Leveraging Integrated Model-Based Approaches to Unlock Bioenergy Potentials in Enhancing Green Energy and Environment

*Fabrice Abunde Neba, Prince Agyemang, Yahaya D. Ndam, Endene Emmanuel, Eyong G. Ndip and Razak Seidu*

## **Abstract**

In the quest for a green economy, bioenergy has become a central component due to its ability to minimize depletion of natural energy resources and enhance environmental sustainability. However, the integration of bioenergy for a green economy has often led to policy resistance, the tendency for solutions to cause disastrous side effects on other aspects of the system that were not envisaged. The use of integrated model-based approaches for selection, design, and analysis of technological alternatives for bioenergy production would significantly enhance the systems'sustainability by optimizing design and operation, improving growth and profitability, and enabling a more synergistic interaction between the engineering and the macroeconomic aspects of bioenergy production systems. This chapter is designed to develop model-based methodological frameworks that will support sustainable decision making by all stakeholders involved in the design, operation, and commercialization of bioenergy production systems. Practical case studies are presented for bioethanol, biomethane, and synthetic gas production.

**Keywords:** system thinking, model identification and analysis, bioreactor synthesis, performance targeting, and economics

## **1. Introduction**

### **1.1 Bioenergy as a source of sustainable energy**

Increasing concerns about depletion of natural resources, precarious nature of waste management and sanitation challenges, as well as environmental deterioration and climate change, have led to a growing interest by many countries to switch to renewable energy technologies. Consequently, the last two decades have seen a rapid implementation of new renewable energy systems, followed by integration of renewable energy into plants where fossil fuels exist. Amongst the existing renewable energy technologies, bioenergy systems are of special significance, because in addition to

being able to generate renewable energy, these systems also breakdown pollutants as well as recycle valuable nutrients found in organic waste [1]. Recent studies have confirmed that the bioenergy technology is robust and offers a great potential not only to reduce energy poverty through the provision of green energy but also enhance a green environment by reducing emissions associated with poor waste management [2–4]. According to one estimate on a bioenergy system, the anaerobic digestion technology, co-digestion of wastewater in a decentralized treatment plant with food wastes could allow the generation of 0.9 kWh electricity per person per day, leaving the nutrients as part of organic matter intact for agricultural use [5]. The recognition of the advantages of bioenergy systems in complying with the progressively more restrictive environmental requirements has led to an increased development and use of new bioenergy technologies, some of which include: sugar fermentation for bioethanol production, anaerobic digestion for biogas generation, pyrolysis for bio-oil production, microbial fuels cells for electricity generation, transesterification for biodiesel production, gasification for syngas production, etc. [6, 7].

understand how every specific component of the bioenergy system provides the value with regards to meeting the overall needs of the community. The objective here is to improve the economic value of the product by examining each component to determine how many functions that component performs, and the cost contributions of those functions. Systems components with high cost-function ratios are identified as opportunities for further investigation and improvement. The authors will like to mention here that a green economy cannot be achieved without changing the way we design and implement our solutions. However, international discussions on sustainable development have always focused on new technologies that can guarantee sustainability and ignored strategies or tools that can be used to design and implement technological solutions optimally. This chapter is therefore designed to fill this gap and provide a series of model-inspired tools, which can be

*Leveraging Integrated Model-Based Approaches to Unlock Bioenergy Potentials in Enhancing…*

*DOI: http://dx.doi.org/10.5772/intechopen.91978*

used to enhance the successful implementation of bioenergy technologies.

When confronted by any complex system, with things about it that you're dissatisfied with (environmental pollution and climate change) and anxious to fix (such as using bioenergy to enhance a green economy), you cannot just step in and set about fixing with much hope of helping. This realization is one of the sore discouragements of our century. You cannot meddle with one part of a complex system from the outside without the almost certain risk of setting off disastrous events that you hadn't counted on in other, remote parts. If you want to fix something, you are first obliged to understand the underlying dynamics of the whole system. Intervening is a way of causing trouble. This is because positively intended solutions to real problems have often led to policy resistance, the tendency for interventions to be delayed, diluted, or defeated by the response of the system to the intervention itself. Considering the complex nature of the economy, modelbased techniques, which simultaneously considers all degrees of freedom (the efficiency of the solution as well as the quantitative social economic and environmental impact both in time and space) by employing mathematical models becomes undoubtedly the strategy of choice. To understand the problems better and make good decisions, appropriate analysis tools are required. Previous analysis has tended to use 'soft' approaches, which do not require a knowledge of mathematical or computational techniques. These approaches can often be complementary to the techniques presented here. However, the use of mathematical and computational methods can be advantageous, due to the complexity and interactive nature of many of the problems involved and can, for instance, support decision making and making trade-offs in complex problems. However, many researchers are unfamiliar with the range of analytical, mathematical, and computational methods that could be applied in this area. Therefore, they are not able to take advantage of the full range of available methods in their research or analysis. This book chapter aims to fill this gap by providing both a basic introduction and advanced technical details of some of the available mathematical and computing methods, as well as illustrating

The methods presented here are aimed specifically at sustainable deployment of bioenergy technologies into production, and the case studies and examples are all in this area, but they have a wide range of other application areas, including in economics, medicine, and control systems. The techniques presented include:

• Systems theory and methodologies for structuring complex sustainable development problems to make it easier to obtain a solution to them.

**1.2 Model-based techniques for sustainable bioenergy systems**

their use through case studies and examples.

**91**

Special challenges arise when attempting to implement a bioenergy technology for renewable energy generation in a given community. Firstly, assessing and selecting the optimal technological alternatives that meet social, economic, and environmental sustainability standards is a challenging task. This is because the successful operation of bioenergy systems depends on the availability of a sustainable supply of feedstock, requiring tradeoffs to be made, on whether to use feedstocks and other utilities for bioenergy generation or to channel theses inputs to other industrial sectors requiring the same feedstocks and utility. In addition, bioenergy systems have specific characteristics, making them more adapted to specific feedstocks than others. Secondly, after knowing the technology to implement, challenges often arise from deciding on an optimal spatio-temporal strategy to implement the technology. A long-term perspective is needed to account for the spatio-temporal impact of the bioenergy system on the community to ensure that the system does on result in disastrous side-effects. Some systems might be reliable over the short term or in a given location but pose significant negative effects in the long-term or other locations. It is highly important to use systematic model-based techniques to understand the possible impacts of a given bioenergy system over time horizons that span from months to years, and determine the optimal implementation strategies, which maximize the positive effects and minimize the unwanted side-effects. Thirdly, wouldn't it be surprising if the authors state that getting the right technology and the right implementation strategy doesn't guarantee successful operation? Optimal operation of bioenergy systems requires optimal process configurations that ensure process stability, as well as maximize yield and productivity to ensure economic sustainability of the plant. Systematic mathematical and computational techniques are required for process modelling and simulation aimed at synthesizing optimal plant configurations well adapted to the specific feedstock characteristics of interest. Finally, after obtaining optimal process configurations, it is important to now shift the focus away from the technological solution and placing the focus on the practical considerations required for construction and installation of the technology vis-à-vis the required performance or need. This is highly important because the right technology, with optimal implementation strategy and optimal process configurations, can fail because of wrong equipment characteristics. For the same bioenergy technology, the choice of equipment components required for a rural community in a developing country would not be the same as that required in an urban setting. In addition, choice of equipment characteristics plays a significant role in the cost of installation, and there have been cases where projects have failed to get to completion due to high cost required for implementation. Systematic model-based techniques are again required to

*Leveraging Integrated Model-Based Approaches to Unlock Bioenergy Potentials in Enhancing… DOI: http://dx.doi.org/10.5772/intechopen.91978*

understand how every specific component of the bioenergy system provides the value with regards to meeting the overall needs of the community. The objective here is to improve the economic value of the product by examining each component to determine how many functions that component performs, and the cost contributions of those functions. Systems components with high cost-function ratios are identified as opportunities for further investigation and improvement. The authors will like to mention here that a green economy cannot be achieved without changing the way we design and implement our solutions. However, international discussions on sustainable development have always focused on new technologies that can guarantee sustainability and ignored strategies or tools that can be used to design and implement technological solutions optimally. This chapter is therefore designed to fill this gap and provide a series of model-inspired tools, which can be used to enhance the successful implementation of bioenergy technologies.

### **1.2 Model-based techniques for sustainable bioenergy systems**

When confronted by any complex system, with things about it that you're dissatisfied with (environmental pollution and climate change) and anxious to fix (such as using bioenergy to enhance a green economy), you cannot just step in and set about fixing with much hope of helping. This realization is one of the sore discouragements of our century. You cannot meddle with one part of a complex system from the outside without the almost certain risk of setting off disastrous events that you hadn't counted on in other, remote parts. If you want to fix something, you are first obliged to understand the underlying dynamics of the whole system. Intervening is a way of causing trouble. This is because positively intended solutions to real problems have often led to policy resistance, the tendency for interventions to be delayed, diluted, or defeated by the response of the system to the intervention itself. Considering the complex nature of the economy, modelbased techniques, which simultaneously considers all degrees of freedom (the efficiency of the solution as well as the quantitative social economic and environmental impact both in time and space) by employing mathematical models becomes undoubtedly the strategy of choice. To understand the problems better and make good decisions, appropriate analysis tools are required. Previous analysis has tended to use 'soft' approaches, which do not require a knowledge of mathematical or computational techniques. These approaches can often be complementary to the techniques presented here. However, the use of mathematical and computational methods can be advantageous, due to the complexity and interactive nature of many of the problems involved and can, for instance, support decision making and making trade-offs in complex problems. However, many researchers are unfamiliar with the range of analytical, mathematical, and computational methods that could be applied in this area. Therefore, they are not able to take advantage of the full range of available methods in their research or analysis. This book chapter aims to fill this gap by providing both a basic introduction and advanced technical details of some of the available mathematical and computing methods, as well as illustrating their use through case studies and examples.

The methods presented here are aimed specifically at sustainable deployment of bioenergy technologies into production, and the case studies and examples are all in this area, but they have a wide range of other application areas, including in economics, medicine, and control systems. The techniques presented include:

• Systems theory and methodologies for structuring complex sustainable development problems to make it easier to obtain a solution to them.

being able to generate renewable energy, these systems also breakdown pollutants as well as recycle valuable nutrients found in organic waste [1]. Recent studies have confirmed that the bioenergy technology is robust and offers a great potential not only to reduce energy poverty through the provision of green energy but also enhance a green environment by reducing emissions associated with poor waste management [2–4]. According to one estimate on a bioenergy system, the anaerobic digestion technology, co-digestion of wastewater in a decentralized treatment plant with food wastes could allow the generation of 0.9 kWh electricity per person per day, leaving the nutrients as part of organic matter intact for agricultural use [5]. The recognition of the advantages of bioenergy systems in complying with the progressively more restrictive environmental requirements has led to an increased development and use of new bioenergy technologies, some of which include: sugar fermentation for bioethanol production, anaerobic digestion for biogas generation, pyrolysis for bio-oil production, microbial fuels cells for electricity generation, transesterification for bio-

Special challenges arise when attempting to implement a bioenergy technology

for renewable energy generation in a given community. Firstly, assessing and selecting the optimal technological alternatives that meet social, economic, and environmental sustainability standards is a challenging task. This is because the successful operation of bioenergy systems depends on the availability of a sustainable supply of feedstock, requiring tradeoffs to be made, on whether to use feedstocks and other utilities for bioenergy generation or to channel theses inputs to other industrial sectors requiring the same feedstocks and utility. In addition, bioenergy systems have specific characteristics, making them more adapted to specific feedstocks than others. Secondly, after knowing the technology to implement, challenges often arise from deciding on an optimal spatio-temporal strategy to implement the technology. A long-term perspective is needed to account for the spatio-temporal impact of the bioenergy system on the community to ensure that the system does on result in disastrous side-effects. Some systems might be reliable over the short term or in a given location but pose significant negative effects in the long-term or other locations. It is highly important to use systematic model-based techniques to understand the possible impacts of a given bioenergy system over time horizons that span from months to years, and determine the optimal implementation strategies, which maximize the positive effects and minimize the unwanted side-effects. Thirdly, wouldn't it be surprising if the authors state that getting the right technology and the right implementation strategy doesn't guarantee successful operation? Optimal operation of bioenergy systems requires optimal process configurations that ensure process stability, as well as maximize yield and productivity to ensure economic sustainability of the plant. Systematic mathematical and computational techniques are required for process modelling and simulation aimed at synthesizing optimal plant configurations well adapted to the specific feedstock characteristics of interest. Finally, after obtaining optimal process configurations, it is important to now shift the focus away from the technological solution and placing the focus on the practical considerations required for construction and installation of the technology vis-à-vis the required performance or need. This is highly important because the right technology, with optimal implementation strategy and optimal process configurations, can fail because of wrong equipment characteristics. For the same bioenergy technology, the choice of equipment components required for a rural community in a developing country would not be the same as that required in an urban setting. In addition, choice of equipment characteristics plays a significant role in the cost of installation, and there have been cases where projects have failed to get to completion due to high cost required for implementation. Systematic model-based techniques are again required to

diesel production, gasification for syngas production, etc. [6, 7].

*Green Energy and Environment*

**90**

• Optimization and decision-making techniques to support policy formulation and other decision applications.

selection of an optimal implementation strategy for the system. In stage three, the concept of attainable regions is used for the design of optimal process configurations. The AR technique is a systematic approach to process synthesis, which integrates elements of geometry and mathematical optimization to understand how systems can be designed and improve. The power of the AR technique is that all possible states, for all possible bioreactor configurations, are first determined [8–10]. The AR can be constructed after specifying the geometric space, kinetic models, and the feed conditions; then, the appropriate objective functions can be overlaid on the AR boundary to identify the optimal operating points and associated process configurations [8]. Once the optimal process configuration has been identified, the next stage is to analyze the design process adequately. This is achieved by employing the functional analysis method, a technique that provides technological solutions and design specifications which permit the satisfaction of the principal and constraint function. Interesting, the technique provides an effective method in improving the quality and performance as well as minimizing the cost of the proposed solution [11]. Finally, the proposed solution can be constructed. It is worth noting that it is not the intention of the authors to discuss the construction phase,

*Leveraging Integrated Model-Based Approaches to Unlock Bioenergy Potentials in Enhancing…*

The implementation of successful sustainable energy solutions in local communities consist of a balance of social, economic, technical, and environmental aspects [12]. In that, the energy generated should: (1) be within an environmental tolerance limit, (2) generate employment opportunities for the locals, thereby improving their income and contribute to the regional or national economy, (3) meet the energy demand using available feedstock in the local community [13, 14]. These factors cannot be achieved without a systematic methodological framework that simultaneously considers all the degrees of freedom associated with the bioenergy system. This is because complex interactions exist between these factors, making the decision on the type of bioenergy system difficult. Therefore, sustainable decision making should integrate MCDM tool for successful bioenergy installation as this would guarantee the potentials for an increased standard of living as well as social and economic stability. The technique requires deciding on the type of decision model to employ and developing an evaluation criterion that reliably selects the best bioenergy alternative based on the aforementioned factors. **Table 1** illustrates the different categories of classifying MCDM and their respective methodologies.

A system is a set of interrelated elements, where any change in any element affects the set as a whole. Only elements directly or indirectly related to the problem form the system under study. To study a system, we must know the elements that make it up and the relationships between them. A strong focus must be geared towards understanding the characteristics of its constituents and the nature of the relationship that exists between them. This is necessitated because, more often than not, positively intended solutions to real-life problems have often led to unwanted side effects that were not envisaged [16]. Therefore considering the complex nature of systems, a system thinking approach that simultaneously considers all degrees of freedom of the problem is undoubtedly the strategy of choice. System thinking, also known as system dynamics modelling is a scientific framework for addressing

although it is mentioned.

**2.2 Modeling concepts**

*2.2.1 Multicriteria decision making*

*DOI: http://dx.doi.org/10.5772/intechopen.91978*

*2.2.2 System dynamics modeling*

**93**


## **2. Theoretical background**

## **2.1 Conceptual framework of an integrated model-based approach**

The conceptual model-based framework that can be used for the implementation of a given bioenergy technology in a community is illustrated in **Figure 1**, consisting of five major stages. Stage one involves deciding on the type of bioenergy to install. This decision making in energy supply is influenced by factors such as social, economic, environmental, political, and technical impact, making it helpful in developing a sustainable solution to the local community. Due to the difficulty in complex interactions between the aforementioned factors, the Multicriteria Decision Method (MCDM) is employed. This provides an approach that eliminates the challenges by developing evaluation criteria and methods that reliably measure sustainability, leading to the selection of an appropriate bioenergy system for the community. The next stage involves the use of system dynamic modelling to devise an implementation strategy for the proposed bioenergy technology from stage one. This stage involves the development of linear and non-linear mathematical models for the underlying mechanism of the system and evaluating the dynamic behaviors to identify policy resistance and any human decisions that can exacerbate perturbations. The strength of the technique is that it helps to minimize unforeseen side effects and generate a forecast to determine future side effects, aiding in the

*Leveraging Integrated Model-Based Approaches to Unlock Bioenergy Potentials in Enhancing… DOI: http://dx.doi.org/10.5772/intechopen.91978*

selection of an optimal implementation strategy for the system. In stage three, the concept of attainable regions is used for the design of optimal process configurations. The AR technique is a systematic approach to process synthesis, which integrates elements of geometry and mathematical optimization to understand how systems can be designed and improve. The power of the AR technique is that all possible states, for all possible bioreactor configurations, are first determined [8–10]. The AR can be constructed after specifying the geometric space, kinetic models, and the feed conditions; then, the appropriate objective functions can be overlaid on the AR boundary to identify the optimal operating points and associated process configurations [8]. Once the optimal process configuration has been identified, the next stage is to analyze the design process adequately. This is achieved by employing the functional analysis method, a technique that provides technological solutions and design specifications which permit the satisfaction of the principal and constraint function. Interesting, the technique provides an effective method in improving the quality and performance as well as minimizing the cost of the proposed solution [11]. Finally, the proposed solution can be constructed. It is worth noting that it is not the intention of the authors to discuss the construction phase, although it is mentioned.

### **2.2 Modeling concepts**

• Optimization and decision-making techniques to support policy formulation

• Attainable region technique for performance targeting, synthesis as well as analysis of process configurations required to operate bioenergy systems

• Functional analysis system technique, use to ensure that the engineering

**2.1 Conceptual framework of an integrated model-based approach**

*Model-based framework for bioenergy systems implementation in rural communities.*

systems are designed and constructed with minimal cost, and strongly align the

The conceptual model-based framework that can be used for the implementation of a given bioenergy technology in a community is illustrated in **Figure 1**, consisting of five major stages. Stage one involves deciding on the type of bioenergy to install. This decision making in energy supply is influenced by factors such as social, economic, environmental, political, and technical impact, making it helpful in developing a sustainable solution to the local community. Due to the difficulty in complex interactions between the aforementioned factors, the Multicriteria Decision Method (MCDM) is employed. This provides an approach that eliminates the challenges by developing evaluation criteria and methods that reliably measure sustainability, leading to the selection of an appropriate bioenergy system for the community. The next stage involves the use of system dynamic modelling to devise an implementation strategy for the proposed bioenergy technology from stage one. This stage involves the development of linear and non-linear mathematical models for the underlying mechanism of the system and evaluating the dynamic behaviors to identify policy resistance and any human decisions that can exacerbate perturbations. The strength of the technique is that it helps to minimize unforeseen side effects and generate a forecast to determine future side effects, aiding in the

and other decision applications.

*Green Energy and Environment*

**2. Theoretical background**

**Figure 1.**

**92**

needs of the community using the system.

### *2.2.1 Multicriteria decision making*

The implementation of successful sustainable energy solutions in local communities consist of a balance of social, economic, technical, and environmental aspects [12]. In that, the energy generated should: (1) be within an environmental tolerance limit, (2) generate employment opportunities for the locals, thereby improving their income and contribute to the regional or national economy, (3) meet the energy demand using available feedstock in the local community [13, 14]. These factors cannot be achieved without a systematic methodological framework that simultaneously considers all the degrees of freedom associated with the bioenergy system. This is because complex interactions exist between these factors, making the decision on the type of bioenergy system difficult. Therefore, sustainable decision making should integrate MCDM tool for successful bioenergy installation as this would guarantee the potentials for an increased standard of living as well as social and economic stability. The technique requires deciding on the type of decision model to employ and developing an evaluation criterion that reliably selects the best bioenergy alternative based on the aforementioned factors. **Table 1** illustrates the different categories of classifying MCDM and their respective methodologies.

### *2.2.2 System dynamics modeling*

A system is a set of interrelated elements, where any change in any element affects the set as a whole. Only elements directly or indirectly related to the problem form the system under study. To study a system, we must know the elements that make it up and the relationships between them. A strong focus must be geared towards understanding the characteristics of its constituents and the nature of the relationship that exists between them. This is necessitated because, more often than not, positively intended solutions to real-life problems have often led to unwanted side effects that were not envisaged [16]. Therefore considering the complex nature of systems, a system thinking approach that simultaneously considers all degrees of freedom of the problem is undoubtedly the strategy of choice. System thinking, also known as system dynamics modelling is a scientific framework for addressing


*2.2.4 Functional analysis (need analysis and technological features, design specification)*

*Leveraging Integrated Model-Based Approaches to Unlock Bioenergy Potentials in Enhancing…*

**3. Workflow of concepts and integrated model-based methodology**

This section discusses in detail, a step by step approach to how the techniques

There are several MCDM tools to deploy in selecting an appropriate bioenergy technology. The decision on the tool to employ at this stage is vital and requires selecting the best alternative, which is a difficult decision since there exists charac-

It is expedient for readers to note that not all MCDM methods presented in Section 2.2.1 are same. This is because some methods incorporate more features than others that are rather limited from different perspective [26]. Moreover, the choice of method is usually dependent on the decision makers' knowledge of the techniques and the availability of software that support the method ([14, 27]). In this regard, most multicriteria decision problems are adjusted to suite a particular method. However, subjective and objective MCDM tools such as Analytical Hierarchy Process (AHP) and Technique for order preference by similarity to ideal solution (TOPSIS), respectively, are often used in sustainable energy decisions. AHP provides a very simple and flexible model for a problem and is useful in achieving a consensus in cases where there are multiple conflicting criteria. However, its inability to capture uncertainties and determine alternative ratings in decision making is complimented by TOPSIS, making the use of an integrated AHP-TOPSIS

It is interesting to note that there exist frameworks that aid the selection of a decision-making tool as presented by Watróbski et al [27], that links a decisionmaking situation to the most suitable multicriteria decision method. However, this presents an inexhaustive, detailed and nearly impossible approach that takes into consideration all decision dimension, not to mention the extensive number and variety of methods available (in the reported presented by Watróbski et al, over 56 decision making tools exist). For this reason the authors focused on AHP and TOPSIS which has been successfully deployed by Akash et al., for the successful selection of an electric power plant [28] and Mohsen et al., in evaluation of an electric heating system [29]. It is also worth noting that it is not the intention of the authors to describe how to select a tool but rather to demonstrate how model-based techniques can be used to select an optimal bioenergy for a community. Interested

product.

**95**

**3.1 Workflow of methodology**

**3.2 Overview of MCDM methodology**

*DOI: http://dx.doi.org/10.5772/intechopen.91978*

teristics that are peculiar to each technology.

presented in the conceptual framework are deployed.

technique a more robust approach to decision making.

readers can resort to the referenced material in this section.

Function analysis, a vital component of value analysis, is a technique employed during the design and construction stage to assess a product's function to eliminate components that neither contribute to the quality nor improve the efficiency product. This technique provides an assessment of the proposed technology from different perspectives to adequately identify possible rooms of improvement [11]. The advantage of the technique is that it allows a transition from a focus on the expected solution to a problem to the appropriate and desired performance needs of the

### **Table 1.**

*Categories for classifying MCDM methodologies.*

complex, nonlinear feedback systems [17]. The strength of this technique is that it provides an opportunity to understand the dynamic behavior of the system under study and generate useful information that affects policy evaluations.

## *2.2.3 Attainable regions (performance targeting and equipment design)*

Attainable region (AR) is an approach to graphical/geometric optimization of bioreactor network synthesis. The technique originated from the work of Horn, who defined the AR as the set of all possible values of the outlet stream variables, which can be reached by any possible (physically realizable) steady-state reactor system from a given feed stream using only the processes of reaction and mixing. The technique has been used in the synthesis of isothermal reactor networks [18], synthesis and design of biogas digester structures [19–22], classical variations and dynamic problem synthesis, optimal batch distillation for reduced energy consumption [23, 24], and in analysis to optimize particle breakage in ball mills [25]. Interestingly, in recent publications by Abunde et al., the concept of AR has been extended to include Self-optimizing attainable regions for the design of anaerobic digesters [21]. This is the first of its kind and saught to addresses the design of anaerobic digesters in situations where reliable kinetic coefficients are unavailable. The technique offers exciting possibilities for process synthesis seeing the countless opportunities it holds to address reactor network synthesis problems. More importantly, there are speculations of an extension of the concept to the design of dryers and distillation columns. Other future studies could look at how self-optimizing AR for design could be integrated with self-optimizing controllers to achieve optimality in processes. The strength of the AR approach is that it simultaneously considers all possible outputs for all possible process configurations, by interpreting the process as a geometric object that defines the limits of achievability without having to enumerate all reactor configurations explicitly [8].

