A Review of the Application of Fuzzy Logic in Bioenergy Technology

Abstract

Although fuzzy logic is regarded as an old modelling technique, its application in recent studies cannot be overemphasised. Therefore, the study aims to provide recent developments and ideas based on the scholarly contribution from the literature on how uncertainty can be reduced and to enhance decision-making through fuzzy logic in relation to bioenergy technologies. This is necessary to address the potential of uncertainty, inherently subjective information, and handling imprecise data, as well as identifying sustainable determinants in bioenergy technologies. Fuzzy logic application is an essential modelling technique in this regard. In this paper, a review focusing on the comprehensive and detailed applications of fuzzy logic models in bioenergy technologies is presented. From the review, it is found that the integration and combination of a fuzzy logic model plus other modelling techniques provides a better performance and is known to be effective and efficient. The review demonstrates how fuzzy logic can help to manage complicated variables, thereby ultimately promoting more effective and sustainable bioenergy solutions. Hence, for maximum attention on the review, it is suitable for stakeholders, planners, and decision makers in bioenergy research and industry.

1. Introduction

The demand to replace conventional electricity generation resources, such as fossil fuels, has increased due to an increase in sustainable energy solutions [1]. This amazing expansion includes a wide range of renewable energy forms, including the key areas of solar, wind, hydro, and biofuels [2]. In comparison with fossil fuels, renewable energy sources generate low greenhouse gas emissions, minimise air pollution, and increase energy independence by using locally accessible resources [3]. Governments, members of the legislature, and international organisations are putting in more effort to encourage the development of renewable energy sources to prevent climate change, reduce dependency on fossil fuels, and achieve energy security [4]. An example of a renewable energy source as a case study for the present article is bioenergy.

Bioenergy is defined as a renewable energy derived from biomass, and hence, it represents one of the most substantial and sustainable energy sources globally [5]. It is said to fulfil the worldwide demand profiles in energy sectors such as building, electricity, and transportation. Bioenergy accounts for around 10% of the world’s total primary supply and 14% to 18% of the world’s overall renewable energy mix [6]. The application and utilisation of bioenergy results in a net-zero carbon footprint because of the emission of carbon dioxide (CO₂). Also, the burning of biomass is balanced by the CO₂ absorbed during biomass growth [7]. Bioenergy is a flexible energy option for all stages of development due to its ability to produce multiple forms of energy and chemicals. It can be dispatched to balance dynamic demands, has high integration potential with existing infrastructures, and emits less greenhouse gases than fossil fuels [8].

The modelling of bioenergy systems has recently become one of the main interests in the research of bioenergy technology. This deals with focusing on numerous models that focus on analysing the potential role of bioenergy, one or more of the key stages, and its integration into the bioenergy system [9]. Independent design and operational factors are optimised using mathematical programming to provide the optimum trade-offs and results within the limitations. A simulation-based technique enables integrated process conceptualisation using interactive mass, energy, and momentum transfer models of the underlying processes at the unit or flowsheet scale. Both approaches evaluate scenarios and assess the sensitivity of independent variables on output outcomes, including technoeconomic, environmental, or social performance [10].

However, producing bioenergy from various biomass sources is difficult, involving multiple factors and uncertainties [11]. Based on this, Lofti Zadeh introduced the notion of fuzzy sets to solve the problems associated with systems that incorporate uncertainty and ambiguity. Classic binary logic, in which statements are either true or false, proved insufficient for dealing with many real-world problems based on several possibilities. Fuzzy sets allow for degrees of membership, making it possible to more correctly model complicated systems [12]. The study of fuzzy logic paved the way for a wide range of applications, including fuzzy logic in control systems, decision making, and predictive modelling in domains such as engineering, biology, and environmental science [12]. However, in bioenergy technologies, fuzzy logic is a powerful tool for dealing with uncertainty, as well as a promising approach for improving its performance. By mimicking human reasoning, fuzzy logic can effectively address the complexities and changes in bioenergy production [13].

Due to the benefits of fuzzy logic, such as the ability to handle ambiguity and approximate reasoning, making it suited for situations where precise data may be unavailable or highly uncertain, studies have been conducted [14]. For instance, Althubaiti [15] developed a fuzzy logic controller to improve energy management in hybrid renewable energy systems (HRES) that use a variety of methods to store energy, like batteries and supercapacitors. The controller could handle the uncertainty of renewable energy production using fuzzy logic, allowing for real-time modifications in energy distribution to maintain a supply–demand balance. Stepanenko et al. [16] explored the application of fuzzy logic in decision-making when connecting renewable energy sources to the electric grid. The study concentrated on how fuzzy logic may control uncertainties in power output from renewable sources, improve grid stability, and optimise the integration process, assuring efficient and dependable coordination with existing power infrastructure. The possibility of optimising operating parameters for methane production in biomass gasification using fuzzy logic modelling was carried out by Rezk et al. [17]. The study simulates the complicated and unpredictable relationships between different process factors to increase methane yield. Mahmoudi et al. [18] studied how fuzzy logic can help with decision making in integrating renewable energy sources into the power system. They specifically investigated applying fuzzy logic to deal with uncertainties in power generation, improve stability, and maximise the coordination of renewable sources with the existing energy infrastructure [19].

2. Research Gap

The application of bioenergy systems has faced uncertainty in recent times in decision-making processes. Therefore, the need for an appropriate process in this regard for effective decision-making still emerges. Previous studies in this area have been on hybrid and experimental studies, which have proposed a variety of criteria for selection, focusing on fuzzy logic in renewable energy systems. However, an existing study on a particular renewable energy source, such as bioenergy systems, that focuses on reducing uncertainty and enhancing decision making via fuzzy logic remains unclear. Demonstrations on how fuzzy logic helps manage complicated variables in bioenergy systems, ultimately promoting more effective and sustainable bioenergy solutions, are still lacking. To the best of the authors’ knowledge, this is the first review article dealing with a particular renewable energy source (bioenergy technology) in relation to fuzzy logic application, which is published in a single paper, thereby providing concise, comprehensive, and detailed information through a thorough review process, which the study aimed to achieve. The major contributions of the review are as follows:

• To provide an extension and integration of other models with fuzzy logic to address the problems of selecting and decision making of bioenergy systems;
• The study contributes to the related body of knowledge by determining the weight of the criteria and ranking bioenergy processes through reviewing different case studies in the literature;
• It proposes and recommends a suitable fuzzy logic modelling technique to reduce the uncertainty present in bioenergy processes with reference to the literature.

The review outlines the general overview of bioenergy technology and the fuzzy logic model as a basic step for the study. Thereafter, the fuzzy logic techniques in renewable energy sources are outlined. This is then narrowed down to the studies on the application of fuzzy logic techniques in bioenergy processes, in most cases with an integration of other models. Another important area that the review will look at is the practical challenges of fuzzy logic in relation to bioenergy sources. The review concluded by providing the limitations and future direction of the study. All these were considered in the study to answer the following research questions.

• Is it possible to reduce uncertainty in the decision-making process of bioenergy systems?
• Can fuzzy logic improve the efficiency of bioenergy processes and decision making under uncertainties?
• What are the ranking criteria for bioenergy processes via fuzzy logic?

3. Methodologies

The databases used in the study were Science Direct, Scopus, Web of Science, and reports from renewable energy. The basic short phrases used to explore the information from these databases combine the same keywords using a Boolean operator and respecting each database’s criteria. Thus, the keywords used were application AND fuzzy logic technique OR model AND bioenergy AND technology OR system. These keywords or terms were used to search for the relevant literature and studies from peer-reviewed journals. Considering the search period, this was not limited, and hence, coverage was investigated from the start of the fuzzy logic model in 1965 till 30 April 2025. During this stage, original articles, review articles, and book chapters were considered in the study. Notably, the result search includes publications with the selected keywords included in the topic, abstract, contributors, and keywords.

Table 1. Selection of data based on criteria.

Study Inclusion Criteria	Study Exclusion Criteria
Scholarly published contribution in the form of original article, review paper, book chapter from peer-reviewed journals, and energy reports	Published contribution outside original article, review paper, book chapter from peer-reviewed journals and energy reports are excluded.
Time span of 1965–2025	Outside the time span of 1965–2025
Publications written in English language only are included	Publications written aside English language are excluded
The type of publication considered is review article	Not any other review as publication type (systematic review, etc.)

shows the criteria for inclusion and exclusion used in the study.

