 \cdot \left(\mathbf{1} - \frac{k\_1 e^{-k\_1 \cdot t} - k\_2 e^{-k\_1 \cdot t}}{k\_1 - k\_2}\right)$$

### 4.3 Reaction in two speeds of a single step with first-order kinetics

The chemical composition of the substrates is generally heterogeneous and can be constituted by several fractions with different hydrolysis rates. This implies that we can consider the process as two parallel but independent mechanisms that occur simultaneously. If we define α as the relation between the amount of rapidly degradable substrate and the total a, kF as the first-order kinetic constant for degradation of rapidly degradable substrate, and kL as the first-order kinetic constant for the degradation of slowly degradable substrate, the amount of methane produced can be defined with the model used by Kusch et al. [36] or Luna del Risco [37]:

$$M = \mathbb{S}\_{\mathfrak{e}} \cdot \left(\mathbb{1} - a \cdot e^{-k\_{\mathbb{F}} \cdot t} - (\mathbb{1} - a) \cdot e^{-k\_{\mathbb{L}} \cdot t}\right),$$

Dennehy et al. [15] compared three different kinetic models to determine the most suitable to describe the kinetics of the discontinuous co-digestion of food waste and pig manure at 37°C: (1) first order, (2) Gompertz, and (3) two-speed one-step reaction with first-order kinetics. They showed that the three models provide similar determination coefficients; however, the RMSE (root of the mean of the squares of the errors) is significantly reduced when the two-speed digestion is considered. The worst RMSE was for the Gompertz model. The first-order kinetic model reduced the RMSE by 39%, and the first-order kinetic model with two speeds reduced the RMSE by 80%. The highest methane yields they obtained were 0.521 � 29 m<sup>3</sup> CH4 kg�<sup>1</sup> VS.

### 4.4 Reaction in two speeds of two steps with first-order kinetics

If we consider two steps in each of the fractions of which the substrate is composed, both for the rapidly degradable substrate fraction and for the slowly degradable substrate fraction, we can obtain the following equation:

$$\mathbf{M} = \mathbf{S}\_{\varepsilon} \cdot \left[ a \cdot \left( \mathbf{1} - \frac{k\_{\mathrm{HF}} \varepsilon^{-k\_{\mathrm{MF}} \varepsilon} - k\_{\mathrm{MF}} \varepsilon^{-k\_{\mathrm{HF}} \varepsilon}}{k\_{\mathrm{HF}} - k\_{\mathrm{MF}}} \right) + (\mathbf{1} - a) \cdot \left( \mathbf{1} - \frac{k\_{\mathrm{HL}} \varepsilon^{-k\_{\mathrm{ML}} \varepsilon} - k\_{\mathrm{ML}} \varepsilon^{-k\_{\mathrm{HL}} \varepsilon}}{k\_{\mathrm{HL}} - k\_{\mathrm{ML}}} \right) \right]$$

Brulé et al. [31] evaluated the four kinetic models described, concluding that the models that consider an easy speed in both a step and two steps yield a reasonable estimate. In contrast, the model that considers two speeds with a single step produces overestimates. Therefore, it is considered inadequate. This overestimation is corrected by applying the two-step model at two speeds but complicates its application.

### 5. Model based on the transfer function

Several studies, such as Ghufran and Charles [38], Li et al. [39], or Zahan et al. [13], have used a function derived from the first-order kinetic model but which substitutes the kinetic constant for the ratio between the maximum and the methane velocity:

$$M = M\_{\epsilon} \cdot \left(\mathbf{1} - e^{-k \cdot \left(t - t\_{\text{leg}}\right)}\right)$$

$$M = M\_{\epsilon} \cdot \left(\mathbf{1} - e^{-\frac{v\_{\text{max}}}{M\_{\epsilon}} \left(t - t\_{\text{leg}}\right)}\right)$$

### 6. Cone model

On the other hand, researchers, such as Pitt et al. [40], El-Mashad [41], Li et al. [39], and Zahan et al. [13], analyzed the cone model. This model describes the fermentation according to Eq. (15):

$$M = \frac{M\_\epsilon}{1 + (k \cdot t)^{-n}} \tag{15}$$

Comparing the values of r<sup>2</sup>

estimates higher latency periods.