*Leveraging Integrated Model-Based Approaches to Unlock Bioenergy Potentials in Enhancing… DOI: http://dx.doi.org/10.5772/intechopen.91978*

### *2.2.4 Functional analysis (need analysis and technological features, design specification)*

Function analysis, a vital component of value analysis, is a technique employed during the design and construction stage to assess a product's function to eliminate components that neither contribute to the quality nor improve the efficiency product. This technique provides an assessment of the proposed technology from different perspectives to adequately identify possible rooms of improvement [11]. The advantage of the technique is that it allows a transition from a focus on the expected solution to a problem to the appropriate and desired performance needs of the product.

## **3. Workflow of concepts and integrated model-based methodology**

### **3.1 Workflow of methodology**

This section discusses in detail, a step by step approach to how the techniques presented in the conceptual framework are deployed.

### **3.2 Overview of MCDM methodology**

There are several MCDM tools to deploy in selecting an appropriate bioenergy technology. The decision on the tool to employ at this stage is vital and requires selecting the best alternative, which is a difficult decision since there exists characteristics that are peculiar to each technology.

It is expedient for readers to note that not all MCDM methods presented in Section 2.2.1 are same. This is because some methods incorporate more features than others that are rather limited from different perspective [26]. Moreover, the choice of method is usually dependent on the decision makers' knowledge of the techniques and the availability of software that support the method ([14, 27]). In this regard, most multicriteria decision problems are adjusted to suite a particular method. However, subjective and objective MCDM tools such as Analytical Hierarchy Process (AHP) and Technique for order preference by similarity to ideal solution (TOPSIS), respectively, are often used in sustainable energy decisions. AHP provides a very simple and flexible model for a problem and is useful in achieving a consensus in cases where there are multiple conflicting criteria. However, its inability to capture uncertainties and determine alternative ratings in decision making is complimented by TOPSIS, making the use of an integrated AHP-TOPSIS technique a more robust approach to decision making.

It is interesting to note that there exist frameworks that aid the selection of a decision-making tool as presented by Watróbski et al [27], that links a decisionmaking situation to the most suitable multicriteria decision method. However, this presents an inexhaustive, detailed and nearly impossible approach that takes into consideration all decision dimension, not to mention the extensive number and variety of methods available (in the reported presented by Watróbski et al, over 56 decision making tools exist). For this reason the authors focused on AHP and TOPSIS which has been successfully deployed by Akash et al., for the successful selection of an electric power plant [28] and Mohsen et al., in evaluation of an electric heating system [29]. It is also worth noting that it is not the intention of the authors to describe how to select a tool but rather to demonstrate how model-based techniques can be used to select an optimal bioenergy for a community. Interested readers can resort to the referenced material in this section.

complex, nonlinear feedback systems [17]. The strength of this technique is that it provides an opportunity to understand the dynamic behavior of the system under

Attainable region (AR) is an approach to graphical/geometric optimization of bioreactor network synthesis. The technique originated from the work of Horn, who defined the AR as the set of all possible values of the outlet stream variables, which can be reached by any possible (physically realizable) steady-state reactor system from a given feed stream using only the processes of reaction and mixing. The technique has been used in the synthesis of isothermal reactor networks [18], synthesis and design of biogas digester structures [19–22], classical variations and dynamic problem synthesis, optimal batch distillation for reduced energy consumption [23, 24], and in analysis to optimize particle breakage in ball mills [25]. Interestingly, in recent publications by Abunde et al., the concept of AR has been extended to include Self-optimizing attainable regions for the design of anaerobic digesters [21]. This is the first of its kind and saught to addresses the design of anaerobic digesters in situations where reliable kinetic coefficients are unavailable. The technique offers exciting possibilities for process synthesis seeing the countless opportunities it holds to address reactor network synthesis problems. More importantly, there are speculations of an extension of the concept to the design of dryers and distillation columns. Other future studies could look at how self-optimizing AR for design could be integrated with self-optimizing controllers to achieve optimality in processes. The strength of the AR approach is that it simultaneously considers all possible outputs for all possible process configurations, by interpreting the process as a geometric object that defines the limits of achievability without having to

study and generate useful information that affects policy evaluations.

**Categories Methodology** Multi-attribute utility and value theory AHP/ANP

Complex aggregation method ASPID Distance-to-target approach • TOPSIS

*Compiled from Refs. [13–15].*

*Green Energy and Environment*

*Categories for classifying MCDM methodologies.*

**Table 1.**

**94**

Multi-objective mathematical programming • Constrain programming

Non-classical method Fuzzy set methodology Elementary aggregation method • Weighted sum method

Direct ranking (high dependence on decision-maker) • Stepwise expert judgment

Outranking method • ELECTRE I, IS, II, III

• Linear programming • Goal programming

• Weighted product method

• Grey Relational Analysis • Data Enveloping Analysis

• PROMETHEE I, II

• Delphi • Scoring method

*2.2.3 Attainable regions (performance targeting and equipment design)*

enumerate all reactor configurations explicitly [8].

### *Green Energy and Environment*

AHP is a multi-level structured technique that presents a comprehensive framework for determining the different alternative solutions for a certain problem [30]. The technique was first introduced by Saaty in 1980 and is described in the following: the score of each alternative with respect to each criterion is known, the following

*Leveraging Integrated Model-Based Approaches to Unlock Bioenergy Potentials in Enhancing…*

In this step, the different attributes dimensions are transformed into nondimensional attribute, to allow comparison across the attributes. Using the

*ij* <sup>q</sup> *<sup>i</sup>* <sup>¼</sup> 1, 2, … *:*, *<sup>m</sup>*; *<sup>j</sup>* <sup>¼</sup> 1, 2, … , *<sup>n</sup>*

With the normalized decision matrix (R) computed from the previous step, the weighted matrix *W* from the AHP method is integrated into the R. This results

*Vij* ¼ *w <sup>j</sup>* � *rij where i* ¼ 1, 2, … *:*, *n* (4)

*w*1*r*<sup>11</sup> ⋯ *wnr*1*<sup>n</sup>* ⋮⋱⋮ *w*1*rm*<sup>1</sup> ⋯ *wnrmn*

<sup>00</sup> � � � � , *<sup>i</sup>* <sup>¼</sup> 1, 2, … *::*, *<sup>m</sup>*; *<sup>j</sup>*

<sup>00</sup> � � � � , *<sup>i</sup>* <sup>¼</sup> 1, 2, … *::*, *<sup>m</sup>*; *<sup>j</sup>*

00 is related to cost attributes

*i* ¼ 1, 2, … *::*, *m* (5)

*i* ¼ 1, 2, … *:*, *m* (6)

3 7 5

in a matrix that is computed by multiplying each column of R with its

This computation results in a new matrix V, which is represented below

2 6 4

In this process, two artificial alternatives *A*<sup>∗</sup> (the ideal alternative) and *A*� (the

<sup>0</sup> � �, *min jvij*j*<sup>i</sup>* <sup>∈</sup>*<sup>I</sup>*

<sup>0</sup> � �, *max jvij*j*i*<sup>∈</sup> *<sup>I</sup>*

In the process, the separation measurement is done by calculating the distance between each alternative in V and the ideal vector *A*<sup>∗</sup> using the Euclidean

*<sup>m</sup>*�*<sup>n</sup>* is normalized to *<sup>R</sup>* <sup>¼</sup>

(3)

steps could be followed in order to rank each alternative. **Step 1:** Construct the normalized decision matrix

method represented in Eq. (3), the matrix *xij* � �

*<sup>m</sup>*�*n*which takes the form shown below:

*r*<sup>11</sup> ⋯ *r*1*<sup>n</sup>*

1

CCCA

**Step 2:** Construct the weighted normalized decision matrix

associated weighted matrix *W* as represented in Eq. (4).

*v*<sup>11</sup> ⋯ *v*1*<sup>n</sup>* ⋮⋱⋮ *vm*<sup>1</sup> ⋯ *vmn*

**Step 3:** Determine the ideal and negative ideal solutions

<sup>0</sup> is related to benefit attributes and *I*

X*<sup>n</sup> j*¼1

X*<sup>n</sup> j*¼1

r

r

distant which is given as Eqs. (5) and (6)

*D*<sup>þ</sup> *<sup>i</sup>* ¼

*D*� *<sup>i</sup>* ¼

**Step 4:** Achieve the remoteness of all choices from *A*<sup>þ</sup> and *A*�

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

*vij* � *v*<sup>þ</sup> *j* � �<sup>2</sup>

*vij* � *v*� *j* � �<sup>2</sup>

⋮⋱⋮

*rm*<sup>1</sup> ⋯ *rmn*

*rij* <sup>¼</sup> *xij* ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P*<sup>m</sup> <sup>j</sup>*¼1*x*<sup>2</sup>

0

*DOI: http://dx.doi.org/10.5772/intechopen.91978*

BBB@

*V* ¼

<sup>2</sup> , … *::*, *v* <sup>∗</sup> *n* � � <sup>¼</sup> *max jvij*j*i*∈*<sup>I</sup>*

<sup>2</sup> , … *::*, *v*� *n* � � <sup>¼</sup> *min jvij*j*i*∈*<sup>I</sup>*

2 6 4

negative ideal alternative) are defined as:

*R* ¼

*rij* � �

*<sup>A</sup>*<sup>∗</sup> <sup>¼</sup> *<sup>v</sup>* <sup>∗</sup>

*A*� ¼ *v*�

where *I*

**97**

<sup>1</sup> , *v* <sup>∗</sup>

<sup>1</sup> , *v*�

¼ 1, 2, … , *n:*

¼ 1, 2, … , *n:*


$$\lambda\_{\text{max}} = \mathbf{1}/n \sum\_{i=1}^{n} \frac{i^{th} \text{entry in } A\mathcal{W}^T}{i^{th} \text{ entry in } \mathcal{W}^T} \tag{1}$$

where *λmax*, maximum Eigen value; n, number of attributes; A, pairwise comparison matrix; W, the estimate of the decision-maker's weight.

Nevertheless, the consistency is checked by comparing the consistency Index (CI) to the Random Index (RI) for the appropriate value of n, used in decisionmaking [30]. If (CI/RI) < 0.10, the degree of consistency is satisfactory, but if (CI/RI) > 0.10, serious inconsistencies may exist, and the results produced by AHP may not be meaningful.

$$CI = \frac{\lambda\_{\text{max}} - n}{n - 1} \tag{2}$$

where the variables have their usual meaning.

TOPSIS selects the best alternative based on their geometric distance from the positive or negative ideal solution. According to the technique, the best alternative from the positive ideal solution has the shortest geometric distance, while the negative ideal solution has the longest geometric distance. Assuming for the bioenergy system understudy, we have m alternatives, *n* number of attributes, and *Leveraging Integrated Model-Based Approaches to Unlock Bioenergy Potentials in Enhancing… DOI: http://dx.doi.org/10.5772/intechopen.91978*

the score of each alternative with respect to each criterion is known, the following steps could be followed in order to rank each alternative.

**Step 1:** Construct the normalized decision matrix

AHP is a multi-level structured technique that presents a comprehensive framework for determining the different alternative solutions for a certain problem [30]. The technique was first introduced by Saaty in 1980 and is described in the following:

• The first step involves developing a hierarchy structure that describes the goal,

• Pair-wise comparison for the criteria and alternatives with respect to the goal (objective) is established to extract the decision matrices using a nine-point scale. Comparing an attribute to itself is assigned a value of 1 so that if an n given criteria matrix is constructed at any given level, the diagonal entries will all be 1. The value 1 also signifies, equal relevance of attributes. The numbers 3, 5, 7, and 9 correspond to "moderate importance," "strong importance," "very strong importance," and "absolute importance", respectively. It is important to note that the length of the pair-wise matrix is equivalent to the number of

• The pair-wise comparison procedure is repeated for each criterion, and then the priority of alternatives is acquired by accumulating the weights. Statistical techniques such as arithmetic mean method, characteristic root method, and least square method can be employed to accumulate the weights. Adopting an arithmetic sum approach, a vector *W* ¼ ½ � *W*1,*W*2, …*WN* is constructed to represents the weight of each criterion in a pair-wise comparison matrix A. each element in column *j* of matrix A is divided by the sum of entries in the *j* column. This step generates a new matrix called the normalized matrix (*Anorm*).

• The final step involves making a decision based on the priorities set, but before that, the normalized matrix is subjected to a consistency check to evaluate whether the comparison made was sound. The check involves determining the

maximum Eigen values and consistency index using Eqs. (1) and (2), respectively. One advantage of the consistency ratio is that it eliminates the

> X*n i*¼1

where *λmax*, maximum Eigen value; n, number of attributes; A, pairwise

*i*

*i*

Nevertheless, the consistency is checked by comparing the consistency Index (CI) to the Random Index (RI) for the appropriate value of n, used in decisionmaking [30]. If (CI/RI) < 0.10, the degree of consistency is satisfactory, but if (CI/RI) > 0.10, serious inconsistencies may exist, and the results produced by AHP

*CI* <sup>¼</sup> *<sup>λ</sup>max* � *<sup>n</sup>*

TOPSIS selects the best alternative based on their geometric distance from the positive or negative ideal solution. According to the technique, the best alternative from the positive ideal solution has the shortest geometric distance, while the negative ideal solution has the longest geometric distance. Assuming for the bioenergy system understudy, we have m alternatives, *n* number of attributes, and

*thentry in AWT*

*th entry in W<sup>T</sup>* (1)

*<sup>n</sup>* � <sup>1</sup> (2)

problem of disagreements in individual judgments.

*λmax* ¼ 1*=n*

where the variables have their usual meaning.

comparison matrix; W, the estimate of the decision-maker's weight.

alternatives, criteria, and sub-criteria for evaluation.

attributes.

*Green Energy and Environment*

may not be meaningful.

**96**

In this step, the different attributes dimensions are transformed into nondimensional attribute, to allow comparison across the attributes. Using the method represented in Eq. (3), the matrix *xij* � � *<sup>m</sup>*�*<sup>n</sup>* is normalized to *<sup>R</sup>* <sup>¼</sup> *rij* � � *<sup>m</sup>*�*n*which takes the form shown below:

$$r\_{\vec{\eta}} = \frac{\mathbf{x\_{\vec{\eta}}}}{\sqrt{\sum\_{j=1}^{m} \mathbf{x\_{\vec{\eta}}^{2}}}} \qquad \qquad \qquad \qquad \qquad \qquad \mathbf{i = 1, 2, \dots, m; j = 1, 2, \dots, n}$$

$$R = \begin{pmatrix} r\_{11} & \cdots & r\_{1n} \\ \vdots & \ddots & \vdots \\ r\_{m1} & \cdots & r\_{mn} \end{pmatrix} \tag{3}$$

**Step 2:** Construct the weighted normalized decision matrix

With the normalized decision matrix (R) computed from the previous step, the weighted matrix *W* from the AHP method is integrated into the R. This results in a matrix that is computed by multiplying each column of R with its associated weighted matrix *W* as represented in Eq. (4).

$$V\_{\vec{\eta}} = w\_{\vec{\jmath}} \times r\_{\vec{\imath}\vec{\jmath}}\,\,where\,\,\, i = 1, 2, \,\ldots, n\tag{4}$$

This computation results in a new matrix V, which is represented below

$$V = \begin{bmatrix} v\_{11} & \cdots & v\_{1n} \\ \vdots & \ddots & \vdots \\ v\_{m1} & \cdots & v\_{mn} \end{bmatrix} = \begin{bmatrix} w\_1 r\_{11} & \cdots & w\_n r\_{1n} \\ \vdots & \ddots & \vdots \\ w\_1 r\_{m1} & \cdots & w\_n r\_{mn} \end{bmatrix}$$

**Step 3:** Determine the ideal and negative ideal solutions

In this process, two artificial alternatives *A*<sup>∗</sup> (the ideal alternative) and *A*� (the negative ideal alternative) are defined as:

$$\begin{array}{l} A^\* = \left\{ v\_1^\*, v\_2^\*, \dots, v\_n^\* \right\} = \left\{ \left( \max\_{j} v\_{j\bar{j}} | i \in I' \right), \left( \min\_{j} v\_{j\bar{j}} | i \in I' \right) \right\}, i = 1, 2, \dots, m; j = 1, 2, \dots, n. \end{array}$$

$$\begin{array}{lcl} A^- = \left\{ v\_1^-, v\_2^-, \dots, v\_n^- \right\} = \left\{ \left( \min\_j v\_{\vec{\eta}} \middle| i \in I' \right), \left( \max\_j v\_{\vec{\eta}} \middle| i \in I'' \right) \right\}, i = 1, 2, \dots, m; j = 1, 2, \dots, n. \end{array}$$

where *I* <sup>0</sup> is related to benefit attributes and *I* 00 is related to cost attributes **Step 4:** Achieve the remoteness of all choices from *A*<sup>þ</sup> and *A*�

In the process, the separation measurement is done by calculating the distance between each alternative in V and the ideal vector *A*<sup>∗</sup> using the Euclidean distant which is given as Eqs. (5) and (6)

$$D\_i^+ = \sqrt{\sum\_{j=1}^n \left(\upsilon\_{ji} - \upsilon\_j^+\right)^2} \text{ i = 1, 2, \dots, m} \tag{5}$$

$$D\_i^- = \sqrt{\sum\_{j=1}^n \left(v\_{\vec{\eta}} - v\_j^-\right)^2} \, i = 1, 2, \dots, m \tag{6}$$


**Step 5**: Determine the relative closeness to the ideal solution using Eq. (7).

$$\text{CC}\_{i}^{\*} = \frac{D\_{i}^{-}}{D\_{i}^{-} + D\_{i}^{+}} \text{ } i = \text{1, 2, ..., m} \tag{7}$$

**3.4 Overview of AR methodology**

*DOI: http://dx.doi.org/10.5772/intechopen.91978*

� � for each extract.

*X <sup>f</sup>* , *P <sup>f</sup>* , *S <sup>f</sup>*

**Figure 3.**

where

**99**

The AR is generated using two fundamental reactor types (CSTR and PFR) and

*Leveraging Integrated Model-Based Approaches to Unlock Bioenergy Potentials in Enhancing…*

The PFR Eq. (8) was solved using *MATLAB ode45*, the yield of each extract was

*<sup>d</sup>*<sup>τ</sup> <sup>¼</sup> *r C*ð Þ (8)

� � (9)

plotted as a function of PFR residence time τ using feed concentrations of *Cf* ¼

*dC*

τ � � <sup>¼</sup> f t, z ð Þ 1

*μX YPXμX*

*rP* � *MsX*

*rX* � <sup>1</sup> *YPX*

r Cð Þ¼ ryE

� 1 *YXS*

*f t*ð Þ¼ , *z*

*z* ¼ ð Þ *X*, *P*, *S*

ii. Plot the CSTR locus from the feed point.

mixing. The AR construction involves four major steps which include:

i. Generating the PFR trajectory from the feed point.

*Schematic diagram showing the steps involved in system dynamic modeling.*

where *CC*<sup>∗</sup> *<sup>i</sup>* is the performance score.

**Step 6:** Rank the alternatives according to relative closeness to ideal solution The set of the alternative Ai can now be ranked according to the descending order of *CC*<sup>∗</sup> *<sup>i</sup>* , the highest value, the better performance. **Figure 2** represents an integrated AHP-TOPSIS for multicriteria decision making.

From **Figure 2**, the AHP is used to determine the weight of each criterion, while the TOPSIS is applied to achieve the final ranking of the alternative bioenergy technology closest to the ideal solution.

### **3.3 Overview of system dynamics modeling methodology**

System dynamics deals with feedback and delays that affect system behavior over time. The power of the technique to capture the underlying dynamics of the essential components of the systems allows it to generate links and interactions that lead to a more accurate conclusion and a better understanding of a system. **Figure 3** illustrates a schematic diagram for the different theoretical and quantitative steps involved in system dynamic modelling.

*Leveraging Integrated Model-Based Approaches to Unlock Bioenergy Potentials in Enhancing… DOI: http://dx.doi.org/10.5772/intechopen.91978*

**Figure 3.**

where *D*<sup>þ</sup>

where *CC*<sup>∗</sup>

**Figure 2.**

**98**

order of *CC*<sup>∗</sup>

respectively.

*Green Energy and Environment*

*<sup>i</sup>* and *D*�

ideal and the negative ideal).

technology closest to the ideal solution.

involved in system dynamic modelling.

*presents an integrated AHP-TOPSIS for multicriteria decision making.*

*CC*<sup>∗</sup>

*<sup>i</sup>* is the performance score.

**3.3 Overview of system dynamics modeling methodology**

*<sup>i</sup>* <sup>¼</sup> *<sup>D</sup>*� *i D*� *<sup>i</sup>* þ *D*<sup>þ</sup> *i*

an integrated AHP-TOPSIS for multicriteria decision making.

the TOPSIS is applied to achieve the final ranking of the alternative bioenergy

*<sup>i</sup>* are the Euclidean distance from the ideal best and ideal worst,

*i* ¼ 1, 2, … *:*, *m* (7)

At the end of this, two quantities namely *Di* and *S <sup>j</sup>* for each alternative has been counted, representing the distance between each alternative and both (the

**Step 5**: Determine the relative closeness to the ideal solution using Eq. (7).

**Step 6:** Rank the alternatives according to relative closeness to ideal solution The set of the alternative Ai can now be ranked according to the descending

From **Figure 2**, the AHP is used to determine the weight of each criterion, while

System dynamics deals with feedback and delays that affect system behavior over time. The power of the technique to capture the underlying dynamics of the essential components of the systems allows it to generate links and interactions that lead to a more accurate conclusion and a better understanding of a system. **Figure 3** illustrates a schematic diagram for the different theoretical and quantitative steps

*<sup>i</sup>* , the highest value, the better performance. **Figure 2** represents

*Schematic diagram showing the steps involved in system dynamic modeling.*

### **3.4 Overview of AR methodology**

The AR is generated using two fundamental reactor types (CSTR and PFR) and mixing. The AR construction involves four major steps which include:

i. Generating the PFR trajectory from the feed point.

The PFR Eq. (8) was solved using *MATLAB ode45*, the yield of each extract was plotted as a function of PFR residence time τ using feed concentrations of *Cf* ¼ *X <sup>f</sup>* , *P <sup>f</sup>* , *S <sup>f</sup>* � � for each extract.

$$\frac{d\mathbf{C}}{d\mathbf{\tau}} = r(\mathbf{C}) \tag{8}$$

$$\mathbf{r}(\mathbf{C}) = \begin{bmatrix} \mathbf{r}\_{\text{yE}} \\ \boldsymbol{\pi} \end{bmatrix} = \begin{bmatrix} \mathbf{f}(\mathbf{t}, \mathbf{z}) \\ \mathbf{1} \end{bmatrix} \tag{9}$$

where

$$f(t,x) = \begin{bmatrix} \mu X \\\\ Y\_{PX}\mu X \\\\ -\frac{1}{Y\_{XS}}r\_X - \frac{1}{Y\_{PX}}r\_P - M\_s X \\\ z = (X, P, S) \end{bmatrix}.$$

ii. Plot the CSTR locus from the feed point.

The CSTR locus from the feed point *Cf* is found by solving the non-linear CSTR equation using MATLAB *fsolve* over a range of residence time, and plotting the yield in the (*yE* � *τ*) space. The data from the CSTR solution are presented as a collection of points and not a line because each residence time corresponds to a different operating scenario. The CSTR relation is represented in the relation below

$$\mathbf{C}\_f + \tau\_1 r(\mathbf{C}) - \mathbf{C} = \mathbf{0} \tag{10}$$

**Need:** something that is necessary or desired by the customer

**4. Application case studies**

*DOI: http://dx.doi.org/10.5772/intechopen.91978*

economic factors of the location.

the energy system.