4. Bioenergy Technologies

Bioenergy technologies are critical to the shift to sustainable energy because they provide renewable alternatives to fossil fuels while also promoting waste management and carbon reduction. However, the complexity of bioenergy supply chains, which include biomass production, transportation, conversion technologies, and energy delivery, requires comprehensive modelling methodologies to optimise performance, economic viability, and environmental sustainability. The technology combines chemical and physical processes at the molecular, reactor, and system levels. A bioenergy supply chain generally consists of five primary components: biomass production, biomass logistics, biomass-to-product conversion, product distribution, and product end use. Mathematical models can provide cost-effective and powerful tools for designing and optimising bioenergy technologies. There are four fundamental types of mathematical modelling tools used in this regard, such as biomass production models, biorefinery processing models, economic models of bioenergy supply chains, and life cycle evaluation models of bioenergy systems [20].

Bioenergy technologies are further classified into thermochemical processes, biochemical processes, and other processes. These processes play an important role in improving bioenergy production efficiency [21]

4.1. Thermochemical Processes

A thermochemical process defines the conversion of biomass resources into energy using various heat-based techniques. The processes include combustion, pyrolysis, gasification, and liquefaction. Thermochemical conversion provides several benefits, such as the potential to create different energy forms (heat, electricity, and fuels), reduced GHG emissions in contrast to fossil fuels, and the ability to use various biomass sources as raw materials [22]. In this section, the study will concentrate on pyrolysis and gasification as the main examples of the thermochemical processes of bioenergy technologies.

4.2. Pyrolysis

Pyrolysis is the chemical breakdown of organic compounds at temperatures of more than 400 °C in an oxygen-free environment [23]. It decomposes biomass in the absence of oxygen, includes fast pyrolysis for high bio-oil yields of up to 75 wt% at 400–600 °C, slow pyrolysis for biochar production at ~35 wt%, catalytic pyrolysis with zeolites to enhance bio-oil quality, and microwave pyrolysis for energy efficiency. Studies have been conducted on the modelling involving pyrolysis processes. Kaczor et al. [24] investigated several computational and mathematical models for simulating waste biomass pyrolysis, with the goal of improving process efficiency and product production. The study looked at several modelling techniques, including kinetic models (lumped, distributed, and detailed mechanistic models), thermodynamic equilibrium models, and computational fluid dynamics (CFD) simulations. From the study, it was discovered that kinetic models, specifically Arrhenius-based and distributed activation energy models (DAEM), were frequently used for predicting reaction rates and product distribution, whereas CFD models were utilised to analyse heat and mass transfer effects in reactor systems. The investigation also addressed the growing use of machine-learning techniques to improve forecast accuracy. Xia et al. [25] investigated numerous modelling methodologies and validation methods for predicting the yield and composition of pyrolysis products from biomass. Kinetic models such as lumped kinetic schemes, distributed activation energy models (DAEM), and detailed reaction processes, as well as thermodynamic and computational fluid dynamics (CFD) models, were considered. Interestingly, the kinetic models proved to be particularly good in forecasting bio-oil, char, and gas yields, whereas CFD simulations revealed reactor-scale behaviour. The study emphasised the significance of experimental validation with techniques such as thermogravimetric analysis (TGA), pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS), and bench-scale reactors to ensure model accuracy. A detailed examination of several modelling methods for biomass pyrolysis was carried out by Hameed et al. [26]. The authors separated the models into three categories: kinetic, network, and mechanistic. Thereafter, the efficacy of several kinetic models, such as global single-step, multi-step, and distributed activation energy models (DAEM), which are often used to predict reaction rates and product distributions, was compared. Network models, such as lumped parameter and structural models, were discovered to efficiently represent complicated reaction networks and intermediate pathways. Despite being computationally demanding, mechanistic models provided precise explanations of fundamental chemical reaction processes. The researchers discovered that, while kinetic models were computationally efficient and extensively used, they frequently oversimplified the pyrolysis chemistry. Mechanistic models, on the other hand, gave improved precision at the expense of large processing resources. Network models strike a compromise between accounting for response complexity and staying tractable. With the combination of experimental and computational approaches, Elhenawy [27] investigated biomass pyrolysis processes using laboratory-scale experiments with various feedstocks under different temperature and heating rate conditions. The study employed the thermogravimetric analysis (TGA) to assess mass loss and gas chromatography/mass spectrometry (GC/MS) to analyse product composition. In the numerical simulations, a developed comprehensive model, integrated with kinetic and transport phenomena (computational fluid dynamics), showed a good agreement with experimental data for primary pyrolysis processes, particularly in predicting mass loss and gas evolution. However, there were differences in modelling secondary reactions and aerosol formation. Branca et al. [28] conducted a thorough evaluation of kinetic modelling methods for biomass pyrolysis, evaluating several reaction schemes such as single-step global models, multi-step parallel reaction models, and distributed activation energy models. They examined the capacity of these models to predict pyrolysis behaviour across various biomass types and operating conditions and discovered that multi-step models were more accurate for complex feedstocks, whereas DAEM efficiently represented scattered reactivity patterns. The study indicated substantial difficulties in adequately modelling secondary cracking reactions and heat transport effects, especially on larger scales. Through rigorous model validation against experimental thermogravimetric data, the authors found that integrating feedstock-specific kinetic parameters, rather than generalised values, significantly improved model accuracy.

4.3. Gasification

Gasification is a thermochemical process that converts carbonaceous materials, such as biomass, coal, or waste, into a combustible gas mixture called syngas. Syngas is primarily composed of H₂, CO, CO₂, CH₄, and small amounts of other hydrocarbons and contaminants. The feedstock is typically reacted at high temperatures (750–950 °C) with a controlled amount of oxygen, steam, or air. Unlike combustion, which completely oxidises the fuel, gasification happens in an oxygen-deficient environment, allowing for partial oxidation and thermal degradation of the feedstock into usable gaseous products [29]. Gasification, which converts biomass into syngas (CO, H₂, and CH₄) at 700–1200 °C, includes fixed-bed gasifiers (simple but prone to tar), fluidised-bed gasifiers (high efficiency and lower tar), plasma gasification (near-complete carbon conversion), and supercritical water gasification (SCWG), which is ideal for wet biomass [30]. Looking at the few studies focusing on the modelling of gasification processes, these include the following. Singh and Tirkey [31] examined the use of a biomass gasifier to process waste chicken litter pellet biomass and produce enriched hydrogen syngas. Aspen Plus was used to model the biomass gasifier, and the model was validated using four distinct agricultural waste biomass samples with the experimental results, yielding a satisfactory result. Higher temperatures of 800–900 °C and a lower equivalency ratio of 0.2–0.3 resulted in enhanced syngas composition, with H₂ and CO contents peaking at ideal circumstances. Furthermore, high moisture reduced efficiency, emphasising the importance of feedstock pre-drying. The response surface methodology (RSM)-based optimisation showed that the model could predict performance accurately, with a CGE of up to 72% under optimal conditions. The study sheds light on the sustainable use of chicken waste for energy recovery by gasification. Suparmin et al. [32] conducted an exhaustive review of gasification models. The study looked at thermodynamic equilibrium, kinetics, computational fluid dynamics (CFD), and artificial neural network (ANN) models. The study weighed the benefits and weaknesses of the four models. The key findings emphasised that equilibrium models (e.g., Gibbs free-energy minimisation) are computationally efficient but less accurate for complex reactions, whereas kinetic and CFD models provide greater precision by accounting for reaction kinetics and fluid dynamics, but need a large computational resource. Marcantonio et al. [29] evaluated the strengths and disadvantages of the most often used models in recent research, such as kinetic models, thermodynamic models, and computational fluid dynamics models, comparing them and determining when one technique is preferable to another. The study discovered that thermodynamic models (such as Gibbs minimisation) are computationally efficient but lack kinetic realism, whereas kinetic and CFD models increase accuracy at the expense of complexity and computing demand. They highlighted process simulation tools (Aspen Plus, ChemCAD) for system-level analysis, as well as new hybrid techniques that combine thermodynamics, kinetics, and machine learning to achieve balanced accuracy and scalability. The recent advances and research on modelling biomass gasification using Aspen Plus for tar production and model validation were studied by Mutlu et al. [33]. Importantly, an examination of modelling techniques, such as equilibrium, kinetic, and hybrid models, was carried out, as well as addressing the problems associated with the study, such as oversimplified processes, failure to account for tar production, and feedstock variability in relation to the study. The Aspen Plus was used in the study because of its flexibility and integration capabilities for system-scale research. Its dependence on thermodynamic equilibrium frequently results in inaccurate predictions of syngas composition and tar yields when compared to real-world conditions. The research identified areas for improvement, such as using user-defined kinetics, applying machine learning for parameter optimisation, and combining with CFD for increased fluid dynamics modelling. In another study, Pilar et al. [34] performed a thermodynamic analysis of biomass gasification using Aspen Plus, thereby comparing its performance of stoichiometric (equilibrium) and non-stoichiometric (Gibbs free-energy minimisation) models in forecasting syngas composition and process efficiency. The aim of their study was to find out how these modelling techniques differed in terms of accuracy, computational complexity, and application to different biomass feedstocks. The key findings revealed that the non-stoichiometric Gibbs model produced more realistic predictions, particularly for complex gasification conditions involving multiple simultaneous reactions, whereas the stoichiometric model was simpler but less accurate due to its reliance on predefined reactions and disregard for intermediate species. The study reported that temperature, pressure, and equivalency ratio all had an impact on model outputs, with the Gibbs model better reflecting their impacts. To provide an overview of gasification technology, Kushwah et al. [35] looked at the modelling decisions and methodologies for biomass gasification to achieve a successful biomass-to-fuel conversion. A technical explanation and critical examination of thermodynamic, kinetic, computational fluid dynamics, and data-driven methodologies are offered, along with essential modelling concerns that have not been investigated in previous works. It was demonstrated that, while computationally economical equilibrium models frequently underestimate tar production and fail to represent complicated reaction kinetics, full kinetic and CFD models give greater fidelity. However, this requires significant computing resources and experimental validation. The study emphasised the rising importance of machine learning and hybrid modelling in improving gasifier performance by combining data-driven insights with mechanical principles.