0.15 and 0.30 m<sup>3</sup> kg<sup>1</sup>

than 0.45 m<sup>3</sup> kg<sup>1</sup>

between 0.30 and 0.45 m<sup>3</sup> kg<sup>1</sup>

The average lag time is 14 days.

SV.

8. Conclusion

Figure 3.

ences in the RMSE.

0.65 m<sup>3</sup> kg<sup>1</sup>

potential:

83

ance, the results shown in Figures 2 and 3 were obtained.

Review of Mathematical Models for the Anaerobic Digestion Process

DOI: http://dx.doi.org/10.5772/intechopen.80815

As you can see, all the models provide high coefficients of determination, and there are few differences between them. The transfer model and the first-order kinetic model generally produce higher RMSE, so the modified Gompertz model and the cone model make more accurate estimates. However, the Gompertz model

LSD intervals of the analysis of variance at 95% confidence level for the comparison of the latency time of the different models applied to the fermentation of different substances and combinations in co-digestion.

In this research work, the most important kinetic models used to describe anaerobic fermentation have been developed. The comparison between them is a subject currently studied as demonstrated in recent publications. All of them provide high coefficients of determination; however, they present significant differ-

SV, under mesophilic conditions (30–37°C). However, digestion pro-

The production of methane in most cases ranges between 0.15 and

SV.

The mean of the first-order kinetic constant is 0.11 d<sup>1</sup>

cesses can be classified into three groups according to the methane production

a. low-production processes, when the amount of methane produced is between

b.medium-production processes, when the amount of methane produced is

c. high-production processes, when the amount of methane produced is greater

.

SV.

, RMSE, and lag time provided by analysis of vari-

### 7. Comparison of models

For the evaluation of the models, most researchers usually use two statistics: (a) coefficient of determination of the fit (r2 ) and (b) root of the mean of the squares of the errors (RMSE) calculated by Eq. (16), where Mmodel is the value of methane predicted by the model at an instant t and Mob is the value of methane observed experimentally:

$$RMSE = \sqrt{\frac{\sum \left(M\_{\text{model}} - M\_{\text{ob}}\right)^2}{n}} \tag{16}$$

Pitt et al. [40], Ghufran and Charles [38], El-Mashad [41], Li et al. [39], and Zahan et al. [13] compared the modified Gompertz model, the first-order kinetic model, the transfer function model, and the cone model, for different types of substrates and combinations in co-digestion.

### Figure 2.

LSD intervals of the analysis of variance at 95% confidence level for the comparison of the RMSE and the r<sup>2</sup> of the different models applied to the fermentation of different substances and combinations in co-digestion.

Review of Mathematical Models for the Anaerobic Digestion Process DOI: http://dx.doi.org/10.5772/intechopen.80815

### Figure 3.

5. Model based on the transfer function

ane velocity:

Anaerobic Digestion

6. Cone model

experimentally:

Figure 2.

82

fermentation according to Eq. (15):

7. Comparison of models

coefficient of determination of the fit (r2

substrates and combinations in co-digestion.

Several studies, such as Ghufran and Charles [38], Li et al. [39], or Zahan et al. [13], have used a function derived from the first-order kinetic model but which substitutes the kinetic constant for the ratio between the maximum and the meth-

On the other hand, researchers, such as Pitt et al. [40], El-Mashad [41], Li et al.

For the evaluation of the models, most researchers usually use two statistics: (a)

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ∑ð Þ Mmodel � Mob

n

the errors (RMSE) calculated by Eq. (16), where Mmodel is the value of methane predicted by the model at an instant t and Mob is the value of methane observed

Pitt et al. [40], Ghufran and Charles [38], El-Mashad [41], Li et al. [39], and Zahan et al. [13] compared the modified Gompertz model, the first-order kinetic model, the transfer function model, and the cone model, for different types of

LSD intervals of the analysis of variance at 95% confidence level for the comparison of the RMSE and the r<sup>2</sup> of the different models applied to the fermentation of different substances and combinations in co-digestion.

s

RMSE ¼

[39], and Zahan et al. [13], analyzed the cone model. This model describes the

<sup>M</sup> <sup>¼</sup> Me

�k� <sup>t</sup>�<sup>t</sup> ð Þ lag � �

�vmax<sup>M</sup> Me � <sup>t</sup>�<sup>t</sup> ð Þ lag � �

<sup>1</sup> <sup>þ</sup> ð Þ <sup>k</sup> � <sup>t</sup> �<sup>n</sup> (15)

) and (b) root of the mean of the squares of

(16)

2

M ¼ Me � 1 � e

M ¼ Me � 1 � e

LSD intervals of the analysis of variance at 95% confidence level for the comparison of the latency time of the different models applied to the fermentation of different substances and combinations in co-digestion.

