Ethanol Production from Bioresources and Its Kinetic Modeling: Optimization Methods

*Manikandan Kanagasabai, Babu Elango, Preetha Balakrishnan and Jayachitra Jayabalan*

#### **Abstract**

Ethanol is viable alternative fuel and it's substitute to fossil fuel has gained importance with rise in fuel prices. The chapter elaborates about methods of production from different types of bio resources like molasses, starch and cellulose commercially. The chapter also details about different methods of pretreatment for cellulisic and starchy raw materials. This also includes hydrolysis using acid and enzymes. The modes of ethanol fermentation using bioreactors like batch fed batch and continuous operation will be discussed. The growth kinetics models like monad logistic model will be elaborated. The product formation growth associated models like Leudiking piret model and parameter estimation methods will be described. Optimization of process variables using response surface methodology and media optimization using PB design will be elaborated. The application of ANN in modeling will be described.

**Keywords:** ethanol production, growth and product kinetics, optimization response, surface methodology, PB design ANN

#### **1. Introduction**

Biomass energy can contribute to sustainable development and plays a significant role in reducing greenhouse gas emissions. In addition, application of agro-industrial residues in bioprocesses not only provide alternative substrates but also helps solve their disposal problem. The transfer of crude oil-based refinery to biomass–based bio refinery has attracted strong scientific interest which focuses on the development of bioethanol as an alternative transportation fuel to petroleum fuels. A huge ethanol demand will come in the near future, forcing the ethanol production to be fast and have high capacity. [1, 2]. Green fuel from bioresources is gaining more importance to meet the increasing demand of fuel supply and reduced emission of pollutant gases like sulfur dioxide [3]. The major biomass resources that can be converted into biofuels are classified into three major types of carbohydrates namely glucose, starch and cellulose [4]. Pre-treatment steps are more vital in conversion of starchy and

cellulosic substrates that are cheaper and available in plenty. Optimization techniques like artificial neural network, response surface methodology can be effectively used to improve the yield of biofuel from these substrates.

The biggest challenge of the conversion comes from the recalcitrant structure of lignocellulosic biomass. To improve the biomass digestibility, a pre-treatment step is required to break up the lignocellulosic matrix, thus making the carbohydrate fraction more accessible to hydrolytic enzymes for fermentable sugar production. Pretreatment of lignocelluloses is intended to disorganize the crystalline structure of macro-and microfibrils, in order to release the polymer chains of cellulose and hemicellulose to improve its digestibility and easy access for microbial attack [5] and/or modify the pores in the material to allow the enzymatic hydrolysis [6]. This leads to the fractionation of three components and opening of cellulose structure. It results in enlargement of the inner surface area of substrate particle's, accomplished by partial solubilization and or degradation of hemicellulose and lignin [7].

The bioconversion of starchy substrates like corn, cassava, potatoes, involves pretreatment with fungal alpha amylase at suitable temperature followed by saccharification and fermentation. The last two steps can also be carried out in a single fermenter by a process called simultaneous saccharification and fermentation using amylotic enzyme and yeast at optimum conditions of pH and temperature. This process reduces substrate inhibition and maximum utilization of glucose formed by amylotic enzymes. Cellulose from different biomass like paper pulp, sulfite waste liquor etc. must undergo pre-treatment with mineral acids or alkali to remove lignin and hemicellulose. Cellulose can be converted into simple sugars by the action of cellulase enzyme and can readily ferment them to ethanol by the action of yeast. [8]. Although lignocellulosic biomass is a very promising alternative feedstock for ethanol production, its conversion to ethanol is more difficult than that of sugar or starch.

Mathematical models are tools that can be applied to biotechnological processes operating at many different levels, from the action of an enzyme within a cell, to the growth of that cell within a commercial scale bioreactor [9]. Unstructured kinetic models give the most fundamental observations concerning microbial metabolic processes and can be considered a good approximation when the cell composition is time dependent or when the substrate concentration is high compared to the saturation constant [10]. In addition to kinetic models describing the behavior of microbiological systems, artificial neural network (ANN) and Response Surface Methodology (RSM) are effectively used for the optimization of process variables for the fermentation of bioethanol [11, 12].

This chapter will focus on the current status of available ethanol production technologies including chemistry, applications, technological aspects and the kinetics and modeling of biotechnological processes and it especially provides essential evidence for the future scale-up conversion studies.