5. Biochemical Processes

Biochemical conversion of biomass is a process in which microorganisms like bacteria, fungi, or enzymes break down organic matter and turn it into useful biofuels and bioproducts, including alcohols, fatty acids, and gases. The technologies that help with this conversion include composting, which is an old aerobic method that turns biomass into biofertilizer; anaerobic digestion, which breaks down organic material in environments without oxygen to produce biogas; and fermentation, which is the process by which microbes turn sugars into liquid biofuels like ethanol [36]. In this section, attention will focus on anaerobic digestion.

Anaerobic Digestion Technology

Anaerobic digestion occurs in several stages, such as hydrolysis, acidogenesis, acetogenesis, and methanogenesis. In these stages, complex organic polymers are turned into simpler chemicals and then into biogas. The technology is critical for sustainable waste management and renewable energy generation, providing the combined benefits of lowering greenhouse gas emissions from digesting waste while also supplying a diverse energy source [37]. An example of this technology is the processing of agricultural leftovers and energy crops to produce biogas for combined heat and power (CHP) generation. Also, the digestion of organic municipal solid waste (MSW) and food waste minimises landfill emissions while simultaneously producing methane-rich biogas for renewable power generation [38]. Modelling techniques have been conducted to analyse the performance of anaerobic digestion for biogas production. Sevillano et al. [39] investigated the evolution of anaerobic digestion as a vital technology for renewable energy generation, particularly considering new uncertainties such as shifting energy costs, regulatory alterations, and feedstock unpredictability. They described how the technology evolved from a simple waste management approach to a sophisticated bioenergy system that included co-digestion, process optimisation, and circular economy concepts. The study examined numerous modelling techniques used to increase AD performance, including kinetic models such as the Anaerobic Digestion Model No. 1, which reproduced microbial processes and substrate degradation, as well as computational fluid dynamics models that optimised reactor design and mixing. Data-driven approaches, such as artificial neural networks (ANNs) and machine learning, were also used for predicting biogas output and system performance in various scenarios. It was emphasised that there is a need for adaptive modelling frameworks in addressing uncertainties in feedstock quality, economic feasibility, and regulatory changes, in so doing, assuring the viability of anaerobic digestion and sustainability as an energy source. Anaerobic digestion can serve as a biomass waste treatment technique, focusing on its biochemical processes, kinetic mechanisms, and important operational factors that influence efficiency. Evidently, this was the case in the study conducted by Aworanti et al. [40]. The authors looked at the complicated connections between microbial populations, substrate properties, and process factors, including temperature, pH, and retention time. The study employed a variety of modelling tools, such as first-order kinetic models to characterise substrate degradation and the ADM1 to simulate dynamic biochemical pathways. Also, using multivariate statistical models to evaluate the influence of operational factors. These models contributed to the understanding of the links between feedstock composition, digester performance, and biogas output. The findings emphasised the significance of optimising process parameters to increase methane output while maintaining system stability, emphasising AD as an effective but highly sensitive technique that needs accurate control and monitoring. An examination of anaerobic digestion of microalgal biomass as a sustainable strategy to increase bioenergy recovery can be carried out through mathematical modelling, thereby emphasising process optimisation and challenges related to substrate characteristics. In the study, Hasan et al. [41] looked at the process by which microalgal cell wall composition, pretreatment procedures, and co-digestion strategies affect biogas generation and methane output. With the application of kinetic models, including first-order and Monod-based techniques, the characterisation of substrate degradation rates, as well as modified versions of the ADM1, accounts for microalgae-specific biodegradability. The response surface methodology (RSM) was also used to optimise critical operating parameters such as organic loading rate (OLR), temperature, and retention time. The study revealed that pretreatment procedures greatly increased biogas output, while co-digestion with carbon-rich substrates helped balance the carbon-to-nitrogen (C/N) ratio.

6. Other Processes

Transesterification

This is the process of converting saponifiable lipids into biodiesel and glycerol using an alcohol as a suitable catalyst. During the process, the triglycerides (TG) in the oil are broken down into biodiesel or ester and other byproducts [42]. However, discussing briefly on the modelling of the transesterification process for biodiesel production, Win et al. [43] investigated the process as a crucial sustainable waste-to-energy technique for converting lipid-rich waste feedstocks into biodiesel. It is important to note that a chemical reaction occurs in which triglycerides react with alcohol in the presence of a catalyst to form fatty acid methyl esters and glycerol. The study identified key parameters that influence reaction efficiency, such as feedstock quality, alcohol-to-oil molar ratio, catalyst type and concentration, temperature, and reaction time. In the study, kinetic models, such as pseudo-first-order and pseudo-second-order reaction models, were employed to explain reaction rates, while thermodynamic analysis was utilised to determine energy needs. According to the result, while transesterification is a well-established biodiesel manufacturing technology, advances in heterogeneous catalysts, enzymatic processes, and waste-derived feedstocks should be made. This is for the long-term sustainability and economic feasibility of a circular bioeconomy framework. The modelling and optimisation of the transesterification process of shea butter via CD-BaCl-IL catalyst was studied by Nwosu-obieogu et al. [44]. The highlight from the study is the use of RSM as an optimisation technique to optimise the transesterification process, compared with the genetic algorithm (GA). Conversely, to assess the model’s capability, the artificial neural network (ANN) and the adaptive neurofuzzy inference system (ANFIS) were used. The findings from the study reported that the ANFIS had the best prediction with lower mean square error (MSE). Additionally, the combination of the ANFIS and GA gave the best optimisation, with a biodiesel yield of 96.72%, a 4 wt% catalyst concentration, and a time of 2.5 h at 70 °C temperature. Possibly, the tetramethylammonium hydroxide (TMAH) can act as a catalyst for transesterification. This was seen in the Sanek et al. [45] study on the mathematical modelling of the transesterification process kinetics. The study aimed to understand the mechanism of reaction and to identify the key variables for the optimisation of the process. From the experimental data obtained in the study, a mathematical model of a second-order reaction mechanism was developed. This was compared with experimental data in terms of the absolute and relative residues used for the mathematical model. The advantage of the proposed model has to do with the provision of parameters for the calculation of the reaction with respect to specific reaction conditions. Hence, the result is applicable for the design of an industrial reactor for transesterification. With fluorescence spectroscopy, this has been used to interpret the production of biodiesel through the modelling of transesterification reaction kinetics. The study conducted by Izida et al. [46] revealed that the fluorescence spectroscope has proven to be effective for the kinetic analysis of the transesterification reaction. The finite-element method (FEM) is one of the simulation tools for the kinetic empirical model of the transesterification reaction. This was the case in the Galvan et al. [47] study, where the simulation via FEM in relation to the simplex optimisation of the rate constants was compared with the experimental results thereafter validated. As seen previously, a second-order kinetic model was developed, using COMSOL Multiphysics software. In another study, it was seen that mathematical modelling can be used to simulate varying reaction conditions, thereby representing it as an important tool for decision making during the production and planning process. Recently, Carvalho et al. [48] developed a model for the transesterification reaction in biodiesel production from waste oil and fat in a batch reactor. The application of Scilab version 6.1.1 (software) was used for the simulation of the reaction process, as well as to model transesterification reactions, thereby predicting their results. Conditions such as the concentration of reactant, temperature, and reaction time were used and varied in the study to improve the efficiency of biodiesel production. Modelling and simulation of a temperature regulator for the transesterification process of biodiesel production was studied by Stanescu et al. [49]. The study used methanol and a homogeneous basic catalyst for this purpose. The function of the catalyst was to allow for rapid attainment of an equilibrium state, as well as not to interfere with the reaction equilibrium. Therefore, a proper temperature regulator and maintaining a constant temperature are necessary for the reaction as well as for the design of the temperature transducer. For maximum reaction rate, the study confirmed both the molar ratio and temperature as important factors.