Comparing the values of r<sup>2</sup> , RMSE, and lag time provided by analysis of variance, the results shown in Figures 2 and 3 were obtained.

As you can see, all the models provide high coefficients of determination, and there are few differences between them. The transfer model and the first-order kinetic model generally produce higher RMSE, so the modified Gompertz model and the cone model make more accurate estimates. However, the Gompertz model estimates higher latency periods.

### 8. Conclusion

In this research work, the most important kinetic models used to describe anaerobic fermentation have been developed. The comparison between them is a subject currently studied as demonstrated in recent publications. All of them provide high coefficients of determination; however, they present significant differences in the RMSE.

The production of methane in most cases ranges between 0.15 and 0.65 m<sup>3</sup> kg<sup>1</sup> SV, under mesophilic conditions (30–37°C). However, digestion processes can be classified into three groups according to the methane production potential:


The average lag time is 14 days. The mean of the first-order kinetic constant is 0.11 d<sup>1</sup> . Anaerobic Digestion

### Author details

Borja Velázquez-Martí<sup>1</sup> \*, Orlando W. Meneses-Quelal<sup>1</sup> , Juan Gaibor-Chavez<sup>2</sup> and Zulay Niño-Ruiz<sup>2</sup>

1 Departamento de Ingeniería Rural y Agroalimentaria, Universitat Politècnica de Valencia, Valencia, Spain

References

9783527632794

[1] Deublein D, Steinhauser A. Biogas from Waste and Renewable Resources: An Introduction. 2nd ed. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA; 2010. 532 p. DOI: 10.1002/

DOI: http://dx.doi.org/10.5772/intechopen.80815

Review of Mathematical Models for the Anaerobic Digestion Process

paper. Energy Conversion and Management. 2018;156:279-287. DOI: 10.1016/j.enconman.2017.08.002

from wheat straw by chemical

[9] Martín Juárez J, Riol Pastor E, Fernández Sevilla JM, Muñoz Torre R, García-Encina PA, Bolado Rodríguez S. Effect of pretreatments on biogas production from microalgae biomass grown in pig manure treatment plants. Bioresource Technology. 2018;257: 30-38. DOI: 10.1016/j.biortech.2018.

[10] Mustafa AM, Li H, Radwan AA,

pretreatments on anaerobic digestion of

production. Bioresource Technology. 2018;259:54-60. DOI: 10.1016/j.

[11] Vazifehkhoran AH, Shin SG, Triolo JM. Use of tannery wastewater as an alternative substrate and a pretreatment medium for biogas

production. Bioresource Technology. 2018;258:64-69. DOI: 10.1016/j.

[12] Xu W, Fu S, Yang Z, Lu J, Guo R. Improved methane production from

[13] Zahan Z, Othman MZ, Muster TH. Anaerobic digestion/co-digestion kinetic potentials of different agroindustrial wastes: A comparative batch

Sheng K, Chen X. Effect of hydrothermal and Ca(OH)2

sugarcane bagasse for biogas

biortech.2018.03.028

biortech.2018.02.116

2018.02.046

corn straw by microaerobic pretreatment with a pure bacteria system. Bioresource Technology. 2018; 259:18-23. DOI: 10.1016/j.biortech.

2017.12.045

02.063

[8] Mancini G, Papirio S, Lens PNL, Esposito G. Increased biogas production

pretreatments. Renewable Energy. 2018; 119:608-614. DOI: 10.1016/j.renene.