**Table 2.**

**101**

*Matrix of bioenergy alternative.*

**4.1 Deciding on the type of bioenergy system**

The different considerations concerning the functions of the bioenergy technol-

This section deals with how the conceptual framework present in Section 2.1 is

This stage consists of using MCDM (AHP-TOPSIS) to decide on the type of bioenergy system to install considering the environmental, social, technical, and

**Table 2** presents the Alternatives Bioenergy Technologies (ABT), which are utilized as potential candidates for installation. The list presented in **Table 2** is not

The alternative strategies can be evaluated based on multiple attributes, which can be benefit or cost, as shown in **Table 3**. To adhere to the objectives of affordable and clean energy called for by the United Nations Sustainable Development Goal 7, the criteria considered are those that are dominant in determining sustainability of

**Figure 5** presents a hierarchical decomposition of the decision-making problem summarizing the overall objective, the alternatives, as well as the criteria and subcriteria used to evaluate the alternatives. This is structured in a well-organized

It is important to note that an initial assumption of equal weights for the major criteria was made, that is economic, environmental, technical, and social factors. **Figure 6** represents the weights of relative importance of each criterion obtained

The weights presented in **Figure 6**, implies that safety of the bioenergy technology is more relevant compared to the other criteria. Moreover, the different weights directly reflect the relative importance of environmental impact and safety criteria

The next step requires inputting the weights obtained from the AHP into the TOPSIS approach; this results in a ranking of the alternative source of bioenergy.

**Symbol Alternative strategy Type of process** ABT1 Sugar fermentation to produce bioethanol Biochemical ABT2 Anaerobic digestion to produce biogas Biochemical ABT3 Transesterification of oils to produce biodiesel Chemical ABT4 Biomass gasification to produce syngas Thermochemical ABT5 Biomass carbonation to produce biochar Thermochemical ABT6 Biomass compression to Briquette Thermochemical ABT7 Using microbial fuel cells to generate electricity Bio-electrochemical

exhaustive but only used to illustrate how the framework can be applied.

manner such that it shows how each level depends on the upper level.

using the AHP method (see steps presented in Section 3.2).

in the decision making of an alternative energy system.

ogy were obtained by applying the tools of the functional analysis technique.

*Leveraging Integrated Model-Based Approaches to Unlock Bioenergy Potentials in Enhancing…*

applied to a real-time bioenergy project. It's based on the implementation of bioenergy system in Kumasi, Ghana, a Sub-Saharan country in West Africa.

iii. Extend the AR boundary by running a series of PFR from each CSTR locus.

Solving the CSTR non-linear equation results in a series of points known as the CSTR locus. These points for each residence time are used as initial feed conditions to generate the PFR trajectory.

iv. Construct the convex hull. In broad reactor network synthesis terms, convex hull can be defined as the smallest subset of a set of points that encloses the original set of points [8]. The convex hull operation was carried out using MATLAB *convhull.* Identifying the convex hull for the set of points helps to identify unique points that can be used for mixing in order to extend the limit of achievability for the system.

### **3.5 Overview of functional analysis**

**Figure 4** presents a graphical representation of FAST technique, showing the different phases that will be used in the analysis.

Some of the terms employed in functional analysis are described below

**Function:** this defines the effect of the produced a product or one of its components to satisfy a need.

**Service function**: it is the function realized by a product in response to the need of a given user.

**Technical function**: an internal action of the product defined by the designer within the framework of a solution to assure the service function;

**Figure 4.** *The different phases of the functional analysis method.*

*Leveraging Integrated Model-Based Approaches to Unlock Bioenergy Potentials in Enhancing… DOI: http://dx.doi.org/10.5772/intechopen.91978*

**Need:** something that is necessary or desired by the customer

The different considerations concerning the functions of the bioenergy technology were obtained by applying the tools of the functional analysis technique.

## **4. Application case studies**

The CSTR locus from the feed point *Cf* is found by solving the non-linear CSTR equation using MATLAB *fsolve* over a range of residence time, and plotting the yield in the (*yE* � *τ*) space. The data from the CSTR solution are presented as a collection of points and not a line because each residence time corresponds to a different operating scenario. The CSTR relation is represented in the relation below

iii. Extend the AR boundary by running a series of PFR from each CSTR locus.

Solving the CSTR non-linear equation results in a series of points known as the CSTR locus. These points for each residence time are used as initial feed conditions

iv. Construct the convex hull. In broad reactor network synthesis terms, convex hull can be defined as the smallest subset of a set of points that encloses the original set of points [8]. The convex hull operation was carried out using MATLAB *convhull.* Identifying the convex hull for the set of points helps to identify unique points that can be used for mixing in

**Figure 4** presents a graphical representation of FAST technique, showing the

**Function:** this defines the effect of the produced a product or one of its compo-

**Service function**: it is the function realized by a product in response to the need

**Technical function**: an internal action of the product defined by the designer

Some of the terms employed in functional analysis are described below

within the framework of a solution to assure the service function;

order to extend the limit of achievability for the system.

to generate the PFR trajectory.

*Green Energy and Environment*

**3.5 Overview of functional analysis**

nents to satisfy a need.

of a given user.

**Figure 4.**

**100**

*The different phases of the functional analysis method.*

different phases that will be used in the analysis.

*Cf* þ *τ*1*r C*ð Þ� *C* ¼ 0 (10)

This section deals with how the conceptual framework present in Section 2.1 is applied to a real-time bioenergy project. It's based on the implementation of bioenergy system in Kumasi, Ghana, a Sub-Saharan country in West Africa.

### **4.1 Deciding on the type of bioenergy system**

This stage consists of using MCDM (AHP-TOPSIS) to decide on the type of bioenergy system to install considering the environmental, social, technical, and economic factors of the location.

**Table 2** presents the Alternatives Bioenergy Technologies (ABT), which are utilized as potential candidates for installation. The list presented in **Table 2** is not exhaustive but only used to illustrate how the framework can be applied.

The alternative strategies can be evaluated based on multiple attributes, which can be benefit or cost, as shown in **Table 3**. To adhere to the objectives of affordable and clean energy called for by the United Nations Sustainable Development Goal 7, the criteria considered are those that are dominant in determining sustainability of the energy system.

**Figure 5** presents a hierarchical decomposition of the decision-making problem summarizing the overall objective, the alternatives, as well as the criteria and subcriteria used to evaluate the alternatives. This is structured in a well-organized manner such that it shows how each level depends on the upper level.

It is important to note that an initial assumption of equal weights for the major criteria was made, that is economic, environmental, technical, and social factors.

**Figure 6** represents the weights of relative importance of each criterion obtained using the AHP method (see steps presented in Section 3.2).

The weights presented in **Figure 6**, implies that safety of the bioenergy technology is more relevant compared to the other criteria. Moreover, the different weights directly reflect the relative importance of environmental impact and safety criteria in the decision making of an alternative energy system.


The next step requires inputting the weights obtained from the AHP into the TOPSIS approach; this results in a ranking of the alternative source of bioenergy.

**Table 2.** *Matrix of bioenergy alternative.*


**Table 3.**

*Set of decision criteria to appropriate bioenergy technology selection.*

technology. However, before proceeding to the installation of the technology, the next section will discuss how an optimal implementation strategy could be identi-

*Leveraging Integrated Model-Based Approaches to Unlock Bioenergy Potentials in Enhancing…*

*Presents the ranking of alternative bioenergy technologies after an integrated AHP-TOPSIS approach.*

With the appropriate technology selected, the next step involves the selection of

an optimal implementation strategy that requires the development of dynamic models. It is relevant to note that the outcome of the proposed models from the methodology can be used to identify places of management potential (bioenergy policies) and future tipping points that can alleviate potential economic, environmental, and social challenges. The description of the dynamic behavior for bioethanol production was based on the underlying feedbacks and interactions between selected indicators is illustrated through the integrated causal loop diagram in **Figure 1**. The key relevant factors are investment, environmental impact,

fied using system dynamic modelling.

**Figure 6.**

**Figure 7.**

**103**

**4.2 Designing an optimal implementation strategy**

*A spider web diagram that describes the weight of each criterion.*

*DOI: http://dx.doi.org/10.5772/intechopen.91978*

### **Figure 5.**

*Hierarchical breakdown of selecting the bioenergy system problem.*

Based on the indicators used, lower-ranking of alternatives are more desirable and demonstrate favorability towards sustainability.

From **Figure 7**, sugar fermentation to produce bioethanol is the most appropriate technology for installation in the location of interest. So far, this section has focused on how MCDM tool can be used in selecting an appropriate bioenergy

*Leveraging Integrated Model-Based Approaches to Unlock Bioenergy Potentials in Enhancing… DOI: http://dx.doi.org/10.5772/intechopen.91978*

### **Figure 6.**

*A spider web diagram that describes the weight of each criterion.*

### **Figure 7.**

*Presents the ranking of alternative bioenergy technologies after an integrated AHP-TOPSIS approach.*

technology. However, before proceeding to the installation of the technology, the next section will discuss how an optimal implementation strategy could be identified using system dynamic modelling.

### **4.2 Designing an optimal implementation strategy**

With the appropriate technology selected, the next step involves the selection of an optimal implementation strategy that requires the development of dynamic models. It is relevant to note that the outcome of the proposed models from the methodology can be used to identify places of management potential (bioenergy policies) and future tipping points that can alleviate potential economic, environmental, and social challenges. The description of the dynamic behavior for bioethanol production was based on the underlying feedbacks and interactions between selected indicators is illustrated through the integrated causal loop diagram in **Figure 1**. The key relevant factors are investment, environmental impact,

Based on the indicators used, lower-ranking of alternatives are more desirable and

From **Figure 7**, sugar fermentation to produce bioethanol is the most appropriate technology for installation in the location of interest. So far, this section has focused on how MCDM tool can be used in selecting an appropriate bioenergy

demonstrate favorability towards sustainability.

*Hierarchical breakdown of selecting the bioenergy system problem.*

**Symbol Name of criteria Objective Description**

*Set of decision criteria to appropriate bioenergy technology selection.*

C5 Environmental impact (CO2 emissions)

*Green Energy and Environment*

**Table 3.**

**Figure 5.**

**102**

C1 Efficiency Maximize This measured quantity of bioenergy generated per

C2 Safety Maximized This measure the treat the technology possess on the

C4 Service life Maximize This defines how long the technology can sustainable run

technology

C6 Land use Minimize This describes the land space required to construct each equipment

C7 Job creation Maximize This describes the degree of job opportunities

C8 Cost of feedstock Minimize This measures the quantity of readily available

C9 Climate dependency Maximize Is the strategy optimal for different climatic and/or

C3 Investment cost Minimize This measure the capital required to establish the

quantity of feed for the different technologies

Minimize This measures the environmental friendliness of each

employees and environment

generated by each technology

feedstock and their cost.

geographical conditions?

bioenergy technology

**Figure 8.** *Casual loop diagram (CLD) for bioethanol production.*

employment creation, cost of feedstock, and land space. The causal loop diagrams (CLDs) presented in **Figure 8** are flexible and useful tools for diagramming the feedback structure of systems in any domain [16].

investment into a green economy. This has a direct positive influence on the

Stocks for this case study include water, bioethanol, feedstock, atmosphere CO2, populations, workforce, and GDP. These characterize the state of the bioenergy system and generate useful information for policy development. For example, the availability of feedstock for bioethanol has influenced the flow of CO2 uptake, feedstock production rate, and bioethanol demand. Simulation of these factors was conducted overtime period of 100 months. This indicated a strong correlation

Summarily, a hybrid system that works with the national grid is most preferable. This is because such a system will: (1) reduce environmental impact, (2) reduce pressure of land space for feedstock plantation and bioethanol plants, (3) ensure available water for human consumption and (4) most importantly ensure that there is a balance in the quantity of feedstock converted to fuel and consumed as food by the population. Readers need to note that not all the elements of the system were captured, rather key elements that significantly affect the behavior of the system.

Once the optimal implementation strategy had been achieved, the next step is obtaining an optimal fermenter configuration for engineering design and specifications. The technique employed, attainable regions analysis, which is based on the interpretation of the fermentation process as a geometric object by defining a region of achievability that can be attained by the fundamental processes occurring in the

While casual loop diagram emphasizes the feedbacks structures within the bioenergy system, stocks and flows diagram amplifies the underlying physical structures of the system. **Figure 9** presents a stock-flow diagram for bioethanol

*Leveraging Integrated Model-Based Approaches to Unlock Bioenergy Potentials in Enhancing…*

between the aforementioned factors and bioethanol production.

capacity of bioethanol plants.

*DOI: http://dx.doi.org/10.5772/intechopen.91978*

*Stock flow diagram for bioethanol production.*

production.

**Figure 9.**

**4.3 AR construction**

**105**

From **Figure 8**, interesting observations can be made considering the reinforcement loop (R) and the balancing loop (B).


*Leveraging Integrated Model-Based Approaches to Unlock Bioenergy Potentials in Enhancing… DOI: http://dx.doi.org/10.5772/intechopen.91978*

**Figure 9.** *Stock flow diagram for bioethanol production.*

investment into a green economy. This has a direct positive influence on the capacity of bioethanol plants.

While casual loop diagram emphasizes the feedbacks structures within the bioenergy system, stocks and flows diagram amplifies the underlying physical structures of the system. **Figure 9** presents a stock-flow diagram for bioethanol production.

Stocks for this case study include water, bioethanol, feedstock, atmosphere CO2, populations, workforce, and GDP. These characterize the state of the bioenergy system and generate useful information for policy development. For example, the availability of feedstock for bioethanol has influenced the flow of CO2 uptake, feedstock production rate, and bioethanol demand. Simulation of these factors was conducted overtime period of 100 months. This indicated a strong correlation between the aforementioned factors and bioethanol production.

Summarily, a hybrid system that works with the national grid is most preferable. This is because such a system will: (1) reduce environmental impact, (2) reduce pressure of land space for feedstock plantation and bioethanol plants, (3) ensure available water for human consumption and (4) most importantly ensure that there is a balance in the quantity of feedstock converted to fuel and consumed as food by the population. Readers need to note that not all the elements of the system were captured, rather key elements that significantly affect the behavior of the system.

### **4.3 AR construction**

Once the optimal implementation strategy had been achieved, the next step is obtaining an optimal fermenter configuration for engineering design and specifications. The technique employed, attainable regions analysis, which is based on the interpretation of the fermentation process as a geometric object by defining a region of achievability that can be attained by the fundamental processes occurring in the

employment creation, cost of feedstock, and land space. The causal loop diagrams (CLDs) presented in **Figure 8** are flexible and useful tools for diagramming the

From **Figure 8**, interesting observations can be made considering the reinforce-

1.An increase in population leads to a greater demand for transport (private, commercial, or public), increasing in fuel demand (bioethanol demand) (R1). This will indirectly lead to an increase in fossil fuel and bioethanol demand and consumption. This has the propensity to lead to a shortage in bioethanol, which leads to an increase in prices, but an expansion of bioethanol capacity will lead to an increase in bioethanol production, which in turn will lead to a decrease in bioethanol shortage and price as well. Similarly, an increase in fossil fuel demand will lead to shortage due to production and imports. This can lead to an increase in price and the loop is closed by a decrease in demand

2.An increase in bioethanol production leads to an increase in feedstock, which leads to an increase in the land space required (loop (R3)). More importantly, it also leads to the creation of employment. With greater land space being used for feedstock production, price of feedstock will reduce. It is interesting also to notice how an increase in advance method of feedstock production can lead to

the use of fewer resources, which directly reduces price of feedstock.

3.An increase in biofuel production leads to an increase in employment and GDP, which again increases the transport need, fuel demand, and biofuels demand and consumption. This loop reverts to increase biofuel production (loop R6). Moreover, an increase in GDP leads to capital available for

feedback structure of systems in any domain [16].

ment loop (R) and the balancing loop (B).

*Casual loop diagram (CLD) for bioethanol production.*

*Green Energy and Environment*

**Figure 8.**

**104**

due to high prices (loop B4).

### *Green Energy and Environment*

fermenter: mixing and bioreaction. The approach captures all possible bioreactor structures and finds the bounds on the performance of the system.

Eqs. (12)–(14) describes the kinetic models that characterize the fermentation of cassava supplemented by malt using *Saccharomyces carlsbergensis*. Cassava extract was selected due to its relative abundance compared to other crops within the region under study. Also, the monod model adopted to capture the substrate, limiting bioreaction taking place, incorporates two-dimensional substrate-product inhibition patterns.

$$\frac{d\mathbf{X}}{dt} = \mathbf{r}\_{\mathbf{X}} = \mu \mathbf{X} \tag{11}$$

$$\frac{d\mathbf{P}}{dt} = r\_P = \mathbf{Y}\_{\text{RX}} \mu \mathbf{X} \tag{12}$$

$$\frac{d\mathbf{S}}{dt} = \mathbf{r}\_{\mathbf{S}} = -\frac{\mathbf{1}}{\mathbf{Y}\_{\mathbf{X}\mathbf{S}}} r\_{\mathbf{X}} - \frac{\mathbf{1}}{\mathbf{Y}\_{\mathbf{P}\mathbf{X}}} r\_{\mathbf{P}} - \mathbf{M}\_{\mathbf{s}} \mathbf{X} \tag{13}$$

$$
\mu(\mathcal{S}, P) = \left(\mathbf{1} - \frac{\mathcal{S}}{K\_{\rm is}}\right) \frac{\mu\_{\rm max} \mathcal{S}}{K\_{\rm ox} + \mathcal{S}} \left(\mathbf{1} - \frac{P}{K\_{\rm iP}}\right) \tag{14}
$$

Eq. (15) is substituted into the dynamic relations (Eqs. (12)–(14)).

Before constructing the AR, it is expedient to determine the dimension in which the AR will reside. The dimensions of the AR depend on the number of independent reactions taking place. From Eq. (5), only one independent reaction involving three components (X–S–P) is taking place; hence the AR constructed must reside in a one-dimensional subspace of ℝ<sup>3</sup> for all achievable set of points.

$$\mathcal{S}\_1 + \mathcal{S}\_2 + \dots \ + \mathcal{S}\_n \stackrel{X}{\rightarrow} P\_1 + P\_2 + \dots \ + P\_m \tag{15}$$

1.762 (g/g hrs), which provides a geometric representation of all possible yields that can be achieved by the aforementioned reactor structures. It can also be inferred from the figure that using a fermenter structure (A CSTR followed by a PFR with a bypass) as oppose to a single fermenter reduces the overall residence time of the fermentation process. More interestingly, yields within the region of X, which were

*Two-dimensional candidate attainable regions for Cassava extract using two-dimensional sudden stop substrate*

*Leveraging Integrated Model-Based Approaches to Unlock Bioenergy Potentials in Enhancing…*

*DOI: http://dx.doi.org/10.5772/intechopen.91978*

Once the candidate AR has been constructed for a given kinetic and feed concentration, the limits of achievability of the system are defined. The candidate AR generated can, therefore, be used to answer design questions and determine performance targets by developing appropriate objective functions, which can be overlaid as contours on the AR boundaries. For illustration, an economic index such Payback period, which is key to investors, is considered. Economic models were then developed, incorporating the dimension of the AR and overlaid as contours on

**Figure 11** illustrates the payback periods overlaid on the Candidate AR to obtain optimal operating points and corresponding reactor structures by identifying the

Interestingly, two major observations can be made from **Figure 10**: (1) The range of payback periods considered intersect the AR at many points in the region, indicating that there are multiple operating points (multiple optima) for this system. Therefore the actual operating points to be selected vehemently depend on other auxiliary factors such as the investor's available capital. (2) Shorter PBP are achievable for higher yields at lower reactor volumes. This is interesting because an investment that involves smaller reactor volumes (lower investment cost) and higher operating yields (higher annual benefits) should require a shorter to recover investment. (3) Another interesting observation is that, as the payback period increase, the influence of running cost (reactor volume) on the PBP decreases.

initially not achievable, are now achievable by using a fermenter structure.

points of intersection of the objective function on the AR boundary.

the candidate AR.

**107**

**Figure 10.**

*and product inhibition patterns.*

Since the dimension of the AR is one, we, therefore, need to select a variable which contains the effect of all three states for which case the bioethanol yield (*yp*) has been selected. To enable graphical visualization of the AR, another variable, residence time (*τp*) will be added to the system such that it can be plotted in a two-dimensional space. A major consideration when selecting variables for plotting attainable regions is that the variables must follow the linear mixing law. It has been reported in literature by Mings et al. and Abunde et al., how the residence time and yield follow the linear mixing law [8, 20, 22]. This is represented by Eqs. (17) and (18).

$$
\pi^\* = \lambda \pi\_1 + (1 - \lambda)\pi\_2 \tag{16}
$$

$$
\lambda \mathbf{y}\_E^\* = \lambda \mathbf{y}\_E^1 + (\mathbf{1} - \lambda) \mathbf{y}\_E^2 \tag{17}
$$

Where *τ*<sup>1</sup> and *τ*<sup>2</sup> are the residence time in two reactors and *τ* <sup>∗</sup> is the residence time upon mixing. *y*<sup>1</sup> *<sup>E</sup>* and *y*<sup>2</sup> *<sup>E</sup>* are the yields of reactor 1 and reactor 2, respectively, and *y* <sup>∗</sup> *<sup>E</sup>* is the yield upon mixing.

With the kinetic model and initial conditions now known, we begin constructing the AR by generating the PFR trajectory and then the CSTR locus, then generating PFR trajectories using the CSTR locus, as illustrated in **Figure 10**.

From **Figure 10**, the boundary of the candidate AR can be defined by two main reactor configurations: (1) A CSTR followed by a PFR and (2) A CSTR followed by a PFR with a bypass from the feed to the effluent stream. This implies that all the ethanol yield contained within the defined region can be achieved by the above reactor types, with differences coming at the level of the residence time. Furthermore, the operating limits of the system (defined by the area of the convex hull) are *Leveraging Integrated Model-Based Approaches to Unlock Bioenergy Potentials in Enhancing… DOI: http://dx.doi.org/10.5772/intechopen.91978*

**Figure 10.**

fermenter: mixing and bioreaction. The approach captures all possible bioreactor

Eqs. (12)–(14) describes the kinetic models that characterize the fermentation of cassava supplemented by malt using *Saccharomyces carlsbergensis*. Cassava extract was selected due to its relative abundance compared to other crops within the region under study. Also, the monod model adopted to capture the substrate, limiting bioreaction taking place, incorporates two-dimensional substrate-product

dt <sup>¼</sup> rX <sup>¼</sup> <sup>μ</sup><sup>X</sup> (11)

dt <sup>¼</sup> *rP* <sup>¼</sup> YPXμ<sup>X</sup> (12)

*KiP* 

*<sup>X</sup> <sup>P</sup>*<sup>1</sup> <sup>þ</sup> *<sup>P</sup>*<sup>2</sup> <sup>þ</sup> … <sup>þ</sup> *Pm* (15)

*<sup>τ</sup>* <sup>∗</sup> <sup>¼</sup> *λτ*<sup>1</sup> <sup>þ</sup> ð Þ <sup>1</sup> � *<sup>λ</sup> <sup>τ</sup>*<sup>2</sup> (16)

*<sup>E</sup>* are the yields of reactor 1 and reactor 2, respectively,

*<sup>E</sup>* (17)

*rP* � MsX (13)

(14)

structures and finds the bounds on the performance of the system.

dX

YXS

*Kis μmaxS*

Eq. (15) is substituted into the dynamic relations (Eqs. (12)–(14)).

*rX* � <sup>1</sup> YPX

Before constructing the AR, it is expedient to determine the dimension in which the AR will reside. The dimensions of the AR depend on the number of independent reactions taking place. From Eq. (5), only one independent reaction involving three components (X–S–P) is taking place; hence the AR constructed must reside in a

Since the dimension of the AR is one, we, therefore, need to select a variable which contains the effect of all three states for which case the bioethanol yield (*yp*) has been selected. To enable graphical visualization of the AR, another variable, residence time (*τp*) will be added to the system such that it can be plotted in a two-dimensional space. A major consideration when selecting variables for plotting attainable regions is that the variables must follow the linear mixing law. It has been reported in literature by Mings et al. and Abunde et al., how the residence time and yield follow the linear

*<sup>E</sup>* <sup>þ</sup> ð Þ <sup>1</sup> � *<sup>λ</sup> <sup>y</sup>*<sup>2</sup>

Where *τ*<sup>1</sup> and *τ*<sup>2</sup> are the residence time in two reactors and *τ* <sup>∗</sup> is the residence

With the kinetic model and initial conditions now known, we begin constructing the AR by generating the PFR trajectory and then the CSTR locus, then generating

From **Figure 10**, the boundary of the candidate AR can be defined by two main reactor configurations: (1) A CSTR followed by a PFR and (2) A CSTR followed by a PFR with a bypass from the feed to the effluent stream. This implies that all the ethanol yield contained within the defined region can be achieved by the above reactor types, with differences coming at the level of the residence time. Furthermore, the operating limits of the system (defined by the area of the convex hull) are

*Ksx* <sup>þ</sup> *<sup>S</sup>* <sup>1</sup> � *<sup>P</sup>*

dP

dt <sup>¼</sup> rS ¼ � <sup>1</sup>

*<sup>μ</sup>*ð Þ¼ *<sup>S</sup>*, *<sup>P</sup>* <sup>1</sup> � *<sup>S</sup>*

one-dimensional subspace of ℝ<sup>3</sup> for all achievable set of points.