7. General Overview of the Fuzzy Logic Model (FLM)

Fuzzy logic is defined as a method used to map vague inputs to a precise output with the involvement of linguistic rules. According to Jaber [50], fuzzy logic contributes to decision-making problems in situations where existing decision theories are not robust in a fuzzy environment. The concept of the fuzzy logic model is based on its extensive ability to map a situation that is primarily realistic [13], thereby having multiple inputs. This was introduced by Zadeh in 1965 as an extension of the classical crisp logic into a multivariate form. FLM provides flexible decision boundaries, a high tendency to adjust to a specific domain of application, thereby reflecting particularities accurately [51]. To illustrate a fuzzy logic model, assessing the response (output of the model), which is dependent on or affected by predictors or variables, to generate gas yield. In this case, the inputs are the predictors and variables as shown in Figure 1.

In another development, the approach of the FLM, according to Liu et al. [52], involves the use of fuzzy sets, which utilise the concept of membership functions to handle fuzzy relations, as well as solve different types of uncertain problems. Other fundamental elements of fuzzy logic, in addition to the membership function, are the fuzzy set, fuzzy logic operators, and fuzzy rules. By fuzzy set, it is a class with a continuum of grades of membership [53]. Fuzzy logical operators (FLO) are used to find the new fuzzy sets from existing fuzzy sets, whereas the fuzzy rules are the relationship of the system through the input and output by a conditional statement. This includes judgments from a human description [54]. Hence, some of the rules, as mentioned by Cuka and Kim [54], include:

• IF {low signal} AND {low error} THEN {tool wear is small}
• IF {medium signal} AND {low error} THEN {tool wear is medium}

In fuzzy logic, the process that mimics the human expert’s reasoning is known as a fuzzy inference system (FIS). However, it is regarded as a powerful technique used for the implementation of many industrial systems, such as microcontrollers, to improve their capabilities [55]. The practical application of fuzzy logic techniques, as mentioned in Bai et al. [56], requires the process of fuzzification, fuzzy inference, and defuzzification. In defining these terms.

• Fuzzification: This is the conversion of crisp data into fuzzy set data or, in other words, membership functions.
• Fuzzy inference process: This deals with the combination of membership functions in the presence of fuzzy control rules to arrive at a fuzzy output.
• Defuzzification: It is the reverse process of fuzzification, as mentioned earlier, which is the conversion of fuzzy output into crisp output.

These three processes are referred to as the general methodology of the workflow of fuzzy logic systems, as shown in Figure 2.

Integrating the three practical ways to apply fuzzy logic and the fuzzy logic workflow in Figure 2, it is revealed that the crisp input and output (Figure 2) are converted to fuzzy data through the process called fuzzification. However, usually, machines are known for processing crisp data, such as binary systems. Hence, this speeds up the handling of uncertain linguistic data like the ‘high’ and ‘low’. This is said to occur if the crisp input and output are transformed to linguistic variables through the fuzzy components.

For the FIS, the focus is on the fuzzy control rules, which are said to be like the inference of human beings and intuition based on the course of action. To this effect, the mean of maximum (MOM) or centre of gravity (COG) is the method employed to work out the related control output. During this process, each of the control outputs needs to be arranged into a table format known as a lookup table. Notably, the defuzzification process (step 3—refer to Figure 2) is a real-life application whereby the fuzzy control output is transformed from the linguistic variable form to a crisp variable for the purpose of performing control operations. This is necessary because, in the factual world, no crisp variable takes place based on the principle of the fuzzy system process [58].

To conclude this section, it is necessary to highlight the advantages and disadvantages of fuzzy logic.

• Advantages of fuzzy logic
  • The algorithm for FL makes use of little or estimated data. Hence, sensors or little memory are easily employed and required;
  • They have a shorter growth time than the conventional methods;
  • It provides an effective solution to complex issues and problems because of the resemblance of human reasoning and decision-making exhibited;
  • Uncertainty in engineering can be easily handled through the application of fuzzy logic;
  • FL is said to be robust with no precise input required;
  • With FL, there is compensation over pure numerical methods as a result of the frequent system information that is accessible.
• Disadvantages of fuzzy logic
  • Multiple calculations and iterations are required for fuzzification, inference, and defuzzification. This affects the performance of the system as a result of slowing down the process;
  • With a complex system or when dealing with a large dataset, FL is computationally expensive;
  • As a result of the mathematical explanation, in terms of areas of stability of control systems, proving the individuality of fuzzy systems is not easy;
  • FL may be rational when calculating time and memory. Hence, the absolute mathematical realisation can be rigorous;
  • The application of membership functions in fuzzy logic is subjective and relies on human expertise and intuition.

8. Fuzzy Logic Techniques in Renewable Energy Technologies

8.1. Multi-Criteria Decision Making (MCDM)

Kilic and Kaya [59] defined MCDM as a fuzzy logic technique that allows the most suitable alternative to be selected among the predetermined alternatives. This is conducted by identifying and selecting, as well as evaluating, the possible criteria, determining their weights and rankings using the MCDM, as shown in Figure 3. As a result of the vagueness regarding the process of decision-making, the MCDM method is best proposed with the fuzzy set to handle the situation. Studies on fuzzy MCDM are widely employed in renewable energy studies for the selection of sites for power plants, energy resource evaluation and technologies, investments in energy, and determining energy policies [60]. To be specific, fuzzy logic via MCDM is seen as the best tool to obtain more sensitive results in relation to energy policy and decision-making. For instance, the technique was used by Mamlook et al. [61] to determine the best solar energy options in Jordan. To evaluate the trigeneration system and make the best selection among them, Wang et al. [62] used the fuzzy MCDM to achieve the aim of the study. The assessment of combined cooling, heating, and power (CCHP) systems operated by natural gas, biomass energy, fuel cell, and a combined gas–steam cycle was possible through the fuzzy decision-making methodology conducted by Jing et al. [63]. In wind energy, the fuzzy MCDM was utilised for the project selection plan for a wind farm, thereby showing the effectiveness of the model through a case study [64]. With reference to certain factors (technical, environmental, economic, and sociopolitical aspects), Cutz et al. [65] proposed a fuzzy MCDM method to determine the suitable biomass conversion technologies in Central America. In Iran, the prioritisation of bioenergy technologies for sustainable energy systems was carried out by Khistandar et al. [66], using the MCDM method based on the hesitant fuzzy linguistic term set. However, the main limitation of this technique lies in some options being better against one criterion but worse against another, especially in the matrix. Also, the MCDM does not have the analytical tools to compare the impact that occurs in different years. An example of the MCDM includes the fuzzy VIKOR and fuzzy TOPSIS.