[2] Chynoweth DP, Wilkie AC, Owens JM. Anaerobic processing of piggery wastes: A review. In: ASAE Annual International Meeting; 12–16 July. Orlando, Florida, USA: St Joseph: American Society of Agricultural Engineers (ASAE); 1998. 38 pp

[3] Capson-Tojo G, Rouez M, Crest M, Trably E, Steyer J-P, Bernet N, et al. Kinetic study of dry anaerobic codigestion of food waste and cardboard for methane production. Waste Management. 2017;69:470-479. DOI: 10.1016/j.wasman.2017.09.002

[4] Bayrakdar A, Sürmeli RÖ, Çalli B. Anaerobic digestion of chicken manure by a leach-bed process coupled with side-stream membrane ammonia separation. Bioresource Technology. 2018;258:41-47. DOI: 10.1016/j.

[5] Franco RT, Buffière P, Bayard R. Coensiling of cattle manure before biogas production: Effects of fermentation stimulants and inhibitors on biomass and methane preservation. Renewable Energy. 2018;121:315-323. DOI: 10.1016/

[6] Guo J, Cui X, Sun H, Zhao Q, Wen X, Pang C, et al. Effect of glucose and cellulase addition on wet-storage of excessively wilted maize stover and biogas production. Bioresource Technology. 2018;259:198-206. DOI: 10.1016/j.biortech.2018.03.041

[7] Li W, Siddhu MAH, Amin FR, He Y,

Zhang R, Liu G, et al. Methane production through anaerobic codigestion of sheep dung and waste

85

biortech.2018.02.117

j.renene.2018.01.035

2 Departamento de Investigación, Centro de Investigación del Ambiente, Universidad Estatal de Bolívar, Guaranda, Ecuador

\*Address all correspondence to: borvemar@dmta.upv.es

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Review of Mathematical Models for the Anaerobic Digestion Process DOI: http://dx.doi.org/10.5772/intechopen.80815

### References

[1] Deublein D, Steinhauser A. Biogas from Waste and Renewable Resources: An Introduction. 2nd ed. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA; 2010. 532 p. DOI: 10.1002/ 9783527632794

[2] Chynoweth DP, Wilkie AC, Owens JM. Anaerobic processing of piggery wastes: A review. In: ASAE Annual International Meeting; 12–16 July. Orlando, Florida, USA: St Joseph: American Society of Agricultural Engineers (ASAE); 1998. 38 pp

[3] Capson-Tojo G, Rouez M, Crest M, Trably E, Steyer J-P, Bernet N, et al. Kinetic study of dry anaerobic codigestion of food waste and cardboard for methane production. Waste Management. 2017;69:470-479. DOI: 10.1016/j.wasman.2017.09.002

[4] Bayrakdar A, Sürmeli RÖ, Çalli B. Anaerobic digestion of chicken manure by a leach-bed process coupled with side-stream membrane ammonia separation. Bioresource Technology. 2018;258:41-47. DOI: 10.1016/j. biortech.2018.02.117

[5] Franco RT, Buffière P, Bayard R. Coensiling of cattle manure before biogas production: Effects of fermentation stimulants and inhibitors on biomass and methane preservation. Renewable Energy. 2018;121:315-323. DOI: 10.1016/ j.renene.2018.01.035

[6] Guo J, Cui X, Sun H, Zhao Q, Wen X, Pang C, et al. Effect of glucose and cellulase addition on wet-storage of excessively wilted maize stover and biogas production. Bioresource Technology. 2018;259:198-206. DOI: 10.1016/j.biortech.2018.03.041

[7] Li W, Siddhu MAH, Amin FR, He Y, Zhang R, Liu G, et al. Methane production through anaerobic codigestion of sheep dung and waste

paper. Energy Conversion and Management. 2018;156:279-287. DOI: 10.1016/j.enconman.2017.08.002

[8] Mancini G, Papirio S, Lens PNL, Esposito G. Increased biogas production from wheat straw by chemical pretreatments. Renewable Energy. 2018; 119:608-614. DOI: 10.1016/j.renene. 2017.12.045

[9] Martín Juárez J, Riol Pastor E, Fernández Sevilla JM, Muñoz Torre R, García-Encina PA, Bolado Rodríguez S. Effect of pretreatments on biogas production from microalgae biomass grown in pig manure treatment plants. Bioresource Technology. 2018;257: 30-38. DOI: 10.1016/j.biortech.2018. 02.063