#### **2. Industrial ethanol production**

#### **2.1 Ethanol production from corn**

Corn is the most viable source for industrial ethanol production as it contains 70% starch. 5% of annual food grins produced can be effectively diverted to ethanol production [13]. The major threat that poses corn as an effective substrate is the horny endosperm that makes it difficult for pretreatment with alpha amylase enzyme with

*Ethanol Production from Bioresources and Its Kinetic Modeling: Optimization Methods DOI: http://dx.doi.org/10.5772/intechopen.109200*

low conversion. Floury endosperm is easy to digest with enzymes and has high swelling when compared with horny endosperm.

The steps involved in the industrial production are


#### **2.2 Milling of grains**

The two main process that are employed in milling of grains are wet milling and dry milling of corn grains.

#### *2.2.1 Wet milling process*

The wet milling of corn grains is very expensive process as it is highly energy consuming. The valuable by products can be obtained in this process and it is generally used in biorefineries. The byproducts obtained from wet milling process are corn oil from germs, hull, fibers and gluten. Gluten along with corn meal is used as high protein content animal feed [14]. The main product starch solution obtained from this process is used as raw material for saccharification and fermentation process. The ethanol yield from this process is about 10 liters per 100 kg of corn used as substrate.

#### *2.2.2 Dry milling*

Dry grinding process is currently employed in industries as it is less energy intensive when compared with wet milling process [15]. Corn grains are powdered using hammer mill to pass through 30 mesh screen and mixed with eater to form mash. The mash is first liquefied using alpha amylase enzyme at a initial pH 6 and temperature of 90°C then saccharified with glucoamylase enzyme and fermented with yeast to produce ethanol [16]. The fermented liquid is distilled to produce industrial grade ethanol. The byproduct obtained is distiller's grain used as a animal feed stock and carbon dioxide.

#### **2.3 Liquefaction**

Starchy substrates cannot be utilized directly by yeast, and they are to be converted into maltose and dextrin in the first pretreatment step. A thermostable alpha-amylase enzyme is usually employed for converting the starch to dextrin quickly and randomly by hydrolyzing alpha 1–4 bonds of starch molecule. The optimum temperature and pH greatly depends upon the source of alpha amylase enzymes [17]. Microbial sources of alpha amylase includes both bacteria and fungus. Alpha amylase derived from fungi are mostly employed as they are more active at higher temperature in range of 80 to 90°C [18]. The optimum pH is in the range of 5.5- as the activity of the enzyme is maximum. The higher temperature favors the liquefaction of starch by rupturing the molecules and aiding the enzyme molecules to cleave the branching bonds of it. This process is carried out in holding tubes by adding fungal diastase enzyme in excess for a period of 30 minutes.

#### **2.4 Saccharification and fermentation of starch**

Liquified starch is converted into simple sugars by saccharification process. Glucoamylase enzyme is generally employed for this process. The optimum temperature is usually 50 to 55°C and pH is 4.5. Glucoamylase attacks the linear chain 1–6 linkages of dextrin and convert them into simple sugars [19]. Saccharified starch is suitable substrate for fermentative production of ethanol by yeast. Thermotolerant yeast can be used for carrying out saccharification and fermentation process in a single step. Optimization of process variables and nutrients is most important to maximize the ethanol yield. Response surface methodology can be employed to carry out optimization statistically by carefully designing the experiments. The RSM yields best combination of pH, temperature, initial starch concentration and enzyme concentration that give maximum ethanol concentration in the fermenter [20]. The interactive effects of process variables can be analyzed very effectively in the single step simultaneous saccharification and fermentation process. Optimization is essential because enzymes are active in a range of temperature and pH that may or may not favor the growth of yeast [21, 22]. Generally, thermotolerant yeast can withstand the temperature that favors optimum activity of glucoamylase enzyme that can produce simple sugars from dextrin and are consumed by yeast to produce ethanol. Optimum pH and temperature for this process are generally pH 5 and 37°C respectively as reported by many researchers [23, 24]. High temperature also favors reduced contamination of fermentation broth. They also reduces the inhibitive effect of high glucose concentration for yeast as it is simultaneously consumed giving 8 to 10% concentration of ethanol in the broth.

#### **2.5 Modes of ethanol fermentation**

Three different modes of fermentation that are generally used in commercial production are batch, fed batch and continuous operation. Ethanol production is usually carried out in batch fermenters [25]. The advantages of batch fermentation are mainly effective sterilization of medium and fermenter vessel, reduced contamination risk, maintaining required concentration of microbes for fermentation, effective control of process parameters like pH, temperature agitation speed to maximize the ethanol concentration in the broth. The usual fermentation time for ethanol production using yeast is about 48 hours. The additional feature is simultaneous saccharification and fermentation can be carried out most effectively in a single fermenter in batch mode by controlling the process variables at optimum conditions of pH, temperature and agitation speed. Continuous mode of operation is also employed for production of ethanol using beer still, by maintaining the microbes

#### *Ethanol Production from Bioresources and Its Kinetic Modeling: Optimization Methods DOI: http://dx.doi.org/10.5772/intechopen.109200*

inside the still using high aspect ratio. The yeast concentration is more at the bottom of the still and the main advantage is that is agitation is not required and continuous production of ethanol is feasible [26].