8.2. Vlekriterijumsko KOmpromisno Rangiranje (VIKOR) Method

This is the method that deals with the closeness between the evaluation value of alternatives and the ideal solution [68]. It is generally known as an important MCDM technique based on the commitment to the solution. In this case, a satisfactory solution closer to the ideal solution to the problem is hereby obtained [69]. Thus, the VIKOR is regarded as a ranking method of the MCDM used to solve complex decision problems. The method is said to perform poorly with fuzzy information, thereby ineffectively overcoming the challenges in relation to MCDM, in uncertainty problems [70]. Based on this, Ebrahimnejad et al. [71] stated that addressing this challenge requires proposing a designed extension of the fuzzy VIKOR method to handle fuzzy information in decision-making problems. With the application of VIKOR, it combines the maximum group utility and the individual regret of the opponent, thereby compromising the solution, which affects the decision maker with respect to the result. For the VIKOR method, two criteria are required. The first one deals with one group in which the criterion of the larger value indicates the alternatives with the better performance, while the second group is referred to as the smaller value, which shows the alternative with the better performance [72]. A study focusing on selecting sustainable energy conversion technologies for agricultural residues used the VIKOR method to determine the sustainability sequence of the bioenergy technologies (BETs). Despite sustainability criteria put in for bioenergy systems to enhance the making index system, the unrealistic nature of determining the most optimal bioenergy technologies is due to the conflict in accessing the criteria for BETs. Limitations still exist in this case, such as data availability, as well as difficulty in assessing the interactional effects. To address this limitation, the VIKOR method is employed to help achieve compromise solutions that assist the decision maker in making their choice [72]. Based on the performance of the integrated dimension via the VIKOR method, the authors stated that power generation from direct combustion, gasification, and briquettes is the most sustainable technology under certain conditions (environmental and economic). The strength of the VIKOR method lies in the successful ability to account for conflict and non-commensurable criteria. Another application of the VIKOR method was seen with the Pythagorean fuzzy set, which was used by Rani et al. [73] to evaluate the criteria of renewable energy in India. The findings reported the necessity of the method in selecting and accessing renewable energy technologies. Other areas of application of the VIKOR method include land use restraint strategies, evaluating bank performance, solving personnel training problems, and choosing the best web services [74].

8.3. Technical for Order of Preference by Similarity to Ideal Solution (TOPSIS)

The TOPSIS is regarded as a compromise ranking method for the purpose of selecting renewable energy projects, such as bioenergy [75]. It is a linear weighting technique proposed for the crisp version and has been widely employed in solving MCDM problems in many areas of application. Also, the method has been identified as an excellent type of MCDM technique because of the simplicity and ease of understanding it provides. Similarly to the VIKOR method, the concept of the TOPSIS is based on the chosen alternative having the shortest distance with respect to the positive ideal solution, and on the other hand, the longest distance from the negative ideal solution [76]. Basically, it deals with the aggregation function that focuses on the proximity of the reference point. Hence, it addresses the problem in relation to the MCDM, thereby considering the optimal alternatives that have the shortest distance from the ideal solution and the longest distance from the anti-ideal [69]. In Nigeria, Alao et al. [77] used the fuzzy TOPSIS method to rank and identify anaerobic digestion as the best technology for the generation of electricity from waste ahead of other technologies (pyrolysis, incineration, and landfill recovery). In a similar study, an evaluation of the potential of waste-to-energy technologies was ranked in order of preference using a fuzzy TOPSIS approach. Through this approach, Islam et al. [78] reported that gasification and co-combustion were the two main WtE technologies in Bangladesh. Due to the ambiguity, vagueness, and bias of subjective decisions of the sole application of the fuzzy AHP method, Afrane et al. [79] conducted a study to address this challenge with the aid of fuzzy TOPSIS. The study focusing on the techno-economic aspects of WtE technologies pointed out the need to consider the technologies’ environmental and social feasibility, especially for sustainability purposes. The integration of AHP with TOPSIS under a fuzzy environment for the selection of WtE technologies in Ghana was carried out by Afrane et al. [80]. With the mentioned method, the study aims to estimate the weight criteria and ranks of WtE alternatives. It was reported that technical criteria were the highest priority weight from the fuzzy AHP, whereas the ranking using TOPSIS is as follows: anaerobic digestion > gasification > pyrolysis > plasma gasification. The main limitation of the fuzzy TOPSIS is that it results in ambiguous information and undefined problems [81]. Also, handling uncertain or imprecise data is a bit challenging and difficult, especially when dealing with renewable energy options. This is because the data points used in the evaluation are subject to variability. Interestingly, renewable energy sources and technologies are best ranked under uncertainty by the employment of TOPSIS and type-2 fuzzy numbers [82].

8.4. Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL)

This is a method that provides the determination of weights of an objective function by considering how different components of it interact with each other. This is a robust method of causal analysis that allows researchers to divide the criteria involved in a system into cause-and-effect groups. In so doing, decision makers are being assisted, thereby recognising the greatest influence criteria [83]. Wang et al. [84] developed and used the interval-valued fuzzy DEMATEL to identify and prioritise municipal sewage wastewater treatment technologies. The method ranks these technologies in decreasing order of suitability as anaerobic digestion < gasification < incineration < landfill gas recovery. To identify and evaluate the best location for the installation of a biomass reactor, Jeong and Ramirez Gomez [85] employed the combination of fuzzy DEMATEL and a Geographic Information System (GIS). The result indicated that the cost of transportation, agricultural area, potential demand, and vegetation cover are the main necessary criteria for biomass reactor site selection. Mondal et al. [86] incorporated hybrid uncertainty, flexible constraints, and a convex mixture of optimistic–pessimistic areas of the decision maker. In this case, fuzzy DIMATEL and multi-choice reference point goal programming are employed to solve the generated multi-objective model to integrate a sustainable biofuel and bioenergy supply chain. Hence, in finding the best strategies and planning decision making, the authors recommend a multi-objective mixed integer programming model, which is designed by analysing the supply chain in technological, economic, and environmental aspects, with respect to handling various feedstocks, products, and supply zones.

8.5. Fuzzy Analytic Hierarchy Process (AHP)

This technique is applicable in measuring order, rank, and evaluation of decision orders. Hence, it was proposed in the year 1980 by Saaty [87]. In real-world applications, the AHP is known for solving MCDM problems. Therefore, the method through the pairwise comparison estimates the criteria weight according to Loken [88]. Mathematically, the AHP has a robust approach to transforming decision makers’ judgments into numerical results. In renewable energy technologies, AHP has been widely used for the evaluation of renewable energy resources, the assessment of energy source policy, and the investigation of lean-green implementation. Other areas of application of these techniques include the selection of renewable energy sources and the evaluation of the significance of renewable energy resources [89,90,91]. Specifically, the fuzzy AHP method was proposed by Lee et al. [92] to prioritise energy technologies in the energy market in Korea. The study was motivated by the effect of energy technologies due to the change in the price of oil. With the application of the AHP method, it was found in their study that the most preferred technology among other energy technologies was building technology, based on the hike in the oil prices. An assessment of seven different space-heating systems using the fuzzy AHP method was conducted by Jaber et al. [93]. The study, which was conducted in Jordan, revealed that the preferred and most undesirable schemes are the wind-based and electric heating systems, respectively. It was recommended that, based on the financial criteria, a renewable energy system should be preferred. Similarly, Shen et al. [94] assessed the sources of renewable energy using fuzzy AHP in relation to energy, environment, and economy (3E). From the findings of the study, hydropower, solar, and wind energy were reported as alternatives for meeting the policy goal of the 3E. To select the most suitable energy policy among predetermined alternatives, Kahraman and Kaya [95] used a fuzzy AHP method. It was mentioned in the study that wind energy was the best energy alternative with reference to technological, environmental, socio-political, and economic criteria. These are the four criteria of the case reference used in the study. The limitation of the fuzzy AHP deals with the tremendous computational requirement involved even for small problems. The technique is based on both probability and possibility measures, and finally, the methodology cannot guarantee the decision as definitely true [96]. Figure 4 shows the hierarchical structure of the AHP.