*S*<sup>1</sup> þ *S*<sup>2</sup> þ … þ *Sn* !

mixing law [8, 20, 22]. This is represented by Eqs. (17) and (18).

*y* ∗ *<sup>E</sup>* <sup>¼</sup> *<sup>λ</sup>y*<sup>1</sup>

PFR trajectories using the CSTR locus, as illustrated in **Figure 10**.

*<sup>E</sup>* and *y*<sup>2</sup>

*<sup>E</sup>* is the yield upon mixing.

dS

inhibition patterns.

*Green Energy and Environment*

time upon mixing. *y*<sup>1</sup>

and *y* <sup>∗</sup>

**106**

*Two-dimensional candidate attainable regions for Cassava extract using two-dimensional sudden stop substrate and product inhibition patterns.*

1.762 (g/g hrs), which provides a geometric representation of all possible yields that can be achieved by the aforementioned reactor structures. It can also be inferred from the figure that using a fermenter structure (A CSTR followed by a PFR with a bypass) as oppose to a single fermenter reduces the overall residence time of the fermentation process. More interestingly, yields within the region of X, which were initially not achievable, are now achievable by using a fermenter structure.

Once the candidate AR has been constructed for a given kinetic and feed concentration, the limits of achievability of the system are defined. The candidate AR generated can, therefore, be used to answer design questions and determine performance targets by developing appropriate objective functions, which can be overlaid as contours on the AR boundaries. For illustration, an economic index such Payback period, which is key to investors, is considered. Economic models were then developed, incorporating the dimension of the AR and overlaid as contours on the candidate AR.

**Figure 11** illustrates the payback periods overlaid on the Candidate AR to obtain optimal operating points and corresponding reactor structures by identifying the points of intersection of the objective function on the AR boundary.

Interestingly, two major observations can be made from **Figure 10**: (1) The range of payback periods considered intersect the AR at many points in the region, indicating that there are multiple operating points (multiple optima) for this system. Therefore the actual operating points to be selected vehemently depend on other auxiliary factors such as the investor's available capital. (2) Shorter PBP are achievable for higher yields at lower reactor volumes. This is interesting because an investment that involves smaller reactor volumes (lower investment cost) and higher operating yields (higher annual benefits) should require a shorter to recover investment. (3) Another interesting observation is that, as the payback period increase, the influence of running cost (reactor volume) on the PBP decreases.

**Figure 11.**

*Different contours of payback period overlaid onto the AR for cassava extract to determine the optimal operational points ((b) is a closer zoom of (a), demonstrating how the contours of the PBP intersect the AR).*

1.Who will the product serve?

*DOI: http://dx.doi.org/10.5772/intechopen.91978*

3.What does the product do?

**5.2 The octopus diagram**

**Figure 13.**

**Figure 14.**

**109**

elements of the technology.

2.What does the product interact with?

**Figure 14** presents an Octopus diagram that comprises the product in question to be designed and the different components of its external medium. The figure further describes the elements associated with the bioreactor and its environment. The above functions involved in **Figure 14** are elucidated in **Table 4**. The advantage of the octopus diagram is that it helps to visualize and validate the

*The horned beast diagram above is used to determine the needs to which the technology answers.*

*Leveraging Integrated Model-Based Approaches to Unlock Bioenergy Potentials in Enhancing…*

**Figure 15** shows a FAST diagram that presents the technological solutions which

permit the satisfaction of the principal and constraint functions.

*The octopus diagram showing the relationship between the bioenergy system and its environment.*

**Figure 12.**

*Optimal continuous fermenter structure and its corresponding batch fermenters.*

This is observed from **Figure 11a** by the closeness of the contours from 1.5, 2, and 2.5 years. Therefore, it is sensible to construct a fermenter volume that is larger for payback periods between 1.5 and 2.5 years, since the cost influence is minimal, and that reactor volume can be used to achieve all desired payback periods.

In summary, the AR theory presents a geometric technique that can be used to identify optimal process configuration. Therefore **Figure 12** illustrates the optimal continuous reactor and its corresponding batch fermenter for bioethanol production.

### **5. FAST analysis**

### **5.1 Functional analysis of need**

The Horned Beast diagram, illustrated in **Figure 13**, provides a visual tool that seeks to answer three fundamental questions:

*Leveraging Integrated Model-Based Approaches to Unlock Bioenergy Potentials in Enhancing… DOI: http://dx.doi.org/10.5772/intechopen.91978*

### **Figure 13.**

*The horned beast diagram above is used to determine the needs to which the technology answers.*


### **5.2 The octopus diagram**

**Figure 14** presents an Octopus diagram that comprises the product in question to be designed and the different components of its external medium. The figure further describes the elements associated with the bioreactor and its environment.

The above functions involved in **Figure 14** are elucidated in **Table 4**. The advantage of the octopus diagram is that it helps to visualize and validate the elements of the technology.

**Figure 15** shows a FAST diagram that presents the technological solutions which permit the satisfaction of the principal and constraint functions.

*The octopus diagram showing the relationship between the bioenergy system and its environment.*

This is observed from **Figure 11a** by the closeness of the contours from 1.5, 2, and 2.5 years. Therefore, it is sensible to construct a fermenter volume that is larger for payback periods between 1.5 and 2.5 years, since the cost influence is minimal, and

*Different contours of payback period overlaid onto the AR for cassava extract to determine the optimal operational points ((b) is a closer zoom of (a), demonstrating how the contours of the PBP intersect the AR).*

In summary, the AR theory presents a geometric technique that can be used to identify optimal process configuration. Therefore **Figure 12** illustrates the optimal continuous reactor and its corresponding batch fermenter for bioethanol production.

The Horned Beast diagram, illustrated in **Figure 13**, provides a visual tool that

that reactor volume can be used to achieve all desired payback periods.

*Optimal continuous fermenter structure and its corresponding batch fermenters.*

**5. FAST analysis**

**108**

**Figure 11.**

*Green Energy and Environment*

**Figure 12.**

**5.1 Functional analysis of need**

seeks to answer three fundamental questions:


Once the FAST diagram is constructed, the next step is to develop a Value Analysis Matrix that examines the component costs of the equipment in relation to

*Leveraging Integrated Model-Based Approaches to Unlock Bioenergy Potentials in Enhancing…*

Returning to the challenges posed at the beginning of this chapter, it is now possible to state that (1) MCDM provides an appropriate technique for selecting and assessing an optimal bioenergy technology (bioethanol fermenter) that seeks to address the social, economic, technical and environmental factors for sustainable development. (2) A hybrid energy system that comprises of the bioethanol plant along with the national grid proved at optimal implementation strategy as it ensured a balance in the bioenergy system. It is quite interesting to notice how system dynamics modeling presents an efficient tool to model and simulate energy systems and their interaction with other systems, as demonstrated in this chapter. The tool was used to investigate the economic, environmental and social impact of bioethanol production in view of respecting sustainability criteria while striking a balance between the several subsystems involved (3) the optimal fermenter structure required for the fermentation of the different extracts includes a CSTR followed by a PFR as well as a CSTR followed by a PFR with bypass from feed. And finally, a value analysis was conducted to identify the components required for the technology to meet its functions. More importantly, the methodological framework presented an exciting and thrilling route to how sustainable technologies could be successfully installed. This chapter has gone some way towards enhancing our understanding of how model-based approaches relative to conventional implementation strategies ensure sustainable development. A model-based approach to delivering sustainable solutions is gradually becoming an exhilarating area for sustainable systems engineers. Readers should expect electrifying exploits from the authors as they seek to leverage on model-based techniques, Artificial Intelligence (AI), and digital technology to unlock Africa's potential in the food-water-energy-health nexus.

Our team expresses gratitude to the following institutions; The Brew-Hammond Energy Centre, KNUST Ghana, The Water and Environmental Engineering Group, NTNU Ålesund and the Abunde Sustainable Engineering Group (AbundeSEG) for

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in

the value perceived by the customer. Value Analysis Matrix, also known as Functional-Cost Matrix, was derived from the Quality Functional Deployment Methodology. The strength of this technique is that it associates the functions of a product back to the customer's needs. It can also develop mechanisms that relate to functions as either strongly, moderately, or weakly supporting the given function and can also be used to calculate each mechanism's relative weight in satisfying the designated functions. This enables management to check whether the money spent on function and component is worth it. For illustrations, the approach was not exhausted in this work. Once exhausted, management can move further into the

equipment specification, then the installation of the equipment.

*DOI: http://dx.doi.org/10.5772/intechopen.91978*

**6. Conclusion**

**Acknowledgements**

**Conflict of interest**

this paper.

**111**

its immense technical support.

### **Table 4.**

*Principal functions together with the constraints.*

**Figure 15.** *FAST diagram showing functions and their corresponding technical solutions.*

*Leveraging Integrated Model-Based Approaches to Unlock Bioenergy Potentials in Enhancing… DOI: http://dx.doi.org/10.5772/intechopen.91978*

Once the FAST diagram is constructed, the next step is to develop a Value Analysis Matrix that examines the component costs of the equipment in relation to the value perceived by the customer. Value Analysis Matrix, also known as Functional-Cost Matrix, was derived from the Quality Functional Deployment Methodology. The strength of this technique is that it associates the functions of a product back to the customer's needs. It can also develop mechanisms that relate to functions as either strongly, moderately, or weakly supporting the given function and can also be used to calculate each mechanism's relative weight in satisfying the designated functions. This enables management to check whether the money spent on function and component is worth it. For illustrations, the approach was not exhausted in this work. Once exhausted, management can move further into the equipment specification, then the installation of the equipment.

### **6. Conclusion**

**Principal function**

*Green Energy and Environment*

**Constraint functions**

*Principal functions together with the constraints.*

**Table 4.**

**Figure 15.**

**110**

*FAST diagram showing functions and their corresponding technical solutions.*

PF Conversion of starch to bioethanol in order to generate energy

CF3 The biofuel produced should meet international standards for fuels

CF2 The technology should use a renewable energy source

CF4 The technology should have a less environmental impact CF5 Material of construction should be available and less expensive CF6 Maintenance should be simple and easily carried out routinely

CF1 The biofuel should meet all required safety standards and minimize losses from accident

Returning to the challenges posed at the beginning of this chapter, it is now possible to state that (1) MCDM provides an appropriate technique for selecting and assessing an optimal bioenergy technology (bioethanol fermenter) that seeks to address the social, economic, technical and environmental factors for sustainable development. (2) A hybrid energy system that comprises of the bioethanol plant along with the national grid proved at optimal implementation strategy as it ensured a balance in the bioenergy system. It is quite interesting to notice how system dynamics modeling presents an efficient tool to model and simulate energy systems and their interaction with other systems, as demonstrated in this chapter. The tool was used to investigate the economic, environmental and social impact of bioethanol production in view of respecting sustainability criteria while striking a balance between the several subsystems involved (3) the optimal fermenter structure required for the fermentation of the different extracts includes a CSTR followed by a PFR as well as a CSTR followed by a PFR with bypass from feed. And finally, a value analysis was conducted to identify the components required for the technology to meet its functions. More importantly, the methodological framework presented an exciting and thrilling route to how sustainable technologies could be successfully installed. This chapter has gone some way towards enhancing our understanding of how model-based approaches relative to conventional implementation strategies ensure sustainable development. A model-based approach to delivering sustainable solutions is gradually becoming an exhilarating area for sustainable systems engineers. Readers should expect electrifying exploits from the authors as they seek to leverage on model-based techniques, Artificial Intelligence (AI), and digital technology to unlock Africa's potential in the food-water-energy-health nexus.

## **Acknowledgements**

Our team expresses gratitude to the following institutions; The Brew-Hammond Energy Centre, KNUST Ghana, The Water and Environmental Engineering Group, NTNU Ålesund and the Abunde Sustainable Engineering Group (AbundeSEG) for its immense technical support.

### **Conflict of interest**

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## **Funding**

This work was supported by EnPe - NORAD under the project Upgrading Education and Research Capacity in Renewable Energy Technologies (UPERC-RET).

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[13] Wang JJ, Jing YY, Zhang CF, Zhao JH. Review on multi-criteria decision analysis aid in sustainable energy decision-making. Renewable and Sustainable Energy Reviews. 2009;

[14] Ibáñez-Forés V, Bovea MD, Pérez-Belis V. A holistic review of applied methodologies for assessing and selecting the optimal technological alternative from a sustainability perspective. Journal of Cleaner Production. 2014;**70**:259-281

[15] Mardani A, Jusoh A, Nor KMD, Khalifah Z, Zakwan N, Valipour A. Multiple criteria decision-making techniques and their applications—A review of the literature from 2000 to 2014. Economic Research-Ekonomska Istraživanja. 2015;**28**(1):516-571

[16] Sterman J. Business dynamics: Systems thinking and modeling for a complex world. Boston: Irwin/McGraw-

Tedeschi LO, Atzori AS. System

[17] Turner BL, Menendez HM, Gates R,

dynamics modeling for agricultural and natural resource management issues: Review of some past cases and

Hill; 2000

[11] Snegirev DA, Valiev RT, Eroshenko SA, Khalyasmaa AI. Functional assessment system of solar power plant energy production. In: Proc. 8th Int. Conf. Energy Environ. Energy Saved Today is Asset Futur. CIEM 2017.

2017. pp. 349-353

*Leveraging Integrated Model-Based Approaches to Unlock Bioenergy Potentials in Enhancing…*

**13**(9):2263-2278

## **Nomenclature**


## **Author details**

Fabrice Abunde Neba1,2\*, Prince Agyemang1 , Yahaya D. Ndam<sup>1</sup> , Endene Emmanuel<sup>1</sup> , Eyong G. Ndip1 and Razak Seidu<sup>2</sup>

1 Abunde Sustainable Engineering Group (AbundeSEG), Buea, Cameroon

2 Institute of Marine Operations and Civil Engineering, Norwegian University of Science and Technology, Ålesund, Norway

\*Address all correspondence to: fabrice.a.neba@ntnu.no

© 2020 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.

*Leveraging Integrated Model-Based Approaches to Unlock Bioenergy Potentials in Enhancing… DOI: http://dx.doi.org/10.5772/intechopen.91978*

## **References**

**Funding**

(UPERC-RET).

*Green Energy and Environment*

**Nomenclature**

**Author details**

Endene Emmanuel<sup>1</sup>

**112**

Fabrice Abunde Neba1,2\*, Prince Agyemang1

Science and Technology, Ålesund, Norway

provided the original work is properly cited.

\*Address all correspondence to: fabrice.a.neba@ntnu.no

, Eyong G. Ndip1 and Razak Seidu<sup>2</sup>

1 Abunde Sustainable Engineering Group (AbundeSEG), Buea, Cameroon

2 Institute of Marine Operations and Civil Engineering, Norwegian University of

© 2020 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,

, Yahaya D. Ndam<sup>1</sup>

,

*<sup>m</sup><sup>n</sup>* normalized matrix *λmax* maximum Eigen value n number of attributes A pairwise comparison matrix

*τ*<sup>1</sup> residence time

W the estimate of the decision-makers weight

*rij* 

This work was supported by EnPe - NORAD under the project Upgrading

Education and Research Capacity in Renewable Energy Technologies

[1] Chatterjee R, Gajjela S, Thirumdasu RK. Recycling of organic wastes for sustainable soil health and crop growth. International Journal of Waste Resources. 2017;**07**(03). DOI: 10.4172/2252-5211.1000296

[2] Smith A, Brown K, Ogilvie S, Rushton K, Bates J. Waste Management Options and Climate Change: Final Report to the European Commission. DG Environment. 2001

[3] Gingerich DB, Mauter MS. Air Emission Reduction Benefits of Biogas Electricity Generation at Municipal Wastewater Treatment Plants. 2018

[4] Koch K, Helmreich B, Drewes JE. Co-digestion of food waste in municipal wastewater treatment plants: Effect of different mixtures on methane yield and hydrolysis rate constant. Applied Energy. 2015;**137**:250-255

[5] PNUMA. UN Enviroment Annual Report, Empowering People to Protect the Planet. 2016, p. 20

[6] Chel A, Kaushik G. Renewable energy technologies for sustainable development of energy efficient building. Alexandria Engineering Journal. 2018;**57**(2):655-669

[7] Mustafa A et al. Renewable energy technologies and charaterization. **1**(4) (TR-109496):102-116

[8] Ming D, Glasser D, Hildebrandt D, Glasser B, Metzer M. Attainable Region Theory: An Introduction to Choosing an Optimal Reactor. Hoboken, New Jersey: John Wiley & Sons, Inc; 2016

[9] Ming D, Glasser D, Hildebrandt D. Application of attainable region theory to batch reactors. Chemical Engineering Science. 2013;**99**:203-214

[10] Hildebrandt D. Synthesis of chemical reactor networks. 1995

[11] Snegirev DA, Valiev RT, Eroshenko SA, Khalyasmaa AI. Functional assessment system of solar power plant energy production. In: Proc. 8th Int. Conf. Energy Environ. Energy Saved Today is Asset Futur. CIEM 2017. 2017. pp. 349-353

[12] Wüstenhagen R, Wolsink M, Bürer MJ. Social acceptance of renewable energy innovation: An introduction to the concept. Energy Policy. 2007;**35**(5):2683-2691

[13] Wang JJ, Jing YY, Zhang CF, Zhao JH. Review on multi-criteria decision analysis aid in sustainable energy decision-making. Renewable and Sustainable Energy Reviews. 2009; **13**(9):2263-2278

[14] Ibáñez-Forés V, Bovea MD, Pérez-Belis V. A holistic review of applied methodologies for assessing and selecting the optimal technological alternative from a sustainability perspective. Journal of Cleaner Production. 2014;**70**:259-281

[15] Mardani A, Jusoh A, Nor KMD, Khalifah Z, Zakwan N, Valipour A. Multiple criteria decision-making techniques and their applications—A review of the literature from 2000 to 2014. Economic Research-Ekonomska Istraživanja. 2015;**28**(1):516-571

[16] Sterman J. Business dynamics: Systems thinking and modeling for a complex world. Boston: Irwin/McGraw-Hill; 2000

[17] Turner BL, Menendez HM, Gates R, Tedeschi LO, Atzori AS. System dynamics modeling for agricultural and natural resource management issues: Review of some past cases and

forecasting future roles. Resources. 2016;**5**(4)

[18] Asiedu N, Hildebrandt D, Glasser D. Experimental simulation of threedimensional attainable region for the synthesis of exothermic reversible reaction: Ethyl acetate synthesis case study. Industrial & Engineering Chemistry Research. 2015;**54**(10): 2619-2626

[19] Abunde Neba F, Jiokap Nono Y. Modeling and simulated design: A novel model and software of a solar-biomass hybrid dryer. Computers & Chemical Engineering. 2017;**104**:128-140

[20] Neba FA, Asiedu NY, Addo A, Seidu R. Attainable regions and fuzzy multi-criteria decisions: Modeling a novel configuration of methane bioreactor using experimental limits of operation. Bioresource Technology. 2019:122273

[21] Neba FA, Tornyeviadzi HM, Østerhus SW, Seidu R. Self-optimizing attainable regions of the anaerobic treatment process: Modeling performance targets under kinetic uncertainty. Water Research. 2020: 115377

[22] Abunde Neba F, Asiedu NY, Addo A, Morken J, Østerhus SW, Seidu R. Simulation of two-dimensional attainable regions and its application to model digester structures for maximum stability of anaerobic treatment process. Water Research. 2019;**163**:114891

[23] Asiedu NY, Hildebrandt D, Glasser D. Batch distillation targets for minimum energy consumption. Industrial & Engineering Chemistry Research. 2014;**53**(7):2751-2757. DOI: 1021/ie402044y

[24] Asiedu N, Hildebrandt D, Glasser D. Experimental simulation of a twodimensional attainable region and its application in the optimization of

production rate and process time of an adiabatic batch reactor. Industrial & Engineering Chemistry Research. 2014; **53**(34):13308-13319

[25] Metzger MJ, Glasser D, Hausberger B, Hildebrandt D, Glasser BJ. Use of the attainable region analysis to optimize particle breakage in a ball mill. Chemical Engineering Science. 2009;**64**(17):3766-3777

[26] Mota P, Campos AR, Neves-Silva R. First look at MCDM: Choosing a decision method. Adv. Smart Syst. Res. 2013;**3**(2):25-30

[27] Wątróbski J, Jankowski J, Ziemba P, Karczmarczyk A, Zioło M. Generalised framework for multi-criteria method selection. Omega (United Kingdom). 2019;**86**:107-124

[28] Akash BA, Mamlook R, Mohsen MS. Multi-criteria selection of electric power plants using analytical hierarchy process. Electric Power Systems Research. 1999;**52**(1):29-35

[29] Jaber JO, Jaber QM, Sawalha SA, Mohsen MS. Evaluation of conventional and renewable energy sources for space heating in the household sector. 2008; **12**:278-289

[30] Saaty RW. The analytic hierarchy process-what it is and how it is used. Mathematical Modelling. 1987;**9**(3–5): 161-176

**115**

**Chapter 6**

**Abstract**

Energy Potential of Biomass

*Andrea Majlingová, Martin Lieskovský, Maroš Sedliak* 

Renewable energy has provided many potential benefits, including a reduction in greenhouse gas (GHG) emissions, the diversification of energy supplies, and a reduced dependency on fossil fuel markets (oil and gas in particular). The growth of renewable energy sources (RES) may also have the potential to stimulate employment in the European Union (EU), through the creation of jobs in new green technologies. In this chapter, first, we introduce the information on renewable energy sources, their statistics, and legislation background in Slovakia. In more detail, we further introduce the information on forest and agricultural biomass as a renewable energy source. In the experimental part, we introduce two case studies—the assessment of the potential stock of woody biomass and the determination of energetic properties of woody biomass, i.e., selected fastgrowing tree species based on the implementation of laboratory fire tests and

**Keywords:** woody biomass, energy potential, stock, renewable energy source

**1. General overview on renewable energy production in EU and in** 

In general, renewable energy sources (RES) include wind power, solar power (thermal, photovoltaic, and concentrated), hydropower, tidal power, geothermal energy, ambient heat captured by heat pumps, biofuels, and the renewable part

Here we introduce the overview of statistics on renewable energy sources in the

The information presented here is based on data compiled in accordance with accounting rules set down in the Directive 2009/28/EC [2] on the promotion of the use of energy from renewable sources and calculated on the basis of energy statistics covered by Regulation 1099/2008 on energy statistics, most recently amended in November 2017 by Commission Regulation 2017/2010. The most recent data available on the share of energy from renewable sources are for the reference

The primary production of renewable energy within the EU-28 in 2017 was 226.5

million tons of oil equivalent (toe). The quantity of renewable energy produced within the EU-28 increased overall by 64.0% between 2007 and 2017, equivalent to

Sources in Slovakia

*and Marián Slamka*

calorimetric analyses.

EU published by Eurostat [1].

an average increase of 5.1% per year [1].

**Slovakia**

of waste.

year 2017.

## **Chapter 6**

forecasting future roles. Resources.

*Green Energy and Environment*

[18] Asiedu N, Hildebrandt D, Glasser D. Experimental simulation of threedimensional attainable region for the synthesis of exothermic reversible reaction: Ethyl acetate synthesis case study. Industrial & Engineering Chemistry Research. 2015;**54**(10):

production rate and process time of an adiabatic batch reactor. Industrial & Engineering Chemistry Research. 2014;

Glasser BJ. Use of the attainable region analysis to optimize particle breakage in a ball mill. Chemical Engineering Science. 2009;**64**(17):3766-3777

[26] Mota P, Campos AR, Neves-Silva R.

[27] Wątróbski J, Jankowski J, Ziemba P, Karczmarczyk A, Zioło M. Generalised framework for multi-criteria method selection. Omega (United Kingdom).

[28] Akash BA, Mamlook R, Mohsen MS. Multi-criteria selection of electric power

plants using analytical hierarchy process. Electric Power Systems Research. 1999;**52**(1):29-35

[29] Jaber JO, Jaber QM, Sawalha SA, Mohsen MS. Evaluation of conventional and renewable energy sources for space heating in the household sector. 2008;

[30] Saaty RW. The analytic hierarchy process-what it is and how it is used. Mathematical Modelling. 1987;**9**(3–5):

First look at MCDM: Choosing a decision method. Adv. Smart Syst. Res.