#### **2.6 Production of absolute alcohol**

Distillation is the process of separating the ethanol from the solids and water in the mash. The difference in boiling point of alcohol and water allows water to be separated from ethanol by heating in a distillation column. The maximum ethanol concentration in the top product of distillation column is 95% ethanol and 5% water. Azeotropic extractive distillation using benzene as third component produce absolute alcohol. Modern dry grind ethanol plants use a molecular sieve system to produce absolute (100%, or 200 proof) ethanol.

The bottom product containing fiber, oil, and protein components of the grain, as well as the non-fermented starch is used as feed stock for animals. Bottom product is further processed to remove insoluble solids using centrifuges or extruders. The liquid is decanted and part of it is recycled to the still. The remaining liquor is further concentrated by evaporation and mixed with solids and used ad feed stock. The block diagram for ethanol production from corn is shown in **Figure 1**.

#### **2.7 Ethanol production from molasses**

Sugarcane is grown primarily for sucrose and molasses production. The crop may be harvested over an extended period of time due to its long growing season. Black

**Figure 1.** *Ethanol production from corn.*

strap molasses is the by- product obtained from sugar mill and can be stored and easily diluted to a concentration of 10% reducing sugars [27]. The total sugar content of molasses is generally in the range of 50 to 60%. The fermentation of diluted molasses is carried out in batch fermenters using yeast as inoculum. The fermentation time is generally 20–24 and rate of ethanol production decreases as sugar utilized by the yeast is about 90–95%. The ethanol concentration in the broth is about 10% and does not increase above it as ethanol inhibits the yeast growth. The additional nitrogen nutrients like ammonium sulphate and micronutrients like magnesium sulfate, potassium hydrogen phosphate is added to favor the growth of yeast [28]. Ethanol production is always growth associated fermentation and hence it is desirable to optimize the process variables like temperature pH and agitation to maximize the yeast concentration and ethanol production. The overall productivity for this process is about 1.8– 2.5 kg ethanol produced per m<sup>3</sup> fermenter volume per hour [29]. **Figure 2** illustrates the steps required to produce ethanol from cane juice molasses.

#### **2.8 Ethanol production from cellulose**

Cellulose-based ethanol production is attractive as an alternative fuel in the transportation sector. Extensive research in the past 20 years on fuel-grade ethanol fermentation has answered most of the major challenges on the road to commercialization and also open the door for novel technological development [30].

The first step in this process is primarily removal of lignin and hemi cellulose present in the wood wastes by digestion using steam at very high pressure followed by hydrolysis of cellulosic substrate into glucose using sulfuric acid [31]. The final step is fermentation of the reducing sugar solution in a fermenter using yeast. Hydrolysis of

*Ethanol Production from Bioresources and Its Kinetic Modeling: Optimization Methods DOI: http://dx.doi.org/10.5772/intechopen.109200*

**Figure 3.** *Ethanol production from cellulose.*

wood waste is carried out using sulfuric acid and steam at a temperature of 200°C and at a very high pressure of 1500 kPa. Lignin rich residue is removed as the bottom product and hydrolysate solution containing methanol and furfural is separated from the condenser of the flash vaporization process carried out in two different stages of different operating pressure, i.e., the first stage operating at 456.0 kPa and the second stage is at atmospheric pressure. The underflow of the second stage flash vaporizer is reducing sugar solution and it is diluted to desired concentration and fermented to ethanol using yeast. Methanol and furfural are separated using two stages of distillation.

The sugar rich solution is neutralized using lime and the precipitate is removed as sludge. The clarified liquid is added with additional micronutrients and fermented in a batch fermenter using yeast, The recovered yeast from an earlier fermentation is added to neutralized liquor and sent to fermentation tanks. The fermented liquor is filtered or centrifuged to remove the yeast content. Ethanol is separated from the distillation column as top product containing 95 vol.%. The bottom product contains unfermented pentoses. **Figure 3** shows the flow sheet of industrial ethanol production form cellulose.