8.6. Fuzzy Analytic Network Process (ANP)

The fuzzy ANP is a method capable of connecting the dependence of one group and among different groups. Hence, it is an improvement on AHP [98], thereby overcoming its weakness through accommodating their dependence and alternative criteria. Both the AHP and ANP are measured using the pairwise comparison based on the representation of the importance matrix. In dealing with ANP, it consists of three matrices (super matrix, weighted super matrix, and limit matrix). In differentiating the fuzzy AHP and ANP, the former (AHP) is a weighting method used to evaluate criteria that do not have an internal relationship with one another, whereas the latter (ANP) aims at weighting criteria that are related to one another, thereby evaluating them in terms of their relative importance [99]. From Topaloglu’s [100] view, the AHP allows one-way relationships of units, and on the other hand, ANP gives room for more complex relationships between decision levels and features. In renewable energy research, the fuzzy ANP was used to evaluate alternative energy policies by Kabak et al. [101]. A similar study was conducted by the same author [102] with the use of the fuzzy ANP method to evaluate alternative buildings with their overall energy performance in Turkey. In the same country, Turkey, fuzzy ANP was employed by Oztaysi et al. [103] to evaluate and rank green-energy alternatives in terms of technical, environmental, and economic factors. The result of the study showed that hydropower has the highest priority, followed by geothermal and biomass sources of energy. To contribute to the existing knowledge about solar PV energy and create new knowledge, Chen and Pang [104] propose and develop a fuzzy ANP method to analyse appropriate forms of organisation. The study was necessary to critically examine the breakthrough of solar PV energy in China. From the findings of the study, it was concluded that the best performance based on the form of organisation depends on the values of the confidence level and risk index. Lee et al. [105] proposed a model to analyse strategic products for the PV silicon thin-film solar cell power industry. The study combines fuzzy ANP with interpretive structure modelling (ISM) and benefits, opportunities, costs, and risks (BOCR) methods. With studies of fuzzy ANP, Gu et al. [106] mentioned that the limitation of the method lies in its over-reliance on expert judgment and experiences, as well as the unwieldy model based on the large number of factors associated with the technique. Figure 5 shows the typical network of the AHP structure as referred to in Alptekin and Alptekin, 2018, [107].

8.7. Neuro-Fuzzy System (NFS)

The neuro-fuzzy system is defined as the combination of a fuzzy set and an adaptive neural network (ANN), which modifies the fuzzy membership function with the aid of neural networks. It is formed by an algorithm through the theory of ANN and fuzzy sets, as shown in Figure 6. The system is aimed at providing a learning method using input–output data to adjust the fuzzy system parameters. By the combination of ANN and fuzzy set, Yousefi and Hamilton-Wright [108] mentioned that it incorporates the advantages of both methods. This implies that, with neuro-fuzzy, the drawbacks or challenges present in ANN and fuzzy sets can be overcome. The main advantage of this type of technique is the flexibility of the ANN and the capability of the fuzzy reasoning while focusing on imprecise information. The techniques are applicable to intrusion detection system (IDS) problems and found in the milieu of network-based intrusion detection systems (NIDS) according to Gomeza and Dasgupta [109]. Here, through the performance of a fuzzy interface by the neural network, a multi-layer perceptron learns the fuzzy rule during the process of identifying the attacks [110,111]. Some researchers have conducted studies on neuro-fuzzy expert systems in relation to renewable energy. For instance, Dragomir et al. [112] created a knowledge-based system based on neuro-fuzzy to ensure the best use of energy from renewable energy systems. The essence of the study was to establish the application of the technique for energy scheduling in smart grids integrated with PV panels. Ulutas et al. [113] propose an algorithm for energy management based on the NFS for grid-connected microgrids. The study was designed to determine the operating state of the system, thereby comparing it with the rule-based control strategy. Similarly, the energy management strategy for a hybrid DC micro-network of PV and storage system energy (SSE) was presented using the neuro-fuzzy method. The study carried out by Ndiaye and Ngom [114] aimed at proposing an algorithm based on the technique to ensure good management to optimise the energy flow and protect batteries against overloading. One limitation of the NFS is that it can be slow because the system must perform both ANN and fuzzy logic. This might take a lot of time, depending on the size and complexity of the data.

8.8. Fuzzy C-Means (FCM)

This is a clustering method that uses fuzzy membership to allocate a degree of membership for every cluster. According to Nie et al. [115], the method usually employs the use of an alternating optimisation algorithm to update the membership and cluster centre matrix. Hence, its structural algorithm is like that of K-means clustering [116]. Conversely, it is interesting to note that the results from FCM studies are achieved through the process of clustering tasks. Although due to the many constraints, it is inconvenient to directly optimise and converge to a suboptimal local minimum. This tends to affect the clustering performance of the method or technique [117]. Considering Guo and Yang’s [117] study on the utilisation of the FCM clustering method, a fault detection approach for grounding distribution systems was designed. The dataset used in the study was generated using the model from the simulation of the grounding mathematical distribution. In understanding the real problem using the FCM, Pimentel et al. [118] mentioned that the better the clustering quality of the partition, the better the interpretation of the data. Fuzzy C-means was used to analyse the clustering to determine the suitable site for the installation of solar power plants on unproductive areas. The study, which consisted of the pre-processing of data, a hybrid fuzzy c-mean algorithm, initialised differential evolution, genetic algorithm, and particle swarm optimisation, was conducted by de Barros et al. [119]. Therefore, the implementation of solar power plants requires the revitalisation of unproductive land by considering the social, economic, and environmental gains. Gomez and Casanovas [120] developed a fuzzy clustering model of solar irradiance on the inclined surface. The model is said to consist of concepts from earlier models, thereby considering the non-disjunctive sky categories. By the application of fuzzy clustering in the study, the number and definition of sky categories were optimised, respectively. A fuzzy C-means clustering algorithm for the hourly solar classification for PV system sizing was conducted by Benmouiza et al. [121]. The study was conducted to address the complexity of obtaining the optimum sizing and lowest cost of stand-alone PV systems, mostly on an hourly scale. It was reported that the daily solar radiation scale performs better than the sizing in the hourly solar radiation scale. Generally, the sensitivity to the initialisation and falling into a local minimum tends to be one of the difficulties and limitations of the FCM.

8.9. Adaptive Neuro-Fuzzy Inference System (ANFIS)

Jang [122] developed the ANFIS as a new system that consists of an artificial neural network (ANN) and a fuzzy inference system (FIS). Its application as a predictive model in real-world data makes use of rules and input parameters from the actual data. Figure 7 shows the single-input single-output (SISO) network for the ANFIS. Using a suitable and appropriate membership function, the input parameters are calculated from fuzzy data inputs. The use of ANFIS parameters (premise and conclusion) can be adjusted, as well as obtaining the IF–Then rules. The IF–Then rules represent the relationship of the local input and output of a non-linear system [51]. Numerous pieces of information can be analysed and handled using ANFIS, thereby achieving self-learning properties. Interestingly, the ANFIS models have been widely used by various scholars, such as Kassem et al. [123], Akkaya [124], and Fajobi et al. [125], as a predictive model in renewable energy, especially in biomass and bioenergy research. In renewable energy research, the ANFIS technique has been widely studied. Few of these studies include the Adedeji et al. [126] publication, dealing with the application of ANFIS as a standalone modelling and optimisation tool used in renewable energy systems and water resources. The model provided a derivative, hybrid, or heuristic algorithm in both research fields. Elena Dragomir et al. [127] developed the ANFIS as a strategy and modelling tool for predicting and controlling the production of energy from renewable energy. The authors consider changing the prediction time horizon and shape of the membership functions as the built scenario in the study. Having established that, the ANFIS model was tested and evaluated. To increase the performance of PV panels, Amara et al. [128] developed an ANFIS technique focusing on the algorithm of maximum power point tracking with a PI controller. The study employed the mathematical principles of ANFIS, which were presented and developed in the MATLAB/Simulink environment. A prediction system of solar energy radiation in the tropical region was developed using ANFIS by Lestari et al. [129]. In the study, the used predictor (input parameters) was the weather factor (temperature, precipitation, wind speed data, and relative humidity), which was built by using soft computing. The limitation of the ANFIS techniques is their high computational expense, loss of interpretability in larger units, and selection of a suitable membership function. Thus, the trade-off between accuracy and interpretability is also referred to as one of the limitations of ANFIS [130].

9. Studies on the Application of Fuzzy Logic in Bioenergy Technologies

In Jurado and Saenz [131], the application of a neuro-fuzzy controller for the purpose of a wind–diesel system was proposed with the use of biomass. The system was designed to be composed of a stall-regulated wind turbine involving an induction generator, which is said to be connected to an AC bus bar parallel with a diesel generator having a synchronous generator. With the use of a biomass gasifier, the gaseous fuel from wood chips was introduced into a diesel engine. According to Soliman et al. [132], the neuro-fuzzy models are employed for the simulation, prediction, control, and diagnosis of energy systems. However, the study used the proportional integral derivative (PID) controller and neuro-fuzzy logic controller as the control configuration of the system. Based on the simulation, it was established that the robustness of the hybrid or tuning neuro-fuzzy logic controller performs better and is superior to the linear PID controller in a wind–diesel system. This is attributed to the fact that the hybrid or tuning neuro-fuzzy logic controller can be achieved under a wide range of operating conditions.