[10] Mustafa AM, Li H, Radwan AA, Sheng K, Chen X. Effect of hydrothermal and Ca(OH)2 pretreatments on anaerobic digestion of sugarcane bagasse for biogas production. Bioresource Technology. 2018;259:54-60. DOI: 10.1016/j. biortech.2018.03.028

[11] Vazifehkhoran AH, Shin SG, Triolo JM. Use of tannery wastewater as an alternative substrate and a pretreatment medium for biogas production. Bioresource Technology. 2018;258:64-69. DOI: 10.1016/j. biortech.2018.02.116

[12] Xu W, Fu S, Yang Z, Lu J, Guo R. Improved methane production from corn straw by microaerobic pretreatment with a pure bacteria system. Bioresource Technology. 2018; 259:18-23. DOI: 10.1016/j.biortech. 2018.02.046

[13] Zahan Z, Othman MZ, Muster TH. Anaerobic digestion/co-digestion kinetic potentials of different agroindustrial wastes: A comparative batch

Author details

Anaerobic Digestion

84

Borja Velázquez-Martí<sup>1</sup>

Valencia, Valencia, Spain

and Zulay Niño-Ruiz<sup>2</sup>

\*, Orlando W. Meneses-Quelal<sup>1</sup>

2 Departamento de Investigación, Centro de Investigación del Ambiente,

Universidad Estatal de Bolívar, Guaranda, Ecuador

provided the original work is properly cited.

\*Address all correspondence to: borvemar@dmta.upv.es

1 Departamento de Ingeniería Rural y Agroalimentaria, Universitat Politècnica de

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

, Juan Gaibor-Chavez<sup>2</sup>

study for C/N optimisation. Waste Management. 2018;71:663-674. DOI: 10.1016/j.wasman.2017.08.014

[14] Aboudi K, Álvarez-Gallego CJ, Romero-García LI. Evaluation of methane generation and process stability from anaerobic co-digestion of sugar beet by-product and cow manure. Journal of Bioscience and Bioengineering. 2016;121(5):566-572. DOI: 10.1016/j.jbiosc.2015.10.005

[15] Dennehy C, Lawlor PG, Croize T, Jiang Y, Morrison L, Gardiner GE, et al. Synergism and effect of high initial volatile fatty acid concentrations during food waste and pig manure anaerobic co-digestion. Waste Management. 2016; 56:173-180. DOI: 10.1016/j.wasman. 2016.06.032

[16] Glanpracha N, Annachhatre AP. Anaerobic co-digestion of cyanide containing cassava pulp with pig manure. Bioresource Technology. 2016; 214:112-121. DOI: 10.1016/j.biortech. 2016.04.079

[17] Marin Batista J, Salazar L, Castro L, Escalante-Hernández H. Co-digestión anaerobia de vinaza y gallinaza de jaula: alternativa para el manejo de residuos agrícolas colombianos. Revista Colombiana de Biotecnología. 2016; 18(2):6-12. Retrieved from: https:// dialnet.unirioja.es/descarga/articulo/ 5798936.pdf

[18] Aboudi K, Álvarez-Gallego CJ, Romero-García LI. Semi-continuous anaerobic co-digestion of sugar beet byproduct and pig manure: Effect of the organic loading rate (OLR) on process performance. Bioresource Technology. 2015;194:283-290. DOI: 10.1016/j. biortech.2015.07.031

[19] Belle AJ, Lansing S, Mulbry W, Weil RR. Anaerobic co-digestion of forage radish and dairy manure in complete mix digesters. Bioresource Technology. 2015;178:230-237. DOI: 10.1016/j. biortech.2014.09.036

[20] Cestonaro T, Costa MSS de M, Costa LA de M, Rozatti MAT, Pereira DC, Francisconi Lorin HE, et al. The anaerobic co-digestion of sheep bedding and ⩾50% cattle manure increases biogas production and improves biofertilizer quality. Waste Management. 2015;46:612-618. DOI: 10.1016/j.wasman.2015.08.040

[27] Gompertz B. On the nature of the function expressive of the law of human mortality, and on a new mode on determining the value of live contingencies. Philosophical