#### **3. Modeling**

Mathematical models are very much useful to describe the performance of enzymatic and fermentation processes. They can be applied to study the kinetics of

enzyme catalyzed reactions, microbial growth in batch fermentation and product formation. These models fit the experimental data and can be modified effectively to find out the inhibiting parameters that affects the growth of microbes or activity of enzymes or rate of product formation [32]. The kinetic models are used in designing the batch fermenter suitable for ethanol production. In addition to kinetic models describing the behavior of microbiological systems, modeling based on combining the effects of various parameters namely time, temperature, and concentration into one single parameter is also used to develop a linear model expressing the relationship between ethanol production and variables like pH, temperature, and initial concentration of substrate. Artificial Neural Network (ANN) and Response Surface Methodology (RSM) are used for the optimization of process variables for the production of bioethanol [33].

#### **3.1 Kinetic models**

Kinetic models describing the behavior of microbiological systems can be a highly appreciated tool and can reduce tests to eliminate extreme possibilities. The kinetic parameters are obtained from the concentrations of biomass, products, and substrates consumed during the fermentation.

$$
\mu = \frac{\mu\_{\text{max}} \mathbf{S}}{\mathbf{K}\_{\text{s}} + \mathbf{S}} \tag{1}
$$

where μmax and Ks are the maximum specific growth rate and the Monod constant respectively.

#### *3.1.1 Logistic growth model*

$$\mathbf{x} = \frac{\mathbf{x}\_o \mathbf{e}^{\mathrm{kt}}}{\mathbf{1} - \beta \mathbf{x}\_o (\mathbf{1} - \mathbf{e}^{\mathrm{kt}})} \tag{2}$$

Where xo is the initial biomass concentration (g/l) and t is time (h).

#### *3.1.2 Leudeking-Piret kinetic model - product formation kinetics*

The rate of product formation is well described by an unstructured model. The kinetic expression, Leudeking-Piret equation, does fit the kinetic data of ethanol formation. Ethanol production is a growth associated product kinetics with significant α, the growth associated term [34].

$$p(t) - p\_o - \beta \left(\frac{\mathbf{x}\_s}{k}\right) \left[\mathbf{1} - \frac{\mathbf{x}\_o}{\mathbf{x}\_s} (\mathbf{1} - e^{kt})\right] = a[\mathbf{x}(t) - \mathbf{x}\_o] \tag{3}$$

#### *3.1.3 Substrate utilization kinetics*

$$-\frac{ds}{dt} = \frac{1}{Y\_{\chi\_{\circ}}} \frac{d\varkappa}{dt} + \frac{1}{Y\_{\mathcal{P}\_{\rm S}}} \frac{dp}{dt} + k\_{\rm c} \varkappa \tag{4}$$

where Yx/s and Yp/s are the yield coefficient for the biomass and product, respectively and Ke is the specific maintenance coefficient.

#### *3.1.4 Yield of growth*

To evaluate the model parameters and to relate the changes in substrate, cell mass and product concentrations, a Yield coefficient is introduced. The yield coefficients are related by the equation [35].

$$\mathbf{Y}\_{\mathbf{p}/\mathbf{s}} = \mathbf{Y}\_{\mathbf{x}/\mathbf{s}} \mathbf{Y}\_{\mathbf{p}/\mathbf{x}} \tag{5}$$

#### **3.2 Experimental design, analysis and optimization using response surface methodology (RSM)**

RSM is an empirical statistical technique employed for multiple regression analysis by using quantitative data obtained from properly designed experiments to solve multivariate equations simultaneously. The response surface, which is a two- dimensional graphic representation of the system's behavior, is used to determine the individual and cumulative effects of the variables and the mutual interactions between the variables on the dependent variable [36]. The graphic representations of the regression equation called the response surfaces. The quality of fit of the second order equation is expressed by the coefficient of determination R<sup>2</sup> , and its statistical significance is determined by F-test. The significance of each coefficient is determined using Student's t-test.

The application of statistical experimental design techniques in ethanol fermentation process development can result in improved production yields, reduced process variability, closer conformance of the output response (Ethanol yield) to nominal and target requirements and reduced development time and overall costs. The statistical design of experiments using central composite design can be effectively used to optimize the process variables like pH, temperature initial reducing sugar concentration to maximize the ethanol concentration [37]. The chosen independent variables used in this experiment are coded according to the Eq. (6):

$$\mathbf{x}\_{i} = \frac{\mathbf{X}\_{i} - \mathbf{X}\_{o}}{\Delta \mathbf{x}} \tag{6}$$

where xi is the coded value of the ith variable, Xi is the uncoded value of the ith test variable and Xo is the uncoded value of the ith test variable at the centre point. The behavior of the system is explained by the following second- degree polynomial Eq. (7):