**53**(34):13308-13319

2013;**3**(2):25-30

2019;**86**:107-124

**12**:278-289

161-176

[25] Metzger MJ, Glasser D, Hausberger B, Hildebrandt D,

[19] Abunde Neba F, Jiokap Nono Y. Modeling and simulated design: A novel model and software of a solar-biomass hybrid dryer. Computers & Chemical Engineering. 2017;**104**:128-140

[20] Neba FA, Asiedu NY, Addo A, Seidu R. Attainable regions and fuzzy multi-criteria decisions: Modeling a novel configuration of methane bioreactor using experimental limits of operation. Bioresource Technology.

[21] Neba FA, Tornyeviadzi HM, Østerhus SW, Seidu R. Self-optimizing attainable regions of the anaerobic treatment process: Modeling performance targets under kinetic uncertainty. Water Research. 2020:

[22] Abunde Neba F, Asiedu NY, Addo A, Morken J, Østerhus SW, Seidu R. Simulation of two-dimensional attainable regions and its application to model digester structures for maximum stability of anaerobic treatment process. Water Research. 2019;**163**:114891

[23] Asiedu NY, Hildebrandt D, Glasser D. Batch distillation targets for minimum energy consumption. Industrial & Engineering Chemistry Research. 2014;**53**(7):2751-2757. DOI:

[24] Asiedu N, Hildebrandt D, Glasser D. Experimental simulation of a twodimensional attainable region and its application in the optimization of

1021/ie402044y

**114**

2016;**5**(4)

2619-2626

2019:122273

115377

## Energy Potential of Biomass Sources in Slovakia

*Andrea Majlingová, Martin Lieskovský, Maroš Sedliak and Marián Slamka*

## **Abstract**

Renewable energy has provided many potential benefits, including a reduction in greenhouse gas (GHG) emissions, the diversification of energy supplies, and a reduced dependency on fossil fuel markets (oil and gas in particular). The growth of renewable energy sources (RES) may also have the potential to stimulate employment in the European Union (EU), through the creation of jobs in new green technologies. In this chapter, first, we introduce the information on renewable energy sources, their statistics, and legislation background in Slovakia. In more detail, we further introduce the information on forest and agricultural biomass as a renewable energy source. In the experimental part, we introduce two case studies—the assessment of the potential stock of woody biomass and the determination of energetic properties of woody biomass, i.e., selected fastgrowing tree species based on the implementation of laboratory fire tests and calorimetric analyses.

**Keywords:** woody biomass, energy potential, stock, renewable energy source

## **1. General overview on renewable energy production in EU and in Slovakia**

In general, renewable energy sources (RES) include wind power, solar power (thermal, photovoltaic, and concentrated), hydropower, tidal power, geothermal energy, ambient heat captured by heat pumps, biofuels, and the renewable part of waste.

Here we introduce the overview of statistics on renewable energy sources in the EU published by Eurostat [1].

The information presented here is based on data compiled in accordance with accounting rules set down in the Directive 2009/28/EC [2] on the promotion of the use of energy from renewable sources and calculated on the basis of energy statistics covered by Regulation 1099/2008 on energy statistics, most recently amended in November 2017 by Commission Regulation 2017/2010. The most recent data available on the share of energy from renewable sources are for the reference year 2017.

The primary production of renewable energy within the EU-28 in 2017 was 226.5 million tons of oil equivalent (toe). The quantity of renewable energy produced within the EU-28 increased overall by 64.0% between 2007 and 2017, equivalent to an average increase of 5.1% per year [1].

Among renewable energies, the most important source in the EU-28 was wood and other solid biofuels, accounting for 42.0% of primary renewable production in 2017 (**Figure 1**).

Wind power was, for the first time, the second most important contributor to the renewable energy mix (13.8% of the total), followed by hydropower (11.4%). Although their levels of production remained relatively low, there was a particularly rapid expansion in the output of biogas, liquid biofuels, and solar energy, which accounted, respectively, for a 7.4, 6.7, and 6.4% share of the EU-28's renewable energy produced in 2017. Ambient heat (captured by heat pumps) and geothermal energy accounted for 5.0 and 3.0% of the total, respectively, while renewable wastes increased to reach 4.4%. There are currently very low levels of tide, wave, and ocean energy production, with these technologies principally found in France and the United Kingdom [1].

In 2018, the share of energy from renewable sources in gross final energy consumption reached 18.0% in the European Union (EU), up from 17.5% in 2017 and more than double the share in 2004 (8.5%), the first year for which the data are available.

Gross final consumption of energy is defined in the Renewable Energy Directive 2009/28/EC [2] as the energy commodities delivered for energy purposes to industry, transport, households, services (including public services), agriculture, forestry, and fisheries, including the consumption of electricity and heat by the energy branch for electricity and heat production and including losses of electricity and heat in distribution and transmission [1].

The increase in the share of renewables is essential to reach the EU climate and energy goals. The EU's target is to reach 20% of its energy from renewable sources by 2020 and at least 32% by 2030.

The European Council endorsed a 2030 Framework for Energy and Climate for the Union based on four key Union-level targets: a reduction of at least 40% in economy-wide greenhouse gas (GHG) emissions; an indicative target of improvement in energy efficiency of at least 27%, to be reviewed by 2020 with a view to increasing the level to 30%; a share of renewable energy consumed in the Union of at least 27%; and electricity interconnection of at least 15%. It specified that the target for renewable energy is binding at Union level and that it will be fulfilled through Member States' contributions guided by the need to deliver collectively the Union target [3].

**Figure 1.**

*Primary production of energy from renewable sources in EU-28 in period 1990–2017 (Source: Eurostat [1]).*

**117**

**Figure 2.**

*Eurostat [1]).*

*Energy Potential of Biomass Sources in Slovakia DOI: http://dx.doi.org/10.5772/intechopen.91847*

of the Member States [5].

from their targets [1].

meet the requirements of this Directive.

2018, compared with 8.5% in 2004.

A recast of Directive 2009/28/EC [2] of the European Parliament and of the Council has introduced a new, binding, renewable energy target for the Union for 2030 of at least 32%, including a provision for a review with a view to increasing the Union-level target by 2023. Amendments to Directive 2012/27/EU [4] of the European Parliament and of the Council have set the Union-level target for improvements in energy efficiency in 2030 to at least 32.5%, including a provision

This target is distributed between the EU Member States with national action plans designed to plot a pathway for the development of renewable energies in each

Among the 28 EU Member States, 12 Member States have already reached a share equal to or above their national 2020 binding targets: Bulgaria, Czechia, Denmark, Estonia, Greece, Croatia, Italy, Latvia, Lithuania, Cyprus, Finland, and Sweden. Four Member States are close to meet their targets (i.e., less than 1 percentage point (pp) away), nine are between 1 and 4 pp. away, while three are 4 or more pp. away

The share of renewable energy in gross final energy consumption is identified as a key indicator for measuring progress under the Europe 2020 strategy for smart, sustainable, and inclusive growth. This indicator may be considered as an estimate for the purpose of monitoring Directive 2009/28/EC [2] on the promotion of the use of energy from renewable sources—however, the statistical system in some countries for specific renewable energy technologies is not yet fully developed to

**Figure 2** shows the latest data available for the share of renewable energies in gross final energy consumption and the targets that have been set for 2020. The share of renewables in gross final energy consumption stood at 18% in the EU-28 in

This positive development has been prompted by the legally binding targets for increasing the share of energy from renewable sources enacted by Directive 2009/28/EC [2] on the promotion of the use of energy from renewable sources. The share of energy from renewable sources is divided into three different components: share in electricity, share in heating and cooling, and share in transport.

*Share of energy from renewable sources in in EU-28 in % of gross final energy consumption in 2018 (Source:* 

for a review with a view to increasing the Union-level targets.

### *Energy Potential of Biomass Sources in Slovakia DOI: http://dx.doi.org/10.5772/intechopen.91847*

*Green Energy and Environment*

2017 (**Figure 1**).

United Kingdom [1].

and heat in distribution and transmission [1].

by 2020 and at least 32% by 2030.

Union target [3].

Among renewable energies, the most important source in the EU-28 was wood and other solid biofuels, accounting for 42.0% of primary renewable production in

Wind power was, for the first time, the second most important contributor to the renewable energy mix (13.8% of the total), followed by hydropower (11.4%). Although their levels of production remained relatively low, there was a particularly rapid expansion in the output of biogas, liquid biofuels, and solar energy, which accounted, respectively, for a 7.4, 6.7, and 6.4% share of the EU-28's renewable energy produced in 2017. Ambient heat (captured by heat pumps) and geothermal energy accounted for 5.0 and 3.0% of the total, respectively, while renewable wastes increased to reach 4.4%. There are currently very low levels of tide, wave, and ocean energy production, with these technologies principally found in France and the

In 2018, the share of energy from renewable sources in gross final energy consumption reached 18.0% in the European Union (EU), up from 17.5% in 2017 and more than

Gross final consumption of energy is defined in the Renewable Energy Directive

The increase in the share of renewables is essential to reach the EU climate and energy goals. The EU's target is to reach 20% of its energy from renewable sources

The European Council endorsed a 2030 Framework for Energy and Climate for the Union based on four key Union-level targets: a reduction of at least 40% in economy-wide greenhouse gas (GHG) emissions; an indicative target of improvement in energy efficiency of at least 27%, to be reviewed by 2020 with a view to increasing the level to 30%; a share of renewable energy consumed in the Union of at least 27%; and electricity interconnection of at least 15%. It specified that the target for renewable energy is binding at Union level and that it will be fulfilled through Member States' contributions guided by the need to deliver collectively the

*Primary production of energy from renewable sources in EU-28 in period 1990–2017 (Source: Eurostat [1]).*

double the share in 2004 (8.5%), the first year for which the data are available.

2009/28/EC [2] as the energy commodities delivered for energy purposes to industry, transport, households, services (including public services), agriculture, forestry, and fisheries, including the consumption of electricity and heat by the energy branch for electricity and heat production and including losses of electricity

**116**

**Figure 1.**

A recast of Directive 2009/28/EC [2] of the European Parliament and of the Council has introduced a new, binding, renewable energy target for the Union for 2030 of at least 32%, including a provision for a review with a view to increasing the Union-level target by 2023. Amendments to Directive 2012/27/EU [4] of the European Parliament and of the Council have set the Union-level target for improvements in energy efficiency in 2030 to at least 32.5%, including a provision for a review with a view to increasing the Union-level targets.

This target is distributed between the EU Member States with national action plans designed to plot a pathway for the development of renewable energies in each of the Member States [5].

Among the 28 EU Member States, 12 Member States have already reached a share equal to or above their national 2020 binding targets: Bulgaria, Czechia, Denmark, Estonia, Greece, Croatia, Italy, Latvia, Lithuania, Cyprus, Finland, and Sweden. Four Member States are close to meet their targets (i.e., less than 1 percentage point (pp) away), nine are between 1 and 4 pp. away, while three are 4 or more pp. away from their targets [1].

The share of renewable energy in gross final energy consumption is identified as a key indicator for measuring progress under the Europe 2020 strategy for smart, sustainable, and inclusive growth. This indicator may be considered as an estimate for the purpose of monitoring Directive 2009/28/EC [2] on the promotion of the use of energy from renewable sources—however, the statistical system in some countries for specific renewable energy technologies is not yet fully developed to meet the requirements of this Directive.

**Figure 2** shows the latest data available for the share of renewable energies in gross final energy consumption and the targets that have been set for 2020. The share of renewables in gross final energy consumption stood at 18% in the EU-28 in 2018, compared with 8.5% in 2004.

This positive development has been prompted by the legally binding targets for increasing the share of energy from renewable sources enacted by Directive 2009/28/EC [2] on the promotion of the use of energy from renewable sources.

The share of energy from renewable sources is divided into three different components: share in electricity, share in heating and cooling, and share in transport.

### **Figure 2.**

*Share of energy from renewable sources in in EU-28 in % of gross final energy consumption in 2018 (Source: Eurostat [1]).*

While the EU as a whole is on course to meet its 2020 targets, some Member States will need to make additional efforts to meet their obligations as regards the two main targets: the overall share of energy from renewable sources in the gross final energy consumption and the specific share of energy from renewable sources in transport [1].

In 2017, electricity generation from renewable sources contributed more than one quarter (30.7%) to total EU-28 gross electricity consumption. Wind power was for the first time the most important source, followed closely by hydropower.

Renewable energy accounted for 19.5% of total energy used for heating and cooling in 2017. This was a significant increase from 10.4% in 2004. Increases in industrial sectors, services, and households (building sector) contributed to this growth [1].

But the Slovak Republic (SR) is moving away from its target for the share of renewable energy sources. This is set at 14% for 2020.

In 2017, however, Slovakia reached only 11.5%, while the share decreased for the second consecutive year. In 2016, it was 12%. In 2015, it was 12.9%. Slovakia returned statistically before 2014, when the share was 11.7% [1].

The share of energy from renewable sources in final energy consumption in the Slovak Republic in period 2004–2016 is shown in **Figure 3**.

The decrease in the share of renewable energy sources was caused by lower growth in the use of renewable energy sources than the growth in final energy consumption. The growth in electricity consumption and the significant increase in the use of motor fuels, which caused a dynamic increase in energy consumption, reflect the Slovak Republic's economic growth. In the long term, the Slovak Republic's priority is energy efficiency, which leads to a reduction in energy consumption and thus to savings in fossil fuels and greenhouse gas emissions.

At the same time, in 2017, the highest increase in energy consumption in Slovakia was recorded by 7% of all EU Member States. Slovak gross domestic product (GDP) increased this year by 3.2%. This means that the country is failing to separate energy consumption from economic growth and thus enhance energy efficiency.

Of all 28 EU Member States, in the share of renewable energy, the Slovak Republic ended in the ninth place backward.

### **Figure 3.**

*Share of energy from renewable sources in final energy consumption in SR in period 2004–2016 (Source: Energie Portál [6]).*

**119**

*Energy Potential of Biomass Sources in Slovakia DOI: http://dx.doi.org/10.5772/intechopen.91847*

According to the latest statistics of Eurostat [1], the Slovak Republic is the country with the highest year-on-year increase in final energy consumption—by 7%.

**Year Natural gas (GWh) Biomass (Kt) Coal (Kt) Biogas (GWh) Fuel oil (Kt)** 8514 1113 571 275 96 8141 845 577 326 128 8637 877 586 326 128

*Renewable energy sources are used in addition to electricity production also for heat production (Source: URSO* 

As energy consumption in the Slovak Republic is growing and renewable sources

In the Slovak Republic, electricity from renewable sources is promoted through a fixed feed-in tariff. Energy companies are obliged to purchase and pay for electric-

Renewable energy biomass must be given priority connection, and electricity from renewable sources must be given priority dispatch. The grid operator is

Renewable energy sources are used in addition to electricity production also for heat production. URSO [7, 8] 2019 statistics show that in 2018 the most used fuel for heat production in Slovakia was natural gas. As can be seen from the data in **Table 1**, its use increased year-on-year most significantly from all fuels to around

The total volume of heat supply from renewable energy sources in 2018 was less than 2000 GWh according to data from the URSO Annual Report [7, 8]. From the combined heat and power (CHP) came 6000 GWh. The distribution of heat supply

The support of heat from renewable energy sources mainly takes the form of

**2. Legislation governing the use of renewable energy sources in Slovakia**

In Slovakia, the primary legislation consists of Act of the National Council of the Slovak Republic No. 656/2004 Coll. Energy Act [9]. This Act defines the basic processes related to electricity and RES, as well as basic concepts, and performance

Further, we are focusing more on the legislation governing the use of renewable

obliged to extend the grid without discriminating against certain users.

are not developing, their share inevitably decreases.

*Heat supply in SR in 2018 (Source: URSO [7, 8]).*

financial support for investments in the Slovak Republic.

ity exported to the grid.

volume is visualized in **Figure 4**.

energy sources in Slovakia.

8640 GWh.

**Table 1.**

*[7, 8]).*

**Figure 4.**

*Energy Potential of Biomass Sources in Slovakia DOI: http://dx.doi.org/10.5772/intechopen.91847*


**Table 1.**

*Green Energy and Environment*

in transport [1].

hydropower.

growth [1].

While the EU as a whole is on course to meet its 2020 targets, some Member States will need to make additional efforts to meet their obligations as regards the two main targets: the overall share of energy from renewable sources in the gross final energy consumption and the specific share of energy from renewable sources

In 2017, electricity generation from renewable sources contributed more than one quarter (30.7%) to total EU-28 gross electricity consumption. Wind power was for the first time the most important source, followed closely by

Renewable energy accounted for 19.5% of total energy used for heating and cooling in 2017. This was a significant increase from 10.4% in 2004. Increases in industrial sectors, services, and households (building sector) contributed to this

But the Slovak Republic (SR) is moving away from its target for the share of

In 2017, however, Slovakia reached only 11.5%, while the share decreased for the second consecutive year. In 2016, it was 12%. In 2015, it was 12.9%. Slovakia

The share of energy from renewable sources in final energy consumption in the

At the same time, in 2017, the highest increase in energy consumption in Slovakia was recorded by 7% of all EU Member States. Slovak gross domestic product (GDP) increased this year by 3.2%. This means that the country is failing to separate energy

Of all 28 EU Member States, in the share of renewable energy, the Slovak Republic

The decrease in the share of renewable energy sources was caused by lower growth in the use of renewable energy sources than the growth in final energy consumption. The growth in electricity consumption and the significant increase in the use of motor fuels, which caused a dynamic increase in energy consumption, reflect the Slovak Republic's economic growth. In the long term, the Slovak Republic's priority is energy efficiency, which leads to a reduction in energy consumption and

renewable energy sources. This is set at 14% for 2020.

returned statistically before 2014, when the share was 11.7% [1].

Slovak Republic in period 2004–2016 is shown in **Figure 3**.

thus to savings in fossil fuels and greenhouse gas emissions.

ended in the ninth place backward.

consumption from economic growth and thus enhance energy efficiency.

*Share of energy from renewable sources in final energy consumption in SR in period 2004–2016 (Source:* 

**118**

**Figure 3.**

*Energie Portál [6]).*

*Renewable energy sources are used in addition to electricity production also for heat production (Source: URSO [7, 8]).*

**Figure 4.**

*Heat supply in SR in 2018 (Source: URSO [7, 8]).*

According to the latest statistics of Eurostat [1], the Slovak Republic is the country with the highest year-on-year increase in final energy consumption—by 7%.

As energy consumption in the Slovak Republic is growing and renewable sources are not developing, their share inevitably decreases.

In the Slovak Republic, electricity from renewable sources is promoted through a fixed feed-in tariff. Energy companies are obliged to purchase and pay for electricity exported to the grid.

Renewable energy biomass must be given priority connection, and electricity from renewable sources must be given priority dispatch. The grid operator is obliged to extend the grid without discriminating against certain users.

Renewable energy sources are used in addition to electricity production also for heat production. URSO [7, 8] 2019 statistics show that in 2018 the most used fuel for heat production in Slovakia was natural gas. As can be seen from the data in **Table 1**, its use increased year-on-year most significantly from all fuels to around 8640 GWh.

The total volume of heat supply from renewable energy sources in 2018 was less than 2000 GWh according to data from the URSO Annual Report [7, 8]. From the combined heat and power (CHP) came 6000 GWh. The distribution of heat supply volume is visualized in **Figure 4**.

The support of heat from renewable energy sources mainly takes the form of financial support for investments in the Slovak Republic.

Further, we are focusing more on the legislation governing the use of renewable energy sources in Slovakia.

### **2. Legislation governing the use of renewable energy sources in Slovakia**

In Slovakia, the primary legislation consists of Act of the National Council of the Slovak Republic No. 656/2004 Coll. Energy Act [9]. This Act defines the basic processes related to electricity and RES, as well as basic concepts, and performance of state administration. It also introduces conditions for issuing a license for electricity production from RES and conditions for the construction of energy facilities (including facilities for electricity production, when electricity is produced from RES). And also, the Act defines the rights and obligations of a producer of electricity from RES and rights and obligations of the transmission and distribution system operator to which the producer of the electricity from RES is connected and through which the transmission or distribution of electricity produced from RES is carried out to the final consumption point. Under this Act, support for RES is achieved through the priority access, connection, transmission, distribution, and supply of electricity produced from RES. However, the producer must respect the technical and commercial conditions of access and grid connection, which are specified in the tertiary legislation.

The secondary legislation consists of the Government Regulation No. 211/2010 [10], laying down the rules for the functioning of the electricity market and the Act no. 309/2009 Coll. [11] on the Promotion of Renewable Energy Sources and High Efficiency Cogeneration and on Amendments to Certain Acts.

Government Regulation No. 211/2010 [10] (Electricity Market Rules), by its very nature, supplements the "Energy Act" and specifies some of its provisions. These market rules lay down the conditions for connection, access, transmission, and distribution of electricity. It defines the criteria to connect the producer to the system, criteria to carry out the distribution of electricity produced also from RES, and the necessary contractual relations necessary for connecting the production equipment. The contractual relations named in the Market Rules are further specified in tertiary legislation. The electricity market rules further define and develop functional processes related to market participant deviation, registration of daily supply diagrams, etc.

The Act no. 309/2009 Coll. [11] specifies the method of support and conditions for the promotion of electricity production from renewable energy sources, electricity by high-efficiency cogeneration, and biomethane; rights and obligations of producers of electricity from renewable energy sources, electricity from cogeneration, electricity from high-efficiency cogeneration, and biomethane; the rights and obligations of other electricity and gas market participants; and the rights and obligations of the legal person or the natural person who places on the market fuels and other energy products used for transport purposes.

The tertiary legislation includes in particular rules of operation of the transmission system operator; operating rules of the distribution system operator; technical conditions of the transmission system operator; technical conditions of the distribution system operator; URSO decisions; and URSO Decree no. 2/2008 and its amendments[7, 8].

### **3. Biomass as a renewable source of energy**

Biomass is one of the key renewable sources of energy that is produced from organic matter. It includes wood, agricultural crops and waste, and other "living" materials that can be used to produce heat and energy.

Dzurenda and Jandačka [12] define biomass as a matter of biological origin, which includes plant biomass grown in soil and water, animal biomass, production of animal origin, and organic waste.

Directive 2001/77/EC [13] defines the biomass as a biodegradable fraction of products, waste, and residues from agriculture (including vegetal and animal substances), forestry, and related industries, as well as the biodegradable fraction of industrial and municipal waste.

**121**

was 1.9% [18].

*Energy Potential of Biomass Sources in Slovakia DOI: http://dx.doi.org/10.5772/intechopen.91847*

[12]. Phytomass is a biomass of plant origin [15].

black locust (*Robinia pseudoacacia*) at that time.

In 2000, approximately 5.5 mil. m3

felling being found in coniferous forests [19].

to 3.05 mil. tons (3.05 MMt) in 2017.

distribution.

(i.e., 2–3 Kt) [17].

stands of higher (ruby) age.

9.3 mil. m3

Lieskovský and Gejdoš [14] understand the term biomass as all living and organic matter in each system that was originated and developed as a product of life processes (development, growth, and reproduction) of living organisms. According to this definition, it provides a very wide range of its possible systematic sorting and

In terms of its origin, we can talk about plant biomass (phytomass), animal biomass (zoomass), and municipal and industrial waste. Dendromass is an organic matter of woody and shrubby plants consisting of wood, bark, and green matter

Regardless of source, biomass materials can be divided into two broad categories: woody and non-woody. Forests provide only woody materials; agriculture sources provide both woody and non-woody biomass for bioenergy production [16].