DOI: http://dx.doi.org/10.5772/intechopen.80815

Review of Mathematical Models for the Anaerobic Digestion Process

2011;102(21):10139-10142. DOI: 10.1016/j.biortech.2011.07.096

0634-0

09593331608616316

[34] Zhang H, Luo L, Li W, Wang X, Sun Y, Sun Y, et al. Optimization of mixing ratio of ammoniated rice straw and food waste co-digestion and impact o f trace element supplementation on biogas production. Journal of Material Cycles and Waste Management. 2018;20(2): 745-753. DOI: 10.1007/s10163-017-

[35] Shin HS, Song YC. A model for evaluation of anaerobic degradation characteristics of organic waste: Focusing on kinetics, rate-limiting step. Environmental Technology. 1995;16:775-784. DOI: 10.1080/

[36] Kusch S, Oechsner H, Jungbluth T. Biogas production with horse dung in

solid-phase digestion systems. Bioresource Technology. 2008;99:

[37] Luna del Risco M, Normak A, Orupold K. Biochemical methane potential of different organic wastes and energy crops from Estonia. Agronomy

[38] Ghufran R, Charles B. The use of a specific function to estimate maximum methane production in a batch-fed anaerobic reactor. Journal of Chemical Technology and Biotechnology. 2004; 79(10):1174-1178. DOI: 10.1002/

[39] Li K, Liu R, Sun C. Comparison of anaerobic digestion characteristics and kinetics of four livestock manures with different substrate concentrations. Bioresource Technology. 2015;198: 133-140. DOI: 10.1016/j.biortech.

[40] Pitt RE, Cross TL, Pell AN, Schofield P, Doane PH. Use of in vitro gas production models in ruminal kinetics. Mathematical Biosciences.

1280-1292. DOI: 10.1016/j. biortech.2007.02.008

Research. 2011;9:331-342

jctb.1107

2015.08.151

Transactions of the Royal Society of

[28] Zwietering MH, Jongenburger I, Rombouts FM, van't Riet K. Modeling of the bacterial growth curve. Applied and Environmental Microbiology. 1990;

[29] Bah H, Zhang W, Wu S, Qi D, Kizito

anaerobic co-digestion of palm pressed

Management. 2014;34(11):1984-1991. DOI: 10.1016/j.wasman.2014.07.015

[30] Lay JJ, Li YY, Noike T. Effect of moisture content and chemical nature on methane fermentation characteristics of municipal solid wastes. Doboku Gakkai Ronbunshu. 1996;1996(552): 101-108. DOI: 10.2208/jscej.1996.

[31] Brulé M, Oechsner H, Jungbluth T. Exponential model describing methane production kinetics in batch anaerobic digestion: A tool for evaluation of biochemical methane potential assays. Bioprocess and Biosystems Engineering. 2014;37(9):1759-1770. DOI: 10.1007/

[32] Angelidaki I, Alves M, Bolzonella D, Borzacconi L, Campos JL, Guwy AJ, et al. Defining the biomethane potential (BMP) of solid organic wastes and energy crops. A proposed protocol for batch assays. Water Science and Technology. 2009;59(5):927-934. Retrieved from http.//wst.iwaponline.

com/content/59/5/927.abstract

[33] Díaz I, Donoso-Bravo A, Fdz-Polanco M. Effect of microaerobic conditions on the degradation kinetics of cellulose. Bioresource Technology.

S, Dong R. Evaluation of batch

fiber and cattle manure under mesophilic conditions. Waste

London. 1825;115:513-585

56(6):1875-1881

552\_101

87

s00449-014-1150-4

[21] Di Maria F, Sordi A, Cirulli G, Micale C. Amount of energy recoverable from an existing sludge digester with the co-digestion with fruit and vegetable waste at reduced retention time. Applied Energy. 2015; 150:9-14. DOI: 10.1016/j.apenergy. 2015.01.146

[22] Fu SF, Wang F, Yuan XZ, Yang ZM, Luo SJ, Wang CS, et al. The thermophilic (55°C) microaerobic pretreatment of corn straw for anaerobic digestion. Bioresource Technology. 2015;175:203-208. DOI: 10.1016/j.biortech.2014.10.072

[23] Fu SF, Shi XS, Xu XH, Wang CS, Wang L, Dai M, et al. Secondary thermophilic microaerobic treatment in the anaerobic digestion of corn straw. Bioresource Technology. 2015;186: 321-324. DOI: 10.1016/j.biortech. 2015.03.053

[24] Agyeman FO, Tao W. Anaerobic co-digestion of food waste and dairy manure: Effects of food waste particle size and organic loading rate. Journal of Environmental Management. 2014;133: 268-274. DOI: 10.1016/j.jenvman. 2013.12.016

[25] Velázquez-Martí B.