In a similar study, a neuro-fuzzy controller was proposed for the gas turbine in a biomass-based (olive-grove) electric power plant. The study was conducted by Jurado et al. [133]. The gas turbine controller was designed in such a way that it regulates both the gas turbine and the gas turbine generator. On the other hand, the development of the two fuzzy logic controllers was conducted using speed and mechanical power derivations, whereas the neural network has the capacity to tune gains of the fuzzy logic controller. This was possible based on the operating conditions of the biomass-based electric power plant used in the study. Through the tuning of the fuzzy controller by the application of a neural network, optimum responses are achieved according to Jurado et al. [133].

The fuzzy C-means clustering and decision trees are a type of MATLAB module for applying data-mining methods. It is used for the storage location and conversion of bioenergy reactors, which focus on a simulation and optimisation model of bioenergy generation. With the use of genetic algorithms, fuzzy C-means clustering, and decision trees, Ayoub et al. [134] conducted a study on the two-level decision system for the efficient planning and production of bioenergy. Based on this, the general bioenergy decision system (gBEDS) was revealed as an effective tool used in planning for bioenergy production. This is necessary for the stakeholders and planners of bioenergy production while dealing with the biomass supply chain. The findings from the study show that the genetic algorithms, fuzzy C-means clustering, and decision trees (data-mining techniques) could determine the optimal size and location of the reactor plant, thereby simulating models that evaluate the supply chain through a user interface from a technical and economic aspect.

To address the challenges associated with the expansion of isolated electrical systems from firewood and diesel fuel, Bitar et al. [135] used an adopted fuzzy multi-objective mathematical programming. The essence was to evaluate the thermoelectric power expansion from firewood and diesel fuel, integrated with cost variations, the emission of CO₂, and the number of direct jobs (NDJ) produced from these technologies. In addition to that, the study aimed to identify the optimal level of the production of energy from different energy sources. The mathematical tool used in the study helps to facilitate the evaluation of various scenarios, which are required to meet the demand of isolated electrical systems used by decision makers. One of the challenges is referred to as a multi-objective analysis. Interestingly, the tools used for fuzzy multi-objective or multi-criteria problems deal with the potential of uncertainty and inherently subjective information, which is beneficial to the scientific community. From the study, the proposed model was able to meet the energetic planning of isolated electric systems. However, the traditional models are said to be limited based on the choices of ignoring externality, inherent uncertainties, and subjectivity in relation to process dynamics.

The optimisation of biomass boilers cleaning and maximising heat transfer over time was developed by Romeo and Gareta [136] using artificial neural networks (ANN) and fuzzy logic expert systems (FLES). In defining both techniques, which are referred to as artificial intelligence (AI), the ANN is a simulation tool that emulates human behaviour based on biological functions, while the FLES is a control strategy that has the tendency to arrive at a conclusion based on non-precise data input [137]. Both techniques are known for the simulation, prediction, and control of biomass boiler fouling and are referred to as hybrid system design. The study was conducted to address the challenges associated with the combustion of biomass in industries. This deals with the fouling tendency and its effect on the performance of the boiler. The results from the optimisation of the soot-blowing schedule revealed a savings of up to 12 GWh/year for the biomass boiler and an average increase of turbine output of 3.5% to produce steam generation. From the findings of the study, the proposed techniques implement and reduce the effect of fouling on biomass, as well as the continuous support of operation and optimisation in relation to biomass boilers.

Various empirical techniques (fuzzy logic, conjoint, logits, and path analysis) are essential for the integration assessment of trade-offs, as well as the development of a bioenergy pathway. To illustrate this, Acosta-Michlik et al. [138] carried out a study on the integrated assessment of sustainability trade-off and the pathway for the global production of bioenergy. In the study, the authors defined fuzzy logic techniques to generate indices of sustainability based on a wide range of qualitative and quantitative indicators. For conjoint techniques, it is applicable to assess sustainability, which does not consider the array of information in temporal and spatial data. Finally, the logit techniques have the tendency to estimate the potential of bioenergy, which is dependent upon temporal indicators and land use maps. It is established in the study that the integration of these techniques provides an enormous amount of data, which includes qualitative, quantitative, temporal, and spatial as well as dilemmas and opportunities in the production of bioenergy. This is important in decision-making, especially in finding interconnections and measuring data. Although the technique might be time-consuming, the model was useful in the production of bioenergy, especially in identifying sustainable determinants.

To explore the conversion of biomass energy among microbes, Cheng et al. [139] used a fuzzy C-means algorithm as a data-mining method. The overall aim of the study was to provide a classification method for biomass energy that is generally acceptable. In the study, 27 different kinds of Archaea (microorganisms) were classified and analysed using the fuzzy C-means (FCM) algorithm. The performance analysis of the FCM was conducted with the use of MATLAB 6.5. However, Archaea were employed as a suitable microorganism because of their contribution to biomass energy development. Secondly, it was observed that the microorganism has the capacity to survive in an ammonia-oxidising environment, as well as being able to release energy by genetic metabolism. By genetic metabolism, the authors used “codon usage bias” involving three amino acids (Leucine, Serine, and Arginine) as the cluster analysis source. With the 27 kinds of Archaea, it was reported that No. 15 (Picrophilus torridus strain DSM9790), No. 21 (Thermoplasma volcanium strain GSS1), and No. 23 (Pyrobaculum aerophilum strain IM2) were found to be potential Archaea by the fuzzy C-means method for the conversion of biomass. Additionally, due to the same Genus species, Nos. 15, 21, and 23 of the different Archaea were seen to have a significant correlation with biological classification.

Tan et al. [140] presented a multi-region, fuzzy logic input–output optimisation model that reflects the production and consumption of bioenergy under environmental footprint (carbon, water, and land resources) constraints. The study was conducted in response to the clear trend of the reported increased biomass trade in the literature, resulting from regional imbalances between the production of bioenergy demand and capacity. Interestingly, the study is an extension of the research carried out by Tan et al. [141] and Tan et al. [142], which focuses on the trade effect accounts integrated into standard IOA models, according to Wiedmann [143]. With the developed model, local production deficits or surpluses are offset, as well as allowing trade to take place within a defined system of different regions. This gives rise to the importation of additional biofuel from external sources. The result of the study provides a linear programming model, which helps to determine the generation of electricity from ethanol and biomass. From the study, the model determines the optimal level of the feedstock production and trade between regions and imports from external sources.

One of the applications of fuzzy logic theory is being used as an assessment tool for the performance of biogas digesters, as well as biomass technologies. This was evidently seen in Djatkov et al.’s [144] study on the assessment performance of an agricultural biogas digester. The study reported four assessment aspects necessary for the performance of biogas digesters. These include the production of biogas, biogas utilisation, and socio-economic and environmental impacts. The application of fuzzy set theory and mathematics enables users to handle imprecise and uncertain data. To test the reliability of the model, data from ten biogas digesters were collected and tested, as well as existing biogas technology in Germany. Conversely, it was observed that the assessment of biogas digesters indicates that biogas utilisation is seen to have the highest potential for performance improvement, thereby increasing the heat’s external utilisation. The method can be applied and is also acceptable in any geographical region in relation to biogas technology and biogas digester assessment. To conclude the study, the authors provided a recommendation mentioning the need to develop a method that can handle compensation between individual criteria as future research, thereby focusing on the definition and inclusion of more assessment criteria.

Fuzzy multi-actor multi-criteria decision making (FMCDM) can be used for sustainability assessment of biomass-based technologies to produce hydrogen. This is the case in the Ren et al. [145] publication. The study was motivated to address the issue of uncertainties and imprecision, particularly as it pertains to the sustainability assessment of biomass technologies, which deals with qualitative criteria, imprecise information, and uncertainty factors. To this effect, FMCDM is known to allow stakeholders and decision makers to participate in decision making, thereby giving an independent evaluation in the form of brainstorming. Additionally, based on the brainstorming feature of the method in terms of considering preferences and experiences of the decision maker, which reflect on collective wisdom, the proposed FMCDM is said to be more reliable. Further, through the method, it is easier for decision makers to give their evaluation, in terms of qualitative criteria, imprecise information, and uncertainty factors. As part of the recommendation of the study, the authors mentioned that the FMCDM method has been used in biomass-based technologies (pyrolysis, conventional gasification, supercritical water gasification, and fermentative hydrogen production). However, biomass gasification has been seen to be the most sustainable scenario and option in this regard. Hence, further development and studies are recommended.