The choice of biomass (i.e., woody or herbaceous species) for energy production purposes depends upon the end-use, bio-conversion portion of interest, e.g., combustion, gasification, pyrolysis, fermentation, or mechanical extraction of oils. Looking back at the recent past, also in the Slovak Republic, biomass for energy purposes was not an interesting topic until 2000. Traditionally, it was previously considered as an additional source of energy to meet local heating needs, mostly in areas without fossil fuel infrastructure. Until 1999, there was no domestic demand for forest fuel chips and their annual production ranged from 2000 to 3000 tons

The pioneer in this area was Slovenské energetické strojárne (SES), a. s., Tlmače in Slovakia, which reconstructed the boiler room in 2001 and adapted the equipment for the combustion of chips. According to the TREND newspaper (published on April 11, 2003), at that time it was 20,000 tons of wood chips per year, covering the heating needs of buildings and part of the Lipník housing estate in Tlmače. The use of wood chips in SES Tlmače also solved to a large extent the problem of the Forest Enterprise Levice (LZ), who were looking for sales opportunities for not very attractive tree species such as Turkey oak (*Quercus cerris*) and

Since that time, much has changed in the timber market. The amount of logging in the Slovak forests has been increasing in the past 15 years. Planned and actual logging is increasing in Slovakia, particularly due to an increase in the share of

The unbalanced age structure in the forests of Slovakia causes cyclical changes

an increase in the share, especially of the fifth-grade timber assortments, is visible. To a large extent, wood degradation is also due to a high proportion of incidental felling, which regularly exceeds 50% (57% in 2015), with a significant proportion of

According to the document "Utilization of wood for energy purposes," the total consumption of solid wood fuel biomass (fuel wood, chips, fine-grained and lump residues after processing and handling of wood, briquettes, and pellets) amounted

The key consumers of wood fuels, which are the dominant renewable energy source in Slovakia, are the wood processing and pulp and paper industry, the population, central heating sources, and the energy sector. The heat produced i s mainly used for heating and industrial purposes. The proportion of wood fuels in the total consumption of primary energy sources in the Slovak Republic

in 2017. The trend of the decreasing quality of timber on the market and

of timber was logged, while it was more than

also in the development of logging possibilities. It is anticipated that they will decline already around 2030 but depending on the extent of incidental felling [18]. *Green Energy and Environment*

specified in the tertiary legislation.

supply diagrams, etc.

amendments[7, 8].

of state administration. It also introduces conditions for issuing a license for electricity production from RES and conditions for the construction of energy facilities (including facilities for electricity production, when electricity is produced from RES). And also, the Act defines the rights and obligations of a producer of electricity from RES and rights and obligations of the transmission and distribution system operator to which the producer of the electricity from RES is connected and through which the transmission or distribution of electricity produced from RES is carried out to the final consumption point. Under this Act, support for RES is achieved through the priority access, connection, transmission, distribution, and supply of electricity produced from RES. However, the producer must respect the technical and commercial conditions of access and grid connection, which are

The secondary legislation consists of the Government Regulation No. 211/2010 [10], laying down the rules for the functioning of the electricity market and the Act no. 309/2009 Coll. [11] on the Promotion of Renewable Energy Sources and High

Government Regulation No. 211/2010 [10] (Electricity Market Rules), by its very nature, supplements the "Energy Act" and specifies some of its provisions. These market rules lay down the conditions for connection, access, transmission, and distribution of electricity. It defines the criteria to connect the producer to the system, criteria to carry out the distribution of electricity produced also from RES, and the necessary contractual relations necessary for connecting the production equipment. The contractual relations named in the Market Rules are further specified in tertiary legislation. The electricity market rules further define and develop functional processes related to market participant deviation, registration of daily

The Act no. 309/2009 Coll. [11] specifies the method of support and conditions for the promotion of electricity production from renewable energy sources, electricity by high-efficiency cogeneration, and biomethane; rights and obligations of producers of electricity from renewable energy sources, electricity from cogeneration, electricity from high-efficiency cogeneration, and biomethane; the rights and obligations of other electricity and gas market participants; and the rights and obligations of the legal person or the natural person who places on the market fuels

The tertiary legislation includes in particular rules of operation of the transmission system operator; operating rules of the distribution system operator; technical conditions of the transmission system operator; technical conditions of the distribution system operator; URSO decisions; and URSO Decree no. 2/2008 and its

Biomass is one of the key renewable sources of energy that is produced from organic matter. It includes wood, agricultural crops and waste, and other "living"

Dzurenda and Jandačka [12] define biomass as a matter of biological origin, which includes plant biomass grown in soil and water, animal biomass, production

Directive 2001/77/EC [13] defines the biomass as a biodegradable fraction of products, waste, and residues from agriculture (including vegetal and animal substances), forestry, and related industries, as well as the biodegradable fraction of

Efficiency Cogeneration and on Amendments to Certain Acts.

and other energy products used for transport purposes.

**3. Biomass as a renewable source of energy**

of animal origin, and organic waste.

industrial and municipal waste.

materials that can be used to produce heat and energy.

**120**

Lieskovský and Gejdoš [14] understand the term biomass as all living and organic matter in each system that was originated and developed as a product of life processes (development, growth, and reproduction) of living organisms. According to this definition, it provides a very wide range of its possible systematic sorting and distribution.

In terms of its origin, we can talk about plant biomass (phytomass), animal biomass (zoomass), and municipal and industrial waste. Dendromass is an organic matter of woody and shrubby plants consisting of wood, bark, and green matter [12]. Phytomass is a biomass of plant origin [15].

Regardless of source, biomass materials can be divided into two broad categories: woody and non-woody. Forests provide only woody materials; agriculture sources provide both woody and non-woody biomass for bioenergy production [16].

The choice of biomass (i.e., woody or herbaceous species) for energy production purposes depends upon the end-use, bio-conversion portion of interest, e.g., combustion, gasification, pyrolysis, fermentation, or mechanical extraction of oils.

Looking back at the recent past, also in the Slovak Republic, biomass for energy purposes was not an interesting topic until 2000. Traditionally, it was previously considered as an additional source of energy to meet local heating needs, mostly in areas without fossil fuel infrastructure. Until 1999, there was no domestic demand for forest fuel chips and their annual production ranged from 2000 to 3000 tons (i.e., 2–3 Kt) [17].

The pioneer in this area was Slovenské energetické strojárne (SES), a. s., Tlmače in Slovakia, which reconstructed the boiler room in 2001 and adapted the equipment for the combustion of chips. According to the TREND newspaper (published on April 11, 2003), at that time it was 20,000 tons of wood chips per year, covering the heating needs of buildings and part of the Lipník housing estate in Tlmače. The use of wood chips in SES Tlmače also solved to a large extent the problem of the Forest Enterprise Levice (LZ), who were looking for sales opportunities for not very attractive tree species such as Turkey oak (*Quercus cerris*) and black locust (*Robinia pseudoacacia*) at that time.

Since that time, much has changed in the timber market. The amount of logging in the Slovak forests has been increasing in the past 15 years. Planned and actual logging is increasing in Slovakia, particularly due to an increase in the share of stands of higher (ruby) age.

The unbalanced age structure in the forests of Slovakia causes cyclical changes also in the development of logging possibilities. It is anticipated that they will decline already around 2030 but depending on the extent of incidental felling [18].

In 2000, approximately 5.5 mil. m3 of timber was logged, while it was more than 9.3 mil. m3 in 2017. The trend of the decreasing quality of timber on the market and an increase in the share, especially of the fifth-grade timber assortments, is visible. To a large extent, wood degradation is also due to a high proportion of incidental felling, which regularly exceeds 50% (57% in 2015), with a significant proportion of felling being found in coniferous forests [19].

According to the document "Utilization of wood for energy purposes," the total consumption of solid wood fuel biomass (fuel wood, chips, fine-grained and lump residues after processing and handling of wood, briquettes, and pellets) amounted to 3.05 mil. tons (3.05 MMt) in 2017.

The key consumers of wood fuels, which are the dominant renewable energy source in Slovakia, are the wood processing and pulp and paper industry, the population, central heating sources, and the energy sector. The heat produced i s mainly used for heating and industrial purposes. The proportion of wood fuels in the total consumption of primary energy sources in the Slovak Republic was 1.9% [18].

### *Green Energy and Environment*

The heat producers associated in the Slovak Association of Heat Producers (SZVT) heat 38 places together, for which approximately 257,000 tons of timber are used annually (i.e., 2.14% of harvested wood). If the heat producers only used branches and wood waste for heating, it would still not be even 10% of the total harvested wood plant.

Other nine electricity producers from biomass, who are not associated with the SZVT, utilize approximately 530,000 tons (530 Kt) of wood annually, i.e., approximately 4.17% of total timber harvesting. Indication of how much wood is used for individual heating of households is not available [20].

The decisive legal document for forest management in the Slovak Republic is the Act no. 326/2005 Coll. [21] on forests, as amended. The Act defines the areas of forest land and forest protection, professional and differentiated forest management, forest use, and the requirement of sustainable forest management.

The current forestry and agriculture legislation also addresses land use issues related to the sustainability of forest biomass (also dendromass) production and has a direct impact on its energy use [22].

**Table 2** presents data representing the development in the dendromass stock specified for energy use.

The expected significant increase in the proportion of renewable energy sources and the use of underproductive agricultural land for the cultivation of energy stands results also in a significant increase in the potential of energy-efficient biomass to produce heat and energy in Slovakia. At the same time, it is possible to support further development of the fuel dendromass market. The amendment to the Act on forests introduced concepts such as energy stands and forest plantations. Energy stands are purpose-built forests with the aim of maximizing biomass production in the first 15 years, while also fulfilling other forest functions, especially soil conservation, erosion control, and partly landscape creation. Biomass produced in this way should be used mainly for energy production.

In energy stands and forest plantations, it is not possible to effectively use the management methods as in conventional forests. For example, it is unreasonable to require the provision of conventional management operations in such forest stand. For that reason, the application of the conventional stand management obligation is excluded in these cases. At the user's request, the stands can be reclassified to


**123**

sources [23].

*Energy Potential of Biomass Sources in Slovakia DOI: http://dx.doi.org/10.5772/intechopen.91847*

abovementioned tree species [23].

trees around roads.

extent more than 1000 m2

stock without bark).

logging technologies [25].

MMm3

energy stands during the recovery of the Forest Management Program (PSL). In 2006, almost 550 ha of forest were reclassified this way in the OZ Levice (management unit of Forests Slovakia, S.E.). These were mostly the coppices of black locust (96.1%) and Turkey oak (1.2%). These coppices are restored by the clear cutting connected with the maximum utilization of the stump and root sprouting of the

Current resources of wood on non-forest ground are mainly the tree stands on long-term unused agricultural land (so-called white areas), streamside stands, and trees in the open country, including linear planting vegetation, e.g., windbreaks and

Legislative conditions for planting fast-growing trees on agricultural land are determined directly by the Act no. 220/2004 Coll. [24] on the protection and use of agricultural land and by the amendment of Act no. 245/2003 Coll. on integrated pollution prevention and control and on amendments and supplements to certain acts. For the purposes of this Act, fast-growing trees on agricultural land shall mean

the plantation of fast-growing trees to produce wood biomass, on an area with

the 3rd to 5th degree of nature and landscape territorial preservation.

The current stock of coniferous trees is 12.7 MMm3

, and soft deciduous trees 14.8 MMm3

ties supporting regional energy self-sufficiency.

, for a maximum of 20 years. The fast-growing tree species can be planted on agricultural land classified into the 5th to 9th quality group, according to the code of a certified soil-ecological unit used in Slovakia. Also they can be planted on agricultural land contaminated by dangerous substances, or on agricultural land classified into the 3rd or 4th quality group according to the code of a certified soil-ecological unit, or on agricultural land, which is located in a floodplain, that is wet or exposed to wind erosion. The plantations of fast-growing tree species cannot be established on areas situated in

The tree stands on "white areas" formed mainly by succession of trees are located on an area of ca. 275,000 ha with a total wood supply of 36.6 MMm3

on "white areas" is represented by a higher proportion of fiber wood and wood for energy use than the stands on forest land. Due to their localization, stands on "white areas" are easily accessible, and terrain conditions enable the use of efficient timber

Another possibility of increasing biomass production is the plantation of fast-growing trees. The establishment of fast-growing tree plantations supports other unique and important environmental and ecological benefits that can provide enough raw material for the energy industry. At the same time, if certain decisions are considered in addition to production when planning a fast-growing tree plantation, they finally can have a positive impact on the landscape, biodiversity, soil, and water cycle in the ecosystem. The use of this method of targeted energy biomass extraction is a combination of forestry and agriculture and brings new opportuni-

With the increasing demands for biomass for energy purposes, the issues of production and targeted cultivation of fast-growing tree plantation (known also as short rotation coppice (SRC)) are becoming topical. In the future, demand for wood as a raw material for heating and electricity production is expected to increase. This increase will mainly be influenced by the situation on the fuel market and will be supported as a target of national and European energy policy. Energy chips from fast-growing tree species can thus make a significant contribution to the European targets related to increasing the proportion of renewable energy

(timber

, hard deciduous trees 9.1

. The assortment structure of stands

*1 Wood chips and wood to produce wood chips*

*2 Fuel wood and wood used for energy from waste, harvest residues, and dead trees*

### **Table 2.**

*Development of the dendromass stock for energy use (Source: NLC 1991–2018).*

### *Energy Potential of Biomass Sources in Slovakia DOI: http://dx.doi.org/10.5772/intechopen.91847*

*Green Energy and Environment*

harvested wood plant.

The heat producers associated in the Slovak Association of Heat Producers (SZVT) heat 38 places together, for which approximately 257,000 tons of timber are used annually (i.e., 2.14% of harvested wood). If the heat producers only used branches and wood waste for heating, it would still not be even 10% of the total

Other nine electricity producers from biomass, who are not associated with the SZVT, utilize approximately 530,000 tons (530 Kt) of wood annually, i.e., approximately 4.17% of total timber harvesting. Indication of how much wood is used for

The decisive legal document for forest management in the Slovak Republic is the Act no. 326/2005 Coll. [21] on forests, as amended. The Act defines the areas of forest land and forest protection, professional and differentiated forest management,

The current forestry and agriculture legislation also addresses land use issues related to the sustainability of forest biomass (also dendromass) production and has

**Table 2** presents data representing the development in the dendromass stock

In energy stands and forest plantations, it is not possible to effectively use the management methods as in conventional forests. For example, it is unreasonable to require the provision of conventional management operations in such forest stand. For that reason, the application of the conventional stand management obligation is excluded in these cases. At the user's request, the stands can be reclassified to

**Year Forest chips1 Wood fuel and other2 Total**

 580 5510 845 8028 1425 13,538 610 5795 830 7885 1440 13,680 615 5843 835 7933 1450 13,775 620 5890 820 7790 1440 13,680 250 2375 695 6602 945 8977 120 1140 640 6080 760 7220 5 48 471 4475 476 4522 2 19 368 3496 370 3515

**(Kt) (TJ) (Kt) (TJ) (Kt) TJ**

and the use of underproductive agricultural land for the cultivation of energy stands results also in a significant increase in the potential of energy-efficient biomass to produce heat and energy in Slovakia. At the same time, it is possible to support further development of the fuel dendromass market. The amendment to the Act on forests introduced concepts such as energy stands and forest plantations. Energy stands are purpose-built forests with the aim of maximizing biomass production in the first 15 years, while also fulfilling other forest functions, especially soil conservation, erosion control, and partly landscape creation. Biomass produced

The expected significant increase in the proportion of renewable energy sources

individual heating of households is not available [20].

in this way should be used mainly for energy production.

a direct impact on its energy use [22].

specified for energy use.

forest use, and the requirement of sustainable forest management.

**122**

*1*

*2*

**Table 2.**

*Wood chips and wood to produce wood chips*

*Fuel wood and wood used for energy from waste, harvest residues, and dead trees*

*Development of the dendromass stock for energy use (Source: NLC 1991–2018).*

energy stands during the recovery of the Forest Management Program (PSL). In 2006, almost 550 ha of forest were reclassified this way in the OZ Levice (management unit of Forests Slovakia, S.E.). These were mostly the coppices of black locust (96.1%) and Turkey oak (1.2%). These coppices are restored by the clear cutting connected with the maximum utilization of the stump and root sprouting of the abovementioned tree species [23].

Current resources of wood on non-forest ground are mainly the tree stands on long-term unused agricultural land (so-called white areas), streamside stands, and trees in the open country, including linear planting vegetation, e.g., windbreaks and trees around roads.

Legislative conditions for planting fast-growing trees on agricultural land are determined directly by the Act no. 220/2004 Coll. [24] on the protection and use of agricultural land and by the amendment of Act no. 245/2003 Coll. on integrated pollution prevention and control and on amendments and supplements to certain acts.

For the purposes of this Act, fast-growing trees on agricultural land shall mean the plantation of fast-growing trees to produce wood biomass, on an area with extent more than 1000 m<sup>2</sup> , for a maximum of 20 years.

The fast-growing tree species can be planted on agricultural land classified into the 5th to 9th quality group, according to the code of a certified soil-ecological unit used in Slovakia. Also they can be planted on agricultural land contaminated by dangerous substances, or on agricultural land classified into the 3rd or 4th quality group according to the code of a certified soil-ecological unit, or on agricultural land, which is located in a floodplain, that is wet or exposed to wind erosion. The plantations of fast-growing tree species cannot be established on areas situated in the 3rd to 5th degree of nature and landscape territorial preservation.

The tree stands on "white areas" formed mainly by succession of trees are located on an area of ca. 275,000 ha with a total wood supply of 36.6 MMm3 (timber stock without bark).

The current stock of coniferous trees is 12.7 MMm3 , hard deciduous trees 9.1 MMm3 , and soft deciduous trees 14.8 MMm3 . The assortment structure of stands on "white areas" is represented by a higher proportion of fiber wood and wood for energy use than the stands on forest land. Due to their localization, stands on "white areas" are easily accessible, and terrain conditions enable the use of efficient timber logging technologies [25].

Another possibility of increasing biomass production is the plantation of fast-growing trees. The establishment of fast-growing tree plantations supports other unique and important environmental and ecological benefits that can provide enough raw material for the energy industry. At the same time, if certain decisions are considered in addition to production when planning a fast-growing tree plantation, they finally can have a positive impact on the landscape, biodiversity, soil, and water cycle in the ecosystem. The use of this method of targeted energy biomass extraction is a combination of forestry and agriculture and brings new opportunities supporting regional energy self-sufficiency.

With the increasing demands for biomass for energy purposes, the issues of production and targeted cultivation of fast-growing tree plantation (known also as short rotation coppice (SRC)) are becoming topical. In the future, demand for wood as a raw material for heating and electricity production is expected to increase. This increase will mainly be influenced by the situation on the fuel market and will be supported as a target of national and European energy policy. Energy chips from fast-growing tree species can thus make a significant contribution to the European targets related to increasing the proportion of renewable energy sources [23].

The most frequently planted tree species on plantations are various clones and varieties of poplar (*Populus* sp.) and willow (*Salix* sp.). Current legislation does not directly limit plantation owners and users to the use of a clone or varieties, but the cultivation of non-origin tree species is in violation of the Act no. 543/2002 Coll. [26] on nature and landscape. Appropriate selection considering habitat conditions is a prerequisite for meeting production expectations.

An important factor that can influence the future plantation of fast-growing trees is enough potential area for their establishment. The potential of plantation establishment is both on the forest and in the agricultural ground fund.

In 2017, the area of utilized agricultural land was 1,910,654 ha. The Slovak Republic accounts for 38.8% of agricultural land in the total land area [18]. In addition, the distribution of agricultural land in the Slovak Republic is also characterized by a high proportion of agricultural land in mountain and foothill areas with rugged terrain and unfavorable climatic conditions.

Under such conditions, intensive agricultural production is not efficient today. However, it creates the preconditions for the possibility of diversification of production, one alternative of which is the production of biomass for energy purposes.

In the medium-term horizon, energy stands is considered to be planted on an area of 30,000 ha. Their production of energy chips is accounted for 70% and fiber wood for 30 %, considering the 15-year-long rotation period (MP SR 2018) [18].

The possibilities of biomass to be used for energy purposes and its energy properties are studied by many experts worldwide. There is introduced brief review of the last research works in this field.

The worldwide research trends related to biomass as renewable energy derived from the analysis of the state of the research and trends in biomass for renewable energy from 1978 to 2018 were published by Perea-Moreno et al. [27]. Woch et al. [28] published a case study focusing evaluation of potential use of forest biomass for renewable energy based on systems approach. The ways to meet the future energy demands based on biotechnology and wood for energy purposes are described by Al-Ahmad [29]. Climate, economic, and environmental impacts of producing wood for bioenergy are introduced by Birdsey et al. [30]. Koponen et al. [31] published a study in which they tried to quantify the climate effects of bioenergy. Cordiner et al. [32] introduced results of biomass pyrolysis modeling at laboratory scale, which were further completed with their experimental validation. Kluts et al. [33] dealt with agriculture biomass sources. There are also several studies focusing on the determination of energetic parameters of biomass, e.g., [34–37].

## **4. Assessment of woody biomass stock in Slovak forests: case study 1**

The geodatabase containing the data from the territory of the Slovak Republic (digital terrain model (DTM), settlements, district borders—producer and provider is the Topography Institute of the col. Jan Lipsky in Banska Bystrica) was added and preprocessed in ArcGIS for Desktop ver. 10.2. together with geodata on forest stand outlines and forest inventory database produced and provided by the Department of Forest Resources and Informatics of the National Forest Centre in Zvolen and containing the detailed description of the stands which is updated every 10 years.

For the needs of further analyses of the database, all the forest stands existing in the territory of the Slovak Republic were selected. Totally, there were 211,968 forest stands included in the analysis.

**125**

*Energy Potential of Biomass Sources in Slovakia DOI: http://dx.doi.org/10.5772/intechopen.91847*

plan elaboration.

excluded.

analyses.

from the analysis.

(region) units in Slovakia.

criteria, is introduced.

biomass stock).

purposes in Slovakia.

category of the stands.

use in forests of the Slovak Republic, was derived.

mostly in the National Parks of the Slovak Republic.

From the digital terrain model, and using the surface analyses tools in ArcGIS, a raster of terrain slopes in the ArcGIS environment, which was later used in the process of identifying available sources of woody biomass (dendromass) for energy

As the primary source of data for calculating the amount of dendromass is available, we used the data concerning the description of the basic parameters of forest stands, which are introduced in the database, which is used as the primary source of data for providing the spatial analyses in GIS environment. These data are the result of detailed surveys on forest which are provided for purposes of forest management

As the basic parameters for the derivation of the total available dendromass stock, we used the data on the extent of area of forest land, timber stock, and the planned annual cutting. In addition, we also analyzed the age structure and forest

Not all dendromass is suitable for energy purposes. There were specified restriction criteria. The most restrictive criterion to identify the dendromass for energy purposes is terrain slope. Steep terrain is a limitation for deployment of majority of timber logging technologies used in Slovakia. That is the reason why the forest stands situated within terrain with slope of 50% and more were

There were also excluded forest stands classified as protection forests, where protection function is superior to productive function. There are also included stands assigned into the 5th degree (the highest) of nature preservation, which are

The information on the category of forest and its nature protection level was obtained from a database containing basic parameters of forests, which we received from the Department of Forest Resources and Informatics, National Forest Centre. Those data were classified, and the unsuitable stands were excluded from further

Another criterion for excluding the stands unsuitable for energy purposes was the classification code of individual forest stands related to the "management set of forest types," which is used in the Slovak Republic. The management sets of forest types [38] were identified, which are naturally very low in nutrients (especially calcium, magnesium and potassium) or habitats with extreme texture, skeleton, water regime, as well as sites with an excess of certain nutrients, but a great lack of potassium and phosphorus. The forest stands belonging to those management sets of forest types were classified as unsuitable and were excluded

The results of the analyses are introduced in **Figure 5** and in **Table 3**. The information on potential woody biomass stock was derived from eight existing district

In **Table 3**, the potential stock of woody biomass to be used for energy purposes in Slovakia, derived from eight existing districts in Slovakia based on pre-defined

Further, we introduce the approaches used to determine the energetic properties of selected fast-growing tree species which have potential to be planted for energy

According to the data introduced in **Table 3**, we can state that the highest stock of woody biomass to be potentially used for energy purposes is in Banská Bystrica (24%) and Prešov (24%) districts (together 48% of the overall woody

The graphical output of the analysis is introduced in **Figure 5**.

### *Energy Potential of Biomass Sources in Slovakia DOI: http://dx.doi.org/10.5772/intechopen.91847*

*Green Energy and Environment*

purposes.

prerequisite for meeting production expectations.

rugged terrain and unfavorable climatic conditions.

of the last research works in this field.

The most frequently planted tree species on plantations are various clones and varieties of poplar (*Populus* sp.) and willow (*Salix* sp.). Current legislation does not directly limit plantation owners and users to the use of a clone or varieties, but the cultivation of non-origin tree species is in violation of the Act no. 543/2002 Coll. [26] on nature and landscape. Appropriate selection considering habitat conditions is a

An important factor that can influence the future plantation of fast-growing trees is enough potential area for their establishment. The potential of plantation

In 2017, the area of utilized agricultural land was 1,910,654 ha. The Slovak Republic accounts for 38.8% of agricultural land in the total land area [18]. In addition, the distribution of agricultural land in the Slovak Republic is also characterized by a high proportion of agricultural land in mountain and foothill areas with

Under such conditions, intensive agricultural production is not efficient today. However, it creates the preconditions for the possibility of diversification of production, one alternative of which is the production of biomass for energy

In the medium-term horizon, energy stands is considered to be planted on an area of 30,000 ha. Their production of energy chips is accounted for 70% and fiber wood for 30 %, considering the 15-year-long rotation period (MP SR 2018) [18]. The possibilities of biomass to be used for energy purposes and its energy properties are studied by many experts worldwide. There is introduced brief review

The worldwide research trends related to biomass as renewable energy derived

from the analysis of the state of the research and trends in biomass for renewable energy from 1978 to 2018 were published by Perea-Moreno et al. [27]. Woch et al. [28] published a case study focusing evaluation of potential use of forest biomass for renewable energy based on systems approach. The ways to meet the future energy demands based on biotechnology and wood for energy purposes are described by Al-Ahmad [29]. Climate, economic, and environmental impacts of producing wood for bioenergy are introduced by Birdsey et al. [30]. Koponen et al. [31] published a study in which they tried to quantify the climate effects of bioenergy. Cordiner et al. [32] introduced results of biomass pyrolysis modeling at laboratory scale, which were further completed with their experimental validation. Kluts et al. [33] dealt with agriculture biomass sources. There are also several studies focusing on the determination of energetic parameters of biomass, e.g., [34–37].