Aprovechamieno de la biomasa para uso energético. 1st ed. Barcelona, Spain: Reverté; 2017. 840 p. ISBN: 9788429147544

[26] Winsor CP. The Gompertz curve as a growth curve. Proceedings of the National Academy of Sciences. 1932;18: 1-8

Review of Mathematical Models for the Anaerobic Digestion Process DOI: http://dx.doi.org/10.5772/intechopen.80815

[27] Gompertz B. On the nature of the function expressive of the law of human mortality, and on a new mode on determining the value of live contingencies. Philosophical Transactions of the Royal Society of London. 1825;115:513-585

study for C/N optimisation. Waste Management. 2018;71:663-674. DOI: 10.1016/j.wasman.2017.08.014

Anaerobic Digestion

[20] Cestonaro T, Costa MSS de M, Costa LA de M, Rozatti MAT, Pereira DC, Francisconi Lorin HE, et al. The anaerobic co-digestion of sheep bedding and ⩾50% cattle manure increases biogas production and improves biofertilizer quality. Waste Management. 2015;46:612-618. DOI: 10.1016/j.wasman.2015.08.040

[21] Di Maria F, Sordi A, Cirulli G, Micale C. Amount of energy

recoverable from an existing sludge digester with the co-digestion with fruit and vegetable waste at reduced retention time. Applied Energy. 2015; 150:9-14. DOI: 10.1016/j.apenergy.

[22] Fu SF, Wang F, Yuan XZ, Yang ZM,

[23] Fu SF, Shi XS, Xu XH, Wang CS, Wang L, Dai M, et al. Secondary thermophilic microaerobic treatment in the anaerobic digestion of corn straw. Bioresource Technology. 2015;186: 321-324. DOI: 10.1016/j.biortech.

[24] Agyeman FO, Tao W. Anaerobic co-digestion of food waste and dairy manure: Effects of food waste particle size and organic loading rate. Journal of Environmental Management. 2014;133: 268-274. DOI: 10.1016/j.jenvman.

Aprovechamieno de la biomasa para uso energético. 1st ed. Barcelona, Spain:

[26] Winsor CP. The Gompertz curve as a growth curve. Proceedings of the National Academy of Sciences. 1932;18:

Luo SJ, Wang CS, et al. The thermophilic (55°C) microaerobic pretreatment of corn straw for anaerobic digestion. Bioresource Technology. 2015;175:203-208. DOI: 10.1016/j.biortech.2014.10.072

2015.01.146

2015.03.053

2013.12.016

9788429147544

1-8

[25] Velázquez-Martí B.

Reverté; 2017. 840 p. ISBN:

[14] Aboudi K, Álvarez-Gallego CJ, Romero-García LI. Evaluation of methane generation and process stability from anaerobic co-digestion of sugar beet by-product and cow manure.

Bioengineering. 2016;121(5):566-572. DOI: 10.1016/j.jbiosc.2015.10.005

[15] Dennehy C, Lawlor PG, Croize T, Jiang Y, Morrison L, Gardiner GE, et al. Synergism and effect of high initial volatile fatty acid concentrations during food waste and pig manure anaerobic co-digestion. Waste Management. 2016; 56:173-180. DOI: 10.1016/j.wasman.

[16] Glanpracha N, Annachhatre AP. Anaerobic co-digestion of cyanide containing cassava pulp with pig manure. Bioresource Technology. 2016; 214:112-121. DOI: 10.1016/j.biortech.

[17] Marin Batista J, Salazar L, Castro L, Escalante-Hernández H. Co-digestión anaerobia de vinaza y gallinaza de jaula: alternativa para el manejo de residuos agrícolas colombianos. Revista Colombiana de Biotecnología. 2016; 18(2):6-12. Retrieved from: https:// dialnet.unirioja.es/descarga/articulo/

[18] Aboudi K, Álvarez-Gallego CJ, Romero-García LI. Semi-continuous anaerobic co-digestion of sugar beet byproduct and pig manure: Effect of the organic loading rate (OLR) on process performance. Bioresource Technology. 2015;194:283-290. DOI: 10.1016/j.