$$\mathbf{Y} = \mathfrak{f}\_{\mathbf{o}} + \sum\_{i=1}^{k} \mathfrak{f}\_{\mathbf{i}} \mathbf{X}\_{\mathbf{i}} + \sum\_{i=1}^{k} \mathfrak{f}\_{\mathbf{i}i} \mathbf{X}\_{\mathbf{i}}^{2} + \sum\_{i=1}^{k-1} \sum\_{j=2}^{k} \mathfrak{f}\_{\mathbf{i}\downarrow} \mathbf{X}\_{\mathbf{i}} \mathbf{X}\_{\mathbf{j}} \tag{7}$$

where Y is the predicted response, βo is the offset term, β<sup>i</sup> is the coefficient of linear effect, βii is the coefficient of squared effect and βij is the coefficient of interaction effect.

#### **3.3 Artificial neural network analysis**

Artificial Neural Network (ANN) is another important tool that has also been effectively used in modeling biotechnological process, majorly reduces time and

experimental readings required for modeling [38]. It is used to predict the degree of non-linearity and to learn complex analyses in various fields. RSM is used to analyze the data with least experimental data and gives a mathematical relation while ANN is a machine learning statistical approach that can be applied in a wide range of data analysis including optimization thereby estimates the response based on the trained data [39].

#### *3.3.1 Analysis of model predictability in artificial neural network*

Eqs. (8) and (9) indicates the Mean Squared Error (MSE) and RMSE, a widespread measure for model predictability in which the ANN output error between the actual and the predicted output can be evaluated [40]:

$$MSE = \frac{1}{n} \sum\_{i=1}^{n} \left(\boldsymbol{\wp}\_{i} - \boldsymbol{\wp}\_{di}\right)^{2} \tag{8}$$

$$\text{RMSE} = \text{MSE}^{\mathbb{A}} \tag{9}$$

where, n is the number of points, yi is the predicted value obtained from the neural network model, ydi is the actual value.

The closer the R2 value is to 1, the better the model fits to the actual data:

$$R^2 = 1 - \sum\_{i=1}^{n} \left( \frac{\left(\mathcal{y}\_i - \mathcal{y}\_{di}\right)^2}{\left(\mathcal{y}\_{di} - \mathcal{y}\_m\right)^2} \right) \tag{10}$$

where, n is the number of points, yi is the predicted value from neural network model, ydi is the actual value, and ym is the average of the actual values.

Absolute Average Deviation (AAD) in Eq. (11) is another significant index to evaluate the ANN output error between the actual and the predicted output:

$$\text{Absolute Average Deviation (AAD)} = \left\{ \left[ \sum\_{i=1}^{n} \left( |\boldsymbol{\eta}\_{i} - \boldsymbol{\eta}\_{d}|\_{\boldsymbol{\eta}\_{d}} \right) \right]\_{\boldsymbol{\eta}} \right\} \times \mathbf{100} \tag{11}$$

where, yi and ydi are the predicted and actual responses, respectively, and n is the number of the points.

#### **4. Conclusion**

Industrial production of ethanol is viable form cheaper substrates like biomass, corn and molasses. The molasses from sugar manufacturing unit can be used as a raw material for ethanol production by fermentation. The starch present in the corn is also used as substrate after liquefaction and saccharification using enzymes. The cellulosic raw materials are cheaper but it is essential to pre-treat the biomass residue by digesting the lignin and hemicellulose using sulfuric acid before fermentation to ethanol. Optimization of process variables like pH, temperature substrate concentration and agitation speed for maximum ethanol production is effectively done using statistical tool response surface methodology and artificial neural network. The modeling and kinetics of ethanol production is the most important to design the suitable bioreactors. Microbial growth kinetics, product formation kinetics and substrate utilization kinetics are enumerated.

*Ethanol Production from Bioresources and Its Kinetic Modeling: Optimization Methods DOI: http://dx.doi.org/10.5772/intechopen.109200*

#### **Author details**

Manikandan Kanagasabai<sup>1</sup> \*, Babu Elango<sup>1</sup> , Preetha Balakrishnan<sup>1</sup> and Jayachitra Jayabalan<sup>2</sup>

1 Faculty of Engineering and Technology Annamalai University, Department of Chemical Engineering, Bioprocess Laboratory, Annamalainagar, Tamilnadu, India

2 Faculty of Agriculture, Department of Agricultural Microbiology, Annamalai University, Annamalainagar, Tamilnadu, India

\*Address all correspondence to: kmchemical\_27@yahoo.co.in

© 2022 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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#### **Chapter 2**