**4. Assessment of woody biomass stock in Slovak forests: case study 1**

The geodatabase containing the data from the territory of the Slovak Republic (digital terrain model (DTM), settlements, district borders—producer and provider is the Topography Institute of the col. Jan Lipsky in Banska Bystrica) was added and preprocessed in ArcGIS for Desktop ver. 10.2. together with geodata on forest stand outlines and forest inventory database produced and provided by the Department of Forest Resources and Informatics of the National Forest Centre in Zvolen and containing the detailed description of the stands which is updated

For the needs of further analyses of the database, all the forest stands existing in the territory of the Slovak Republic were selected. Totally, there were 211,968 forest

establishment is both on the forest and in the agricultural ground fund.

**124**

every 10 years.

stands included in the analysis.

From the digital terrain model, and using the surface analyses tools in ArcGIS, a raster of terrain slopes in the ArcGIS environment, which was later used in the process of identifying available sources of woody biomass (dendromass) for energy use in forests of the Slovak Republic, was derived.

As the primary source of data for calculating the amount of dendromass is available, we used the data concerning the description of the basic parameters of forest stands, which are introduced in the database, which is used as the primary source of data for providing the spatial analyses in GIS environment. These data are the result of detailed surveys on forest which are provided for purposes of forest management plan elaboration.

As the basic parameters for the derivation of the total available dendromass stock, we used the data on the extent of area of forest land, timber stock, and the planned annual cutting. In addition, we also analyzed the age structure and forest category of the stands.

Not all dendromass is suitable for energy purposes. There were specified restriction criteria. The most restrictive criterion to identify the dendromass for energy purposes is terrain slope. Steep terrain is a limitation for deployment of majority of timber logging technologies used in Slovakia. That is the reason why the forest stands situated within terrain with slope of 50% and more were excluded.

There were also excluded forest stands classified as protection forests, where protection function is superior to productive function. There are also included stands assigned into the 5th degree (the highest) of nature preservation, which are mostly in the National Parks of the Slovak Republic.

The information on the category of forest and its nature protection level was obtained from a database containing basic parameters of forests, which we received from the Department of Forest Resources and Informatics, National Forest Centre. Those data were classified, and the unsuitable stands were excluded from further analyses.

Another criterion for excluding the stands unsuitable for energy purposes was the classification code of individual forest stands related to the "management set of forest types," which is used in the Slovak Republic. The management sets of forest types [38] were identified, which are naturally very low in nutrients (especially calcium, magnesium and potassium) or habitats with extreme texture, skeleton, water regime, as well as sites with an excess of certain nutrients, but a great lack of potassium and phosphorus. The forest stands belonging to those management sets of forest types were classified as unsuitable and were excluded from the analysis.

The results of the analyses are introduced in **Figure 5** and in **Table 3**. The information on potential woody biomass stock was derived from eight existing district (region) units in Slovakia.

The graphical output of the analysis is introduced in **Figure 5**.

In **Table 3**, the potential stock of woody biomass to be used for energy purposes in Slovakia, derived from eight existing districts in Slovakia based on pre-defined criteria, is introduced.

According to the data introduced in **Table 3**, we can state that the highest stock of woody biomass to be potentially used for energy purposes is in Banská Bystrica (24%) and Prešov (24%) districts (together 48% of the overall woody biomass stock).

Further, we introduce the approaches used to determine the energetic properties of selected fast-growing tree species which have potential to be planted for energy purposes in Slovakia.

### **Figure 5.**

*Woody biomass stock in forests of the Slovak Republic (Source: Authors).*


### **Table 3.**

*Woody biomass stock to be used for energy purposes in Slovakia.*

## **5. Fire and energy properties of woody biomass: case study 2**

To analyze the fire and energetic properties of selected species of woody biomass for energy production purposes, several standardized but also progressive analytical methods were used.

Three woody biomass species were tested: *Populus x euroamericana* clone MAX 4, *Salix viminalis* clone TORA, and *Paulownia tomentosa*.

To implement the laboratory fire tests, the samples of woody biomass species were represented by the blocks with dimensions of 50 × 40 × 20 mm in the case of mass loss testing and 20 × 20 × 10 mm in the case of spontaneous ignition temperature testing.

The samples of *Salix viminalis* clone TORA and *Populus x euroamericana* clone MAX 4 were taken from the existing plantations of the University Forest Enterprise of the Technical University in Zvolen territory.

**127**

**Table 4.**

*Energy Potential of Biomass Sources in Slovakia DOI: http://dx.doi.org/10.5772/intechopen.91847*

ignition temperature testing, were used.

herbaceous energy crops undergone testing.

Technology, Technical University in Zvolen.

**of samples**

for a shorter time.

of the sample are recommended.

properties testing was applied.

**Spontaneous ignition temperature of** *Populus t* **(°C)**

*Spontaneous ignition temperatures of woody biomass species.*

**source**

**Measurement** 

**no.**

to the Agricultural Co-operative Dolné Saliby.

The samples of *Paulownia tomentosa* were taken from the plantations belonging

The following analyses were implemented: analyses of spontaneous ignition temperature; analyses of mass loss during sample thermal loading with radiant heat

In the laboratory fire tests, samples of woody species, i.e., blocks with dimensions of 50 × 40 × 20 mm for mass loss testing and 20 × 20 × 10 mm for spontaneous

Before the test, all the samples were conditioned according to the STN EN ISO 291 standard requirements. Totally, three samples of each woody biomass and

To determine the temperature of spontaneous ignition, the incendiary hot-air oven (Setchkin furnace) was used, and the methodology for testing the spontaneous ignition temperature, according to the STN ISO 871 standard, was applied. Those analyses were performed in the laboratories and use the research infrastructure of the Department of Fire Protection, Faculty of Wood Sciences and

**Table 4** shows an overview of the determined spontaneous ignition temperatures and induction periods reached by *Populus x euroamericana* clone MAX 4. The lowest mean spontaneous ignition temperature value was recorded by *Salix viminalis* clone TORA (419.46°C), which was reached in 328.87 s from the start of the test. The results also showed that with increasing thermal loading (and higher spontaneous ignition temperature value), the samples were resistant to fire

**5.2 Analysis of mass loss during sample thermal loading with radiant heat** 

To understanding the thermal decomposition process of all the samples tested during their burning, implementing thermal analyses, and studying the mass loss

To study the mass loss of the samples, the nonstandard method of solid thermal

The samples of woody biomass and energy crops undergone thermal loading by a radiant heater with the power of 1000 W for a specific time, i.e., 10 min. The mass

1. 424.92 412.65 420.10 2. 417.14 426.63 410.98 3. 419.69 419.09 441.87 Mean 420.58 419.46 424.32

**Spontaneous ignition temperature of** *Salix t* **(°C)**

**Spontaneous ignition temperature of** *Paulownia t* **(°C)**

source; and gross calorific value, heating value, and ash content analyses.

**5.1 Analyses of spontaneous ignition temperature and induction period** 

*Energy Potential of Biomass Sources in Slovakia DOI: http://dx.doi.org/10.5772/intechopen.91847*

*Green Energy and Environment*

**126**

ture testing.

**Table 3.**

**Figure 5.**

cal methods were used.

**5. Fire and energy properties of woody biomass: case study 2**

*Salix viminalis* clone TORA, and *Paulownia tomentosa*.

*Woody biomass stock to be used for energy purposes in Slovakia.*

*Woody biomass stock in forests of the Slovak Republic (Source: Authors).*

**District Biomass stock (m3**

of the Technical University in Zvolen territory.

To analyze the fire and energetic properties of selected species of woody biomass for energy production purposes, several standardized but also progressive analyti-

**) Number of forest stands**

Bratislava 10,782,036 9955 418.0 2059.5 Trnava 10,034,628 11,109 414.3 4143.0 Trenčín 37,080,538 32,030 1336.8 4501.3 Žilina 16,716,540 19,726 590.0 6789.2 Nitra 15,183,661 15,200 732.2 6338.7 Banská Bystrica 53,848,038 54,345 2292.3 9450.5 Košice 25,116,195 19,536 1041.3 6749.2 Prešov 53,896,725 46,659 2307.5 8988.2 Total 222,658,361 208,560 9132 49,020

**Stand extent (km2 )**

**District extent (km2 )**

Three woody biomass species were tested: *Populus x euroamericana* clone MAX 4,

To implement the laboratory fire tests, the samples of woody biomass species were represented by the blocks with dimensions of 50 × 40 × 20 mm in the case of mass loss testing and 20 × 20 × 10 mm in the case of spontaneous ignition tempera-

The samples of *Salix viminalis* clone TORA and *Populus x euroamericana* clone MAX 4 were taken from the existing plantations of the University Forest Enterprise

The samples of *Paulownia tomentosa* were taken from the plantations belonging to the Agricultural Co-operative Dolné Saliby.

The following analyses were implemented: analyses of spontaneous ignition temperature; analyses of mass loss during sample thermal loading with radiant heat source; and gross calorific value, heating value, and ash content analyses.

### **5.1 Analyses of spontaneous ignition temperature and induction period of samples**

In the laboratory fire tests, samples of woody species, i.e., blocks with dimensions of 50 × 40 × 20 mm for mass loss testing and 20 × 20 × 10 mm for spontaneous ignition temperature testing, were used.

Before the test, all the samples were conditioned according to the STN EN ISO 291 standard requirements. Totally, three samples of each woody biomass and herbaceous energy crops undergone testing.

To determine the temperature of spontaneous ignition, the incendiary hot-air oven (Setchkin furnace) was used, and the methodology for testing the spontaneous ignition temperature, according to the STN ISO 871 standard, was applied.

Those analyses were performed in the laboratories and use the research infrastructure of the Department of Fire Protection, Faculty of Wood Sciences and Technology, Technical University in Zvolen.

**Table 4** shows an overview of the determined spontaneous ignition temperatures and induction periods reached by *Populus x euroamericana* clone MAX 4.

The lowest mean spontaneous ignition temperature value was recorded by *Salix viminalis* clone TORA (419.46°C), which was reached in 328.87 s from the start of the test. The results also showed that with increasing thermal loading (and higher spontaneous ignition temperature value), the samples were resistant to fire for a shorter time.

### **5.2 Analysis of mass loss during sample thermal loading with radiant heat source**

To understanding the thermal decomposition process of all the samples tested during their burning, implementing thermal analyses, and studying the mass loss of the sample are recommended.

To study the mass loss of the samples, the nonstandard method of solid thermal properties testing was applied.

The samples of woody biomass and energy crops undergone thermal loading by a radiant heater with the power of 1000 W for a specific time, i.e., 10 min. The mass


**Table 4.**

*Spontaneous ignition temperatures of woody biomass species.*

loss of the samples (g) was measured for each 10-s interval. Totally, three samples of each woody biomass undergone testing.

Those analyses were performed in the laboratories and use the research infrastructure of the Department of Fire Protection, Faculty of Wood Sciences and Technology, Technical University in Zvolen.

The resulting courses of mass loss of the tested woody biomass species are introduced in **Figures 6**–**8**.

### **5.3 Gross calorific value, heating value, and ash content analyses**

To calculate the heating value, it was necessary to determine the gross caloric value of the samples. The IKA C200 calorimeter was used to determine it. The procedure was conducted in correspondence with the standard STN ISO 1928:2003- 07 Solid fuels. Determination of gross caloric value and calculation of heating value. In the test, the sample is burnt in a calorimetric bomb and filled with oxygen under the pressure of 3–5 MPa.

Based on the mathematical Eq. (1) introduced in the same standard, the heating values (KJ∙kg<sup>−</sup><sup>1</sup> ) of the samples were further calculated:

$$q\_{v, \text{net}, m} = \left[q\_{v \text{gr}, d} - 20 \pounds 0 \cdot w \left(H\right)\_d\right] \cdot \left(1 - 0.01 \cdot M\_T\right) - 23.5 \cdot M\_T \tag{1}$$

where *qv,net,m*—heating value at constant volume and containing with water (kJ∙kg<sup>−</sup><sup>1</sup> ); *qv,gr,d*—gross calorific value at constant volume without water content (kJ∙kg<sup>−</sup><sup>1</sup> ); *w(H)d*—percentage of hydrogen (%); *MT*—total water content of the fuel for which conversion is required - relative moisture (%).

In the calculations of heating value, the relative moisture content of 10% was used. Before the testing, the samples were dried at 103 ± 2°C to reach the moisture content of 0% and further conditioned in a desiccator at the temperature of 20 ± 1°C for 24 hrs. Three measurements were made for each sample. The results show the average value of those measurements.

The procedure for ash determination was based on the requirements of the standard STN ISO 1171:2003 (441378) Solid mineral fuels. Determination of ash. The principle of the method is the incineration of the sample, heated in air at a temperature of 815 °C ± 10 °C, for specified time interval and maintained at that abovementioned constant temperature. For this purpose, the muffle furnace was used. The ash content was calculated from the weight of the residue after incineration.

Those analyses were performed at laboratories and use the research infrastructure of the Department of Forest Harvesting, Logistics and Ameliorations, Faculty of Forestry, Technical University in Zvolen.

**129**

**Table 5.**

*Energy Potential of Biomass Sources in Slovakia DOI: http://dx.doi.org/10.5772/intechopen.91847*

values (19.71 ± 0.18 MJ∙kg<sup>−</sup><sup>1</sup>

cies are introduced in **Table 5**.

lowest value of ash content in the analyses.

of *Paulownia tomentosa* (19.71 ± 0.18 MJ∙kg<sup>−</sup><sup>1</sup>

**Sample Gross calorific** 

*Gross calorific value and heating value of tested woody biomass samples.*

clone MAX 4 (19.47 ± 0.29 MJ∙kg<sup>−</sup><sup>1</sup>

*Paulownia tomentosa*.

**Figure 7.**

**Figure 8**

The highest energy potential expressed in terms of the highest gross calorific

The results of gross calorific value and heating value of all woody biomass spe-

The highest ash content was achieved in *Populus x euroamericana* clone MAX 4, followed by *Salix viminalis* clone Tora and *Paulownia tomentosa* which showed the

The highest energy potential expressed in terms of the highest gross calorific values as well as heating values (at 10% moisture content) was recorded in the case

ues of gross calorific values and heating were recorded in *Populus x euroamericana*

recorded in the tested samples of fast-growing tree species were very low. According

**value (MJ∙kg<sup>−</sup><sup>1</sup>**

*Populus x euroamericana* clone MAX 4 19.47 16.18 0.29 *Salix viminalis* clone Tora 19.63 16.33 0.11 *Paulownia tomentosa* 19.71 16.40 0.18

; 16.18 ± 0.29 MJ∙kg<sup>−</sup><sup>1</sup>

**)**

The ash content analyses results are introduced in **Table 6**.

*Mass loss course of* Salix viminalis *clone TORA during the thermal loading.*

*Mass loss course of* Paulownia tomentosa *during the thermal loading.*

) as well as heating values was found in the case of

; 16.40 ± 0.18 MJ∙kg<sup>−</sup><sup>1</sup>

**Heating value (MJ∙kg<sup>−</sup><sup>1</sup> )**

). The lowest val-

**Standard deviation**

). The differences in values

**Figure 6.** *Mass loss course of* Populus x euroamericana *clone MAX 4 during the thermal loading.*

*Energy Potential of Biomass Sources in Slovakia DOI: http://dx.doi.org/10.5772/intechopen.91847*

**Figure 7.** *Mass loss course of* Salix viminalis *clone TORA during the thermal loading.*

### **Figure 8**

*Green Energy and Environment*

introduced in **Figures 6**–**8**.

the pressure of 3–5 MPa.

values (KJ∙kg<sup>−</sup><sup>1</sup>

(kJ∙kg<sup>−</sup><sup>1</sup>

(kJ∙kg<sup>−</sup><sup>1</sup>

each woody biomass undergone testing.

Technology, Technical University in Zvolen.

loss of the samples (g) was measured for each 10-s interval. Totally, three samples of

Those analyses were performed in the laboratories and use the research infrastructure of the Department of Fire Protection, Faculty of Wood Sciences and

The resulting courses of mass loss of the tested woody biomass species are

To calculate the heating value, it was necessary to determine the gross caloric value of the samples. The IKA C200 calorimeter was used to determine it. The procedure was conducted in correspondence with the standard STN ISO 1928:2003- 07 Solid fuels. Determination of gross caloric value and calculation of heating value. In the test, the sample is burnt in a calorimetric bomb and filled with oxygen under

Based on the mathematical Eq. (1) introduced in the same standard, the heating

*qv*,*net*,*m* = [*qv*,*gr*,*<sup>d</sup>* − 206.0 ∙ *w* (*H*)*d*] ∙ (1 − 0.01 ∙ *MT*) − 23.5 ∙ *MT* (1)

); *qv,gr,d*—gross calorific value at constant volume without water content

In the calculations of heating value, the relative moisture content of 10% was used. Before the testing, the samples were dried at 103 ± 2°C to reach the moisture content of 0% and further conditioned in a desiccator at the temperature of 20 ± 1°C for 24 hrs. Three measurements were made for each sample. The results

The procedure for ash determination was based on the requirements of the standard STN ISO 1171:2003 (441378) Solid mineral fuels. Determination of ash. The principle of the method is the incineration of the sample, heated in air at a temperature of 815 °C ± 10 °C, for specified time interval and maintained at that abovementioned constant temperature. For this purpose, the muffle furnace was used. The ash content was calculated from the weight of the residue after incineration. Those analyses were performed at laboratories and use the research infrastructure of the Department of Forest Harvesting, Logistics and Ameliorations, Faculty

*Mass loss course of* Populus x euroamericana *clone MAX 4 during the thermal loading.*

); *w(H)d*—percentage of hydrogen (%); *MT*—total water content of the fuel

where *qv,net,m*—heating value at constant volume and containing with water

**5.3 Gross calorific value, heating value, and ash content analyses**

) of the samples were further calculated:

for which conversion is required - relative moisture (%).

show the average value of those measurements.

of Forestry, Technical University in Zvolen.

**128**

**Figure 6.**

*Mass loss course of* Paulownia tomentosa *during the thermal loading.*

The highest energy potential expressed in terms of the highest gross calorific values (19.71 ± 0.18 MJ∙kg<sup>−</sup><sup>1</sup> ) as well as heating values was found in the case of *Paulownia tomentosa*.

The results of gross calorific value and heating value of all woody biomass species are introduced in **Table 5**.

The ash content analyses results are introduced in **Table 6**.

The highest ash content was achieved in *Populus x euroamericana* clone MAX 4, followed by *Salix viminalis* clone Tora and *Paulownia tomentosa* which showed the lowest value of ash content in the analyses.

The highest energy potential expressed in terms of the highest gross calorific values as well as heating values (at 10% moisture content) was recorded in the case of *Paulownia tomentosa* (19.71 ± 0.18 MJ∙kg<sup>−</sup><sup>1</sup> ; 16.40 ± 0.18 MJ∙kg<sup>−</sup><sup>1</sup> ). The lowest values of gross calorific values and heating were recorded in *Populus x euroamericana* clone MAX 4 (19.47 ± 0.29 MJ∙kg<sup>−</sup><sup>1</sup> ; 16.18 ± 0.29 MJ∙kg<sup>−</sup><sup>1</sup> ). The differences in values recorded in the tested samples of fast-growing tree species were very low. According


**Table 5.**

*Gross calorific value and heating value of tested woody biomass samples.*


### **Table 6.**

*Ash content of the woody biomass species.*

to these finding, all the tested biomass species were considered suitable to be used for further energy use. However, *Paulownia tomentosa* seems to be the most suitable from calorific value and heating value point of view.

Heating value should be tightly connected also with elemental composition and affected by the variation in cell wall composition and ash. This fact was confirmed also by ash content analysis using the muffle furnace for ashing. The ash content of tested woody biomass species was in the range of 0.75–2.58 w%. The lowest values of ash content were recorded right in *Paulownia tomentosa* (0.75 ± 0.05 w%).

Similar results were achieved also by Yavorov et al. [37], who were engaged in determining the potential of fast-growing hardwood species from Bulgaria (*Paulownia elongata*, *Populus alba*, and *Salix viminalis* RUBRA), and Martinka et al. [39] who studied the calorific value and fire risk of selected fast-growing hardwood species (*Populus nigra x Populus maximowiczii*, *Salix alba* L.).

### **6. Conclusions**

Climate change caused by increasing greenhouse gas emissions is among the most serious global threats. Therefore, many experts have been looking for ways to solve this problem for more than 20 years.

In recent years, in the world, the importance of the energy sector has increased, particularly in terms of sustainable development. The direction of energy sector development is slowly changing toward the use of environmentally friendly fuels and energy from renewable sources.

Slovakia as a country that is more than 90% dependent on imports of primary energy sources should have a primary interest to use its own, renewable energy sources.

Biomass is the largest renewable energy source in Slovakia. It consists of vegetable and animal origin materials suitable for energy use. Biomass is considered in terms of CO2 biomass is a neutral fuel, because it shall release only as much of the CO2 when burning as the plant has taken during its growing.

The Energy Policy of the Slovak Republic aims in increasing the share of renewable and secondary energy sources, which constitutes a significant portion of woody biomass (dendromass) produced in forestry, wood industry, and pulp and paper industries.

To identify the available sources of woody biomass or any kind of biomass, as the first step of any analysis concerning the possible location of any power plant using biomass for energy and heat production, it is recommended to deploy those tools allow processing the existing data on forest and agricultural land and different kinds of spatial analyses to get the required information. An approach that is used in Slovakia for this purpose is introduced.

To study the important characteristics for the biomass combustion process is possible through deployment of standardized and progressive nonstandardized

**131**

*Energy Potential of Biomass Sources in Slovakia DOI: http://dx.doi.org/10.5772/intechopen.91847*

introduced in the framework of this chapter.

International Teaming Research Centers (20%).

RES Renewable Energy Sources

EC European Commission CHP Combined Heat and Power GDP Gross Domestic Product

> [kJ∙kg<sup>−</sup><sup>1</sup> ]

[kJ∙kg<sup>−</sup><sup>1</sup> ] *w(H)d* Percentage of hydrogen [%]

*t* Temperature [°C]

URSO Regulatory Office for Network Industries MP SR Ministry of Agriculture of the Slovak Republic

**Acknowledgements**

**Nomenclature**

**Symbols**

EU European Union

SR Slovak Republic DTM Digital Terrain Model

laboratory fire tests and calorimetric and thermal analyses. Some of them were

This work was supported by the Slovak Research and Development Agency, based on the Agreements no. APVV-17-0005 (20%) and APVV-16-0487 (20%), VEGA Grant Agency under project VEGA 1/0493/18 (20%), KEGA Grant Agency under projects KEGA 032PU-4/2018 (20%) and MPRVSR item 08V0301 – Research and development to promote forestry competitiveness (SLOV – LES) and OPVaI

*qv,net,m* Heating value at constant volume and containing with water

*qv,gr,d* Gross calorific value at constant volume without water content

*Energy Potential of Biomass Sources in Slovakia DOI: http://dx.doi.org/10.5772/intechopen.91847*

laboratory fire tests and calorimetric and thermal analyses. Some of them were introduced in the framework of this chapter.

## **Acknowledgements**

*Green Energy and Environment*

*Ash content of the woody biomass species.*

from calorific value and heating value point of view.

species (*Populus nigra x Populus maximowiczii*, *Salix alba* L.).

CO2 when burning as the plant has taken during its growing.

solve this problem for more than 20 years.

in Slovakia for this purpose is introduced.

and energy from renewable sources.

**Table 6.**

(0.75 ± 0.05 w%).

**6. Conclusions**

paper industries.