[19] Belle AJ, Lansing S, Mulbry W, Weil RR. Anaerobic co-digestion of forage radish and dairy manure in complete mix digesters. Bioresource Technology. 2015;178:230-237. DOI: 10.1016/j.

Journal of Bioscience and

2016.06.032

2016.04.079

5798936.pdf

biortech.2015.07.031

biortech.2014.09.036

86

[28] Zwietering MH, Jongenburger I, Rombouts FM, van't Riet K. Modeling of the bacterial growth curve. Applied and Environmental Microbiology. 1990; 56(6):1875-1881

[29] Bah H, Zhang W, Wu S, Qi D, Kizito S, Dong R. Evaluation of batch anaerobic co-digestion of palm pressed fiber and cattle manure under mesophilic conditions. Waste Management. 2014;34(11):1984-1991. DOI: 10.1016/j.wasman.2014.07.015

[30] Lay JJ, Li YY, Noike T. Effect of moisture content and chemical nature on methane fermentation characteristics of municipal solid wastes. Doboku Gakkai Ronbunshu. 1996;1996(552): 101-108. DOI: 10.2208/jscej.1996. 552\_101

[31] Brulé M, Oechsner H, Jungbluth T. Exponential model describing methane production kinetics in batch anaerobic digestion: A tool for evaluation of biochemical methane potential assays. Bioprocess and Biosystems Engineering. 2014;37(9):1759-1770. DOI: 10.1007/ s00449-014-1150-4

[32] Angelidaki I, Alves M, Bolzonella D, Borzacconi L, Campos JL, Guwy AJ, et al. Defining the biomethane potential (BMP) of solid organic wastes and energy crops. A proposed protocol for batch assays. Water Science and Technology. 2009;59(5):927-934. Retrieved from http.//wst.iwaponline. com/content/59/5/927.abstract

[33] Díaz I, Donoso-Bravo A, Fdz-Polanco M. Effect of microaerobic conditions on the degradation kinetics of cellulose. Bioresource Technology.

2011;102(21):10139-10142. DOI: 10.1016/j.biortech.2011.07.096

[34] Zhang H, Luo L, Li W, Wang X, Sun Y, Sun Y, et al. Optimization of mixing ratio of ammoniated rice straw and food waste co-digestion and impact o f trace element supplementation on biogas production. Journal of Material Cycles and Waste Management. 2018;20(2): 745-753. DOI: 10.1007/s10163-017- 0634-0

[35] Shin HS, Song YC. A model for evaluation of anaerobic degradation characteristics of organic waste: Focusing on kinetics, rate-limiting step. Environmental Technology. 1995;16:775-784. DOI: 10.1080/ 09593331608616316

[36] Kusch S, Oechsner H, Jungbluth T. Biogas production with horse dung in solid-phase digestion systems. Bioresource Technology. 2008;99: 1280-1292. DOI: 10.1016/j. biortech.2007.02.008

[37] Luna del Risco M, Normak A, Orupold K. Biochemical methane potential of different organic wastes and energy crops from Estonia. Agronomy Research. 2011;9:331-342

[38] Ghufran R, Charles B. The use of a specific function to estimate maximum methane production in a batch-fed anaerobic reactor. Journal of Chemical Technology and Biotechnology. 2004; 79(10):1174-1178. DOI: 10.1002/ jctb.1107

[39] Li K, Liu R, Sun C. Comparison of anaerobic digestion characteristics and kinetics of four livestock manures with different substrate concentrations. Bioresource Technology. 2015;198: 133-140. DOI: 10.1016/j.biortech. 2015.08.151

[40] Pitt RE, Cross TL, Pell AN, Schofield P, Doane PH. Use of in vitro gas production models in ruminal kinetics. Mathematical Biosciences.

1999;159(2):145-163. DOI: 10.1016/ S0025-5564(99)00020-6

[41] El-Mashad HM. Kinetics of methane production from the codigestion of switchgrass and Spirulina platensis algae. Bioresource Technology. 2013; 132:305-312. DOI: 10.1016/j. biortech.2012.12.183

Section 3

Anaerobic Digestion

Improvement and Evaluation

89

## Section 3