**Sample Ash content (w%) Standard deviation** *Populus x euroamericana* clone MAX 4 2.58 0.24 *Salix viminalis* clone Tora 1.28 0.08 *Paulownia tomentosa* 0.75 0.05

to these finding, all the tested biomass species were considered suitable to be used for further energy use. However, *Paulownia tomentosa* seems to be the most suitable

Heating value should be tightly connected also with elemental composition and affected by the variation in cell wall composition and ash. This fact was confirmed also by ash content analysis using the muffle furnace for ashing. The ash content of tested woody biomass species was in the range of 0.75–2.58 w%. The lowest values of ash content were recorded right in *Paulownia tomentosa*

Similar results were achieved also by Yavorov et al. [37], who were engaged in determining the potential of fast-growing hardwood species from Bulgaria (*Paulownia elongata*, *Populus alba*, and *Salix viminalis* RUBRA), and Martinka et al. [39] who studied the calorific value and fire risk of selected fast-growing hardwood

Climate change caused by increasing greenhouse gas emissions is among the most serious global threats. Therefore, many experts have been looking for ways to

Slovakia as a country that is more than 90% dependent on imports of primary energy sources should have a primary interest to use its own, renewable energy sources. Biomass is the largest renewable energy source in Slovakia. It consists of vegetable and animal origin materials suitable for energy use. Biomass is considered in terms of CO2 biomass is a neutral fuel, because it shall release only as much of the

The Energy Policy of the Slovak Republic aims in increasing the share of renew-

To identify the available sources of woody biomass or any kind of biomass, as the first step of any analysis concerning the possible location of any power plant using biomass for energy and heat production, it is recommended to deploy those tools allow processing the existing data on forest and agricultural land and different kinds of spatial analyses to get the required information. An approach that is used

To study the important characteristics for the biomass combustion process is possible through deployment of standardized and progressive nonstandardized

able and secondary energy sources, which constitutes a significant portion of woody biomass (dendromass) produced in forestry, wood industry, and pulp and

In recent years, in the world, the importance of the energy sector has increased, particularly in terms of sustainable development. The direction of energy sector development is slowly changing toward the use of environmentally friendly fuels

**130**

This work was supported by the Slovak Research and Development Agency, based on the Agreements no. APVV-17-0005 (20%) and APVV-16-0487 (20%), VEGA Grant Agency under project VEGA 1/0493/18 (20%), KEGA Grant Agency under projects KEGA 032PU-4/2018 (20%) and MPRVSR item 08V0301 – Research and development to promote forestry competitiveness (SLOV – LES) and OPVaI International Teaming Research Centers (20%).

## **Nomenclature**


## **Symbols**


*Green Energy and Environment*

## **Author details**

Andrea Majlingová1 \*, Martin Lieskovský2 , Maroš Sedliak<sup>3</sup> and Marián Slamka3

1 Faculty of Wood Sciences and Technology, Department of Fire Protection, Technical University in Zvolen, Zvolen, Slovakia

2 Faculty of Forestry, Department of Forest Harvesting, Logistics and Amelioration, Technical University in Zvolen, Zvolen, Slovakia

3 National Forest Centre, Forest Research Institute, Zvolen, Slovakia

\*Address all correspondence to: majlingova@tuzvo.sk

© 2020 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.

**133**

*Energy Potential of Biomass Sources in Slovakia DOI: http://dx.doi.org/10.5772/intechopen.91847*

[1] Eurostat. Renewable Energy Statistics [Online]. 2019. Available from: https:// ec.europa.eu [cited 03 June 2019]

Consumption in SR in Period 2004-2016 [Online]. 2019. Available from: https://www.energie-portal.sk/

[7] Decree of the Regulatory Office for Network Industries Laying Down Details of the Request and the List of Documents for Exemption from the Obligation to Provide Third Party Access to Networks and Storage Capacity for New Significant Gas Facility or Refurbished Gas Facility. Available from: http://www.urso.gov.sk

[8] URSO. Annual Report 2018 [Online]. 2019. Available from: http://www.urso.

[9] Act of the National Council of the Slovak Republic No. 656/2004 Coll. "Energy Act". Available from: https://

[10] Regulation of the Government of the Slovak Republic No. 211/2010, Amending and Supplementing Regulation of the Government of the Slovak Republic No. 317/2007 Coll. Laying Down the Rules for the Functioning of the Electricity Market, as Amended by Act No. 309/2009 Coll [Online]. Available from: https://www. slov-lex.sk [cited 17 November 2019]

[11] Act No. 309/2009 Coll. on the Promotion of Renewable Energy Sources and High Efficiency

www.zakonypreludi.sk

in Zvolen; 2010. p. 161

[13] Directive 2001/77/EC of the European Parliament and of the Council of 27 September 2001 On the Promotion of Electricity Produced

Cogeneration and on Amendments to Certain Acts. Available from: https://

[12] Dzurenda L, Jandačka J. Energetické využitie Biomasy [Energy Use of Biomass]. Zvolen: Technical University

gov.sk [cited 11 April 2019]

www.zakonypreludi.sk/

[cited 26 November 2018]

[2] Directive 2009/28/EC of the European Parliament and of the Council of 23 April 2009 On the Promotion of the Use of Energy from Renewable Sources and Amending and Subsequently Repealing Directives 2001/77/EC and 2003/30/EC. Available

from: https://eur-lex.europa.eu

[4] Directive 2012/27/EU of the European Parliament and of the Council of 25 October 2012 On Energy Efficiency, Amending Directives 2009/125/EC and 2010/30/EU and Repealing Directives 2004/8/EC and 2006/32/EC Text with EEA Relevance. Available from: https://eur-lex.europa.eu

[5] European Parliament News. Energy: New Target of 32% from Renewables by 2030 Agreed by MEPs and Ministers [Online]. 2018. Available from: https:// www.europarl.europa.eu/news/en/ press-room/20180614IPR05810/energynew-target-of-32-from-renewables-by-2030-agreed-by-meps-and-ministers

[6] Energie Portál. Share of Energy from Renewable Sources in Final Energy

[cited 17 February 2020]

[3] Regulation (EU) 2018/1999 of the European Parliament and of the Council on the Governance of the Energy Union and Climate Action, Amending Regulations (EC) No. 663/2009 and (EC) No. 715/2009 of the European Parliament and of the Council, Directives 94/22/ EC, 98/70/EC, 2009/31/EC, 2009/73/EC, 2010/31/EU, 2012/27/EU and 2013/30/EU of the European Parliament and of the Council, Council Directives 2009/119/ EC and (EU) 2015/652 and Repealing Regulation (EU) No 525/2013 of the European Parliament and of the Council [Online]. Available from: https://eur-lex. europa.eu [cited 17 February 2020]

**References**

*Energy Potential of Biomass Sources in Slovakia DOI: http://dx.doi.org/10.5772/intechopen.91847*

## **References**

*Green Energy and Environment*

**132**

**Author details**

Andrea Majlingová1

\*, Martin Lieskovský2

Technical University in Zvolen, Zvolen, Slovakia

\*Address all correspondence to: majlingova@tuzvo.sk

provided the original work is properly cited.

1 Faculty of Wood Sciences and Technology, Department of Fire Protection,

© 2020 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,

2 Faculty of Forestry, Department of Forest Harvesting, Logistics and Amelioration, Technical University in Zvolen, Zvolen, Slovakia

3 National Forest Centre, Forest Research Institute, Zvolen, Slovakia

, Maroš Sedliak<sup>3</sup>

and Marián Slamka3

[1] Eurostat. Renewable Energy Statistics [Online]. 2019. Available from: https:// ec.europa.eu [cited 03 June 2019]

[2] Directive 2009/28/EC of the European Parliament and of the Council of 23 April 2009 On the Promotion of the Use of Energy from Renewable Sources and Amending and Subsequently Repealing Directives 2001/77/EC and 2003/30/EC. Available from: https://eur-lex.europa.eu

[3] Regulation (EU) 2018/1999 of the European Parliament and of the Council on the Governance of the Energy Union and Climate Action, Amending Regulations (EC) No. 663/2009 and (EC) No. 715/2009 of the European Parliament and of the Council, Directives 94/22/ EC, 98/70/EC, 2009/31/EC, 2009/73/EC, 2010/31/EU, 2012/27/EU and 2013/30/EU of the European Parliament and of the Council, Council Directives 2009/119/ EC and (EU) 2015/652 and Repealing Regulation (EU) No 525/2013 of the European Parliament and of the Council [Online]. Available from: https://eur-lex. europa.eu [cited 17 February 2020]

[4] Directive 2012/27/EU of the European Parliament and of the Council of 25 October 2012 On Energy Efficiency, Amending Directives 2009/125/EC and 2010/30/EU and Repealing Directives 2004/8/EC and 2006/32/EC Text with EEA Relevance. Available from: https://eur-lex.europa.eu

[5] European Parliament News. Energy: New Target of 32% from Renewables by 2030 Agreed by MEPs and Ministers [Online]. 2018. Available from: https:// www.europarl.europa.eu/news/en/ press-room/20180614IPR05810/energynew-target-of-32-from-renewables-by-2030-agreed-by-meps-and-ministers [cited 17 February 2020]

[6] Energie Portál. Share of Energy from Renewable Sources in Final Energy

Consumption in SR in Period 2004-2016 [Online]. 2019. Available from: https://www.energie-portal.sk/ [cited 26 November 2018]

[7] Decree of the Regulatory Office for Network Industries Laying Down Details of the Request and the List of Documents for Exemption from the Obligation to Provide Third Party Access to Networks and Storage Capacity for New Significant Gas Facility or Refurbished Gas Facility. Available from: http://www.urso.gov.sk

[8] URSO. Annual Report 2018 [Online]. 2019. Available from: http://www.urso. gov.sk [cited 11 April 2019]

[9] Act of the National Council of the Slovak Republic No. 656/2004 Coll. "Energy Act". Available from: https:// www.zakonypreludi.sk/

[10] Regulation of the Government of the Slovak Republic No. 211/2010, Amending and Supplementing Regulation of the Government of the Slovak Republic No. 317/2007 Coll. Laying Down the Rules for the Functioning of the Electricity Market, as Amended by Act No. 309/2009 Coll [Online]. Available from: https://www. slov-lex.sk [cited 17 November 2019]

[11] Act No. 309/2009 Coll. on the Promotion of Renewable Energy Sources and High Efficiency Cogeneration and on Amendments to Certain Acts. Available from: https:// www.zakonypreludi.sk

[12] Dzurenda L, Jandačka J. Energetické využitie Biomasy [Energy Use of Biomass]. Zvolen: Technical University in Zvolen; 2010. p. 161

[13] Directive 2001/77/EC of the European Parliament and of the Council of 27 September 2001 On the Promotion of Electricity Produced

from Renewable Energy Sources in the Internal Electricity Market. Available from: https://eur-lex.europa.eu

[14] Lieskovský M, Gejdoš M. Komplexné využitie Biomasy v Lesnom hospodárstve [Complex Use of Biomass in Forestry]. Zvolen: Technická univerzita vo Zvolene; 2016. p. 136

[15] Hutňan M. Využitie fytomasy ako obnoviteľného zdroja na energetické účely [Use of phytomass as a renewable source for energy purposes]. In: Pospíšil R, editor. Využitie Biomasy z Obnoviteľných Zdrojov na Energetické Účely. Nitra: Slovenská Poľnohospodárska Univerzita v Nitre; 2012. pp. 163-170

[16] Jawaid M, Paridah M, Tahir PM, Saba N. Lignocellulosic Fibre and Biomass-Based Composite Materials— Processing, Properties and Applications. London, UK: Woodhead Publishing; 2017. p. 522

[17] Oravec M. BIOCLUS—Developing Research and Innovation Environment in five European Regions in the field of Sustainable Use of Biomass Sources (Project 245438)—Regionálna Stratégia Slovenska 2010 [Online]. 2010. Available from: http://www.bioclus.eu [cited 02 December 2018]

[18] MP SR. Green Report 2017 [Online]. 2018. Available from: http://www.mpsr. sk/en/index.php?navID=16&id=68 [cited 21 March 2019]

[19] Gejdoš M, Lieskovský M. Aké Hrozia biologické riziká Pri Dlhodobom skladovaní lesných štiepok? [What Are the Biological Risks of Long-Term Storage of Forest Chips?]. In: Správy z Výskumu Lesníckej Fakulty Pre Prax. Zvolen: Vydavateľstvo TU vo Zvolene; 2017. pp. 39-44

[20] Oravec M, Slamka M. Výskum Potenciálu Drevnej Biomasy na Energetické Využitie v Podmienkach Slovenska [Research of the Potential

of Wood Biomass for Energy Use in Slovakia] [Online]. Zvolen: Národné Lesnícke Centrum—Lesnícky Výskumný Ústav Zvolen; 2018. Available from: http://www.nlcsk.sk [cited 02 December 2018]

[21] Act No. 326/2005 Coll. on Forests. Available from: https://www. zakonypreludi.sk

[22] KPMG. Kritériá Udržateľného Využívania Biomasy v Regiónoch Slovenska pre Programy SR na Obdobie 2014-2020 Spolufinancované z EŠIF-So Zameraním na Drevnú Biomasu [Criteria for Sustainable Use of Biomass in Regions of Slovakia for 2014-2020 Programs of the SR Co-financed by the European Structural Investment Fund-Focusing on Wood Biomass] [Online]. 2016. Available from: https://www.op-kzp.sk [cited 02 December 2018]

[23] Messingerová V, Lieskovský M, Gejdoš M, Slugeň J, Mokroš M, Tomaštík J. Technika a Technologické Postupy Pri Produkcii Biomasy a Jej Energetickom Zhodnotení [Technology and Technological Procedures in Biomass Production and its Energy Recovery]. Zvolen, Slovakia: Technická Univerzita vo Zvolene; 2016. p. 101

[24] Act No. 220/2004 Coll. on the Protection and Use of Agricultural Land and on the Amendment of Act No. 245/2003 Coll. on Integrated Pollution Prevention and Control and on Amendments and Supplements to Certain Acts. Available from: https:// www.zakonypreludi.sk

[25] Oravec M, Bartko M, Slamka M. Postupy Intenzifikácie Produkcie Drevnej Biomasy na Energetické Využitie [Procedures for Intensifying Wood Biomass Production for Energy Use]. Zvolen, Slovakia: Národné Lesnícke Centrum—Lesnícky Výskumný Ústav; 2012. p. 65

**135**

*Energy Potential of Biomass Sources in Slovakia DOI: http://dx.doi.org/10.5772/intechopen.91847*

[26] Act No. 543/2002 Coll. on Nature and Landscape Protection. Available from: https://www.zakonypreludi.sk

[35] Majlingová A, Zachar M, Lieskovský M, et al. The analysis of mass loss and activation energy of selected fast-growing tree species and energy crops using the Arrhenius equation. Acta Facultatis Xylologiae

Zvolen. 2018;**60**(2):177-188

(EEEIC/I&CPS Europe); 2018

University in Zvolen; 2006

2018;**131**:899-906

[37] Yavorov N, Petrin ST, Valchev I, et al. Potential of fast-growing poplar, willow and Paulownia for bioenergy production. Bulgarian Chemical Communications. 2015;**47**(SI:A):5-9

[38] Ilavský J et al. Štúdia o Dostupných Zdrojoch Biomasy a Ich Efektívnom Zabezpečení na Výrobu Energie Vo Zvolenskej Teplárenskej a.s. [Study on Available Biomass Resources and their Effective Ensuring for Power Generation in Zvolen Heat Plant, Monograph]. 1st ed. Zvolen: Technical

[39] Martinka J, Martinka F, Rantuch P, Hrušovský I, Blinová L, Balog K. Calorific value and fire risk of selected fast/growing wood species. Journal of Thermal Analysis and Calorimetry.

[36] Rodrigues A, Nunes LJR, Godina R, Matias JCO, et al. Correlation between chemical alterations and energetic properties in Torrefied biomass. In: 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe

Sameron-Manzano E, Pera-Moreno AJ.

[28] Woch F, Hernik J, Sankowski E, Pioro P, et al. Evaluating the potential use of forest biomass for renewable energy: A case study with elements of a systems approach. Polish Journal of Environmental Studies.

[29] Al-Ahmad H. Biotechnology for bioenergy dedicated trees: Meeting future energy demands. Zeitschrift Fur Naturforschung, Section C-A Journal of

Biosciences. 2018;**73**(1-2):15-32

Research Letters. 2018;**13**(5)

[31] Koponen K, Soimakallio S, Kline KL, Cowie A, et al. Quantifying the climate effects of bioenergy— Choice of reference system. Renewable

& Sustainable Energy Reviews.

[32] Cordiner S, Manni A, Mulone V, Rocco V. Biomass pyrolysis modeling of systems at laboratory scale with experimental validation. International Journal of Numerical Methods for Heat & Fluid Flow. 2018;**28**(2):413-438

[33] Kluts IN, Brinkman MLJ, De Jong SA, Junginger HM. Biomass resources: Agriculture. Biorefineries.

[34] Liang F, Zhang T, Xiang H, et al. Pyrolysis characteristics of cellulose derived from moso bamboo and poplar. Journal of Thermal Analysis and Calorimetry. 2018;**132**:1359-1365

2018;**81**:2271-2280

2019;**166**:13-26

[30] Birdsey R, Duffy P, Smyth C, Akurz W, et al. Climate, economic, and environmental impacts of producing wood for bioenergy. Environmental

[27] Perea-Moreno MA,

2020;**29**(1):885-891

Biomass as renewable energy: Worldwide research trends. Sustainability. 2019;**11**:863-881 *Energy Potential of Biomass Sources in Slovakia DOI: http://dx.doi.org/10.5772/intechopen.91847*

*Green Energy and Environment*

[14] Lieskovský M, Gejdoš M.

in Forestry]. Zvolen: Technická univerzita vo Zvolene; 2016. p. 136

from Renewable Energy Sources in the Internal Electricity Market. Available from: https://eur-lex.europa.eu

of Wood Biomass for Energy Use in Slovakia] [Online]. Zvolen: Národné Lesnícke Centrum—Lesnícky

[21] Act No. 326/2005 Coll. on Forests. Available from: https://www.

[cited 02 December 2018]

[23] Messingerová V, Lieskovský M, Gejdoš M, Slugeň J, Mokroš M, Tomaštík J. Technika a Technologické Postupy Pri Produkcii Biomasy a Jej Energetickom

Technological Procedures in Biomass Production and its Energy Recovery]. Zvolen, Slovakia: Technická Univerzita

[24] Act No. 220/2004 Coll. on the Protection and Use of Agricultural Land and on the Amendment of Act No. 245/2003 Coll. on Integrated Pollution Prevention and Control and on Amendments and Supplements to Certain Acts. Available from: https://

[25] Oravec M, Bartko M, Slamka M. Postupy Intenzifikácie Produkcie Drevnej Biomasy na Energetické Využitie [Procedures for Intensifying Wood Biomass Production for Energy Use]. Zvolen, Slovakia: Národné Lesnícke Centrum—Lesnícky Výskumný Ústav; 2012. p. 65

Zhodnotení [Technology and

vo Zvolene; 2016. p. 101

www.zakonypreludi.sk

[22] KPMG. Kritériá Udržateľného Využívania Biomasy v Regiónoch Slovenska pre Programy SR na

Obdobie 2014-2020 Spolufinancované z EŠIF-So Zameraním na Drevnú Biomasu [Criteria for Sustainable Use of Biomass in Regions of Slovakia for 2014-2020 Programs of the SR Co-financed by the European Structural Investment Fund-Focusing on Wood Biomass] [Online]. 2016. Available from: https://www.op-kzp.sk

December 2018]

zakonypreludi.sk

Výskumný Ústav Zvolen; 2018. Available from: http://www.nlcsk.sk [cited 02

Komplexné využitie Biomasy v Lesnom hospodárstve [Complex Use of Biomass

[15] Hutňan M. Využitie fytomasy ako obnoviteľného zdroja na energetické účely [Use of phytomass as a renewable source for energy purposes]. In: Pospíšil R, editor. Využitie Biomasy z Obnoviteľných Zdrojov na Energetické Účely. Nitra: Slovenská Poľnohospodárska Univerzita v Nitre; 2012. pp. 163-170

[16] Jawaid M, Paridah M, Tahir PM, Saba N. Lignocellulosic Fibre and Biomass-Based Composite Materials— Processing, Properties and Applications. London, UK: Woodhead Publishing; 2017.

[17] Oravec M. BIOCLUS—Developing Research and Innovation Environment in five European Regions in the field of Sustainable Use of Biomass Sources (Project 245438)—Regionálna Stratégia

[18] MP SR. Green Report 2017 [Online]. 2018. Available from: http://www.mpsr. sk/en/index.php?navID=16&id=68

[19] Gejdoš M, Lieskovský M. Aké Hrozia biologické riziká Pri Dlhodobom skladovaní lesných štiepok? [What Are the Biological Risks of Long-Term Storage of Forest Chips?]. In: Správy z Výskumu Lesníckej Fakulty Pre Prax. Zvolen: Vydavateľstvo TU vo Zvolene;

[20] Oravec M, Slamka M. Výskum Potenciálu Drevnej Biomasy na Energetické Využitie v Podmienkach Slovenska [Research of the Potential

Slovenska 2010 [Online]. 2010. Available from: http://www.bioclus.eu

[cited 02 December 2018]

[cited 21 March 2019]

2017. pp. 39-44

p. 522

**134**

[26] Act No. 543/2002 Coll. on Nature and Landscape Protection. Available from: https://www.zakonypreludi.sk

[27] Perea-Moreno MA, Sameron-Manzano E, Pera-Moreno AJ. Biomass as renewable energy: Worldwide research trends. Sustainability. 2019;**11**:863-881

[28] Woch F, Hernik J, Sankowski E, Pioro P, et al. Evaluating the potential use of forest biomass for renewable energy: A case study with elements of a systems approach. Polish Journal of Environmental Studies. 2020;**29**(1):885-891

[29] Al-Ahmad H. Biotechnology for bioenergy dedicated trees: Meeting future energy demands. Zeitschrift Fur Naturforschung, Section C-A Journal of Biosciences. 2018;**73**(1-2):15-32

[30] Birdsey R, Duffy P, Smyth C, Akurz W, et al. Climate, economic, and environmental impacts of producing wood for bioenergy. Environmental Research Letters. 2018;**13**(5)

[31] Koponen K, Soimakallio S, Kline KL, Cowie A, et al. Quantifying the climate effects of bioenergy— Choice of reference system. Renewable & Sustainable Energy Reviews. 2018;**81**:2271-2280

[32] Cordiner S, Manni A, Mulone V, Rocco V. Biomass pyrolysis modeling of systems at laboratory scale with experimental validation. International Journal of Numerical Methods for Heat & Fluid Flow. 2018;**28**(2):413-438

[33] Kluts IN, Brinkman MLJ, De Jong SA, Junginger HM. Biomass resources: Agriculture. Biorefineries. 2019;**166**:13-26

[34] Liang F, Zhang T, Xiang H, et al. Pyrolysis characteristics of cellulose derived from moso bamboo and poplar. Journal of Thermal Analysis and Calorimetry. 2018;**132**:1359-1365

[35] Majlingová A, Zachar M, Lieskovský M, et al. The analysis of mass loss and activation energy of selected fast-growing tree species and energy crops using the Arrhenius equation. Acta Facultatis Xylologiae Zvolen. 2018;**60**(2):177-188

[36] Rodrigues A, Nunes LJR, Godina R, Matias JCO, et al. Correlation between chemical alterations and energetic properties in Torrefied biomass. In: 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe); 2018

[37] Yavorov N, Petrin ST, Valchev I, et al. Potential of fast-growing poplar, willow and Paulownia for bioenergy production. Bulgarian Chemical Communications. 2015;**47**(SI:A):5-9

[38] Ilavský J et al. Štúdia o Dostupných Zdrojoch Biomasy a Ich Efektívnom Zabezpečení na Výrobu Energie Vo Zvolenskej Teplárenskej a.s. [Study on Available Biomass Resources and their Effective Ensuring for Power Generation in Zvolen Heat Plant, Monograph]. 1st ed. Zvolen: Technical University in Zvolen; 2006

[39] Martinka J, Martinka F, Rantuch P, Hrušovský I, Blinová L, Balog K. Calorific value and fire risk of selected fast/growing wood species. Journal of Thermal Analysis and Calorimetry. 2018;**131**:899-906

## *Edited by Eng Hwa Yap and Andrew Huey Ping Tan*

Energy is a vital element in sustaining our modern society but the future of energy is volatile, uncertain, complex, and ambiguous; especially when facing a continuous drive to ensure a sustained and equitable access as well as mounting pressures to reduce its emissions. Traditional approaches in developing energy technologies have always been in isolation with distinct and unique contexts. However, we cannot afford to work in silos any longer. Future energy systems and their relationship with the society and the environment will have to be conceived, designed, developed, commissioned, and operated alongside and within contemporary geo-political, ethical, and socio-economic contexts. This has posed an unprecedented volatility, uncertainty, complexity, and ambiguity (VUCA), where systemic and holistic approaches are often warranted. This book aims to focus on the VUCA of addressing the future of energy and environment by considering contemporary issues and insights from diverse contexts, viewed as a system, and anchored upon emerging and smart energy technologies.

Published in London, UK © 2020 IntechOpen © barbol88 / iStock

Green Energy and Environment

Green Energy

and Environment

*Edited by Eng Hwa Yap* 

*and Andrew Huey Ping Tan*