In the United States, Lewis et al. [146] published an article on the fuzzy logic-based spatial suitability model for drought-tolerant switch grass. The study was conducted to show that a fuzzy logic model can be used to assess the profitability of cultivating crops to produce bioenergy. In the study, the authors employed the spatial suitability modelling method, which involves fuzzy logic using both physical and economic variables. To achieve this, several fuzzy overlay techniques were assessed to identify and synthesise the suitability criteria between trade-offs. It is worth mentioning that fuzzy spatial suitability models provide a framework that supports the uncertainty of where the threshold of suitability falls based on the continuous landscape. Based on Jiang and Eastman’s [147] assertion, fuzzy logic provides more flexible classifications of suitability. The result of their research showed that 80% of the suitable land in Kansas is within a dryness index equivalent to about four or one 22- and 45-day stretch, respectively. On the other hand, the GAMMA fuzzy overlay was the best at recognising trade-offs between a combination of multiple criteria. From the study, a guide regarding research on drought-tolerant varieties of switch grass was obtained.

The design and management of anaerobic digestion, focusing on the biomass-to-energy supply chain, was developed using a fuzzy multi-objective mixed-integer linear programming model (MILP). The study was carried out by Balaman and Selim [148] with the objective of developing a decision support system (DSS) that is effective and environmentally friendly regarding the topic by tackling inherent uncertainties. To achieve this aim, an experiment was performed in the real world to explore the viability of the MILP model. With the application of the MILP model, similar studies have been conducted by researchers. For instance, Perez–Fortes et al. [149] used the model to support designing and planning biomass-based supply chains. The model was used to minimise the environmental impact associated with the palm oil-based regional supply chain [150]. Finally, Eksioglu et al. [151] employed the model with scenario generation that can be used to design a supply chain and manage the logistics of a biorefinery. From Balaman and Selim’s [148] study, it was found that the DSS can be effectively used in practice. Hence, considering the benefits associated with economic and environmental aspects in relation to biomass-to-energy systems can be significant. Interestingly, the DSS is said to be applicable in other countries for the design of biomass-to-energy supply chains by investors. In bioenergy industries, the system is profitable and eco-friendly, thereby facilitating identification and supporting policies. Through the MILP model, it is capable of handling different varieties of feedstock, as well as helping in arriving at a feasible solution based on the various scenarios, with the inclusion of fuzzy logic.

Having looked at the previous studies of the different fuzzy logic techniques’ application to bioenergy processes,

Table 2. Summary comparison of fuzzy logic techniques with their respective application type and findings.

Fuzzy Logic Techniques/Model	Types of Applications	Main Findings	References
ANFIS	Biomass/Pyrolysis	Suitable for predicting pyrolysis outcome with imprecise data	[152]
Multilayer perceptron neural network	Biochar	High R2 of 0.984, provides accurate predictions	[153]
ANFIS + K-means clustering	Co-pyrolysis of biomass	High energy yield (19.8 Mg/kg) and reduced emissions by 82.4%	[154]
Neural network + FL	Anaerobic digestion	Reduces complexity and improves gas production efficiency	[155]
RSM/ANN/ANFIS	Anaerobic digestion	0.8841 and 0.9402 for MSE and RMSE; R2 = 0.9995 and 0.9998	[156]
Fuzzy logic controller	Anaerobic digestion	Biogas electrical power output increased by 3.65 kWh, 45.6% increase because of the introduction of fuzzy logic controller	[157]
FNN	Biomass gasification and pyrolysis	Enhances prediction accuracy and managed efficient process uncertainty	[158]
ANFIS	Pyrolysis	Highly predictive ability. 91.82% and 97.29% of pyrolysis reactions of P. Pinnata and J. Curcas	[14]
Neuro-fuzzy expert	Biomass boiler	Optimises soot blowing, saving up to 12 GWh/year of biomass boiler. Increases turbine output by 3.5%	[136]
Multiobjective linear programming (MILP)	Anaerobic digestion	Proposed model can effectively be used in practice	[148]

presents a comparison summary detailing the type of applications and findings from the various literature.

Notably, fuzzy logic has been seen and used as an effective and realistic energy modelling tool. Therefore, the fuzzy logic model has a wide scope of techniques, essentially for the optimal planning (economic, environmental, geographical, and climatic conditions) of resources. Conversely, it is possible to integrate techniques such as a genetic algorithm (GA) with fuzzy logic for the purpose of predicting and solving optimisation problems in relation to bioenergy. To conclude this section, it is necessary to briefly summarise the advantages and limitations of the fuzzy logic techniques in relation to bioenergy processes. This is presented in

Table 3. Advantages and limitations of fuzzy logic techniques in relation to bioenergy systems.

Advantages of Fuzzy Logic Technique to Bioenergy Systems	Limitations of Fuzzy Logic to Bioenergy Systems
It provides and improves adaptability and accuracy in modelling non-linear systems for biomass conversion processes.	The FL may require extensive or large dataset for optimisation and complex model.
The FL can optimise and predict the performance of bioenergy system.	The technique is usually limited to specific variables such as temperature, pH, and feedstock properties in relation biomass and anaerobic digestion characterisation.
The technique plays a role in advancing decision-making framework under uncertainties, thereby improving process efficiency.	Fuzzy logic lacks transparency in decision model, which results in hindrance to its adoption in the bioenergy industries.
Characterisation of bioenergy involves incorporation of imprecise data, vague and linguistic information. With fuzzy logic, these concerns are addressed.	Fuzzy logic models trained on a narrow dataset are impossible to generalise to new conditions. Hence, this results in decreased performance and accuracy of bioenergy data.
The technique has the tendency to map input (temperature, biomass composition) to output (syngas composition) variable to reflect inherent uncertainties in the biomass system.	The technique is said to function as a “black box”, limiting interpretability.
Ability to learn from bioenergy data and adjust model parameters based on empirical observation.
The FL has the tendency to integrate expert knowledge and data-driven insight in bioenergy processing systems, specifically as regards prediction accuracy and better generalisation, as well as decision making.

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10. Practical Challenges of Fuzzy Logic in Relation to Bioenergy Systems

According to Bukhtoyarov et al. [158], one of the main challenges of fuzzy logic application in bioenergy systems deals with the quality and data availability for training the model. For instance, a study conducted on the employment of the ANFIS model trained on a dataset from the biomass pyrolysis of Reed Canary grass revealed high accuracy in predicting the outcomes of pyrolysis. Bukhtoyarov et al. [158] mentioned that, for the model to be effective, it requires the consistency and detailed availability of the training data. In the real-world scenario, lacking accurate and high-quality data from bioenergy experimental conditions leads to reduced predictive accuracy because of the compromise of the fuzzy logic model. Therefore, the capacity of the fuzzy logic application to provide reliable predictions is affected by the scarcity of data.

The composition of bioenergy in terms of complexity further affects the accuracy of its modelling. Biomass feedstocks are said by nature to be highly variable with characteristics (chemical and physical) that differ from one type of biomass to another. In the study, it is evident that fuzzy logic can handle the non-linearity and uncertainty inherent in biomass pyrolysis. However, the diverse chemical interactions of more complex biomass are said to be difficult to model using a single set of parameters. Despite the robust performance associated with the fuzzy logic model, there still exist discrepancies in the prediction results because of the underlying complexity of the biomass chemistry.

With the involvement of a large dataset, the training of fuzzy logic incorporated with optimisation tools is said to be computationally intensive. A case study was seen in Aghbashloa et al. [159]. Although at a longer training time, the fuzzy logic in the form of the ANFIS model was integrated with the PSO, thereby providing an accurate prediction. On the other hand, the incorporation of a fuzzy logic model with advanced techniques increases the model complexity and requires the deployment of computational resources.

11. Conclusions, Limitations, and Future Direction

Though studies on the application of fuzzy logic focus generally on experimental studies of renewable energy sources, providing detailed and comprehensive information on a particular renewable energy source is still lacking. With that in mind, this review highlights the advancements in bioenergy technology, emphasising the use of fuzzy logic techniques to address the challenges associated with the complexity, handling of imprecision and uncertainty, as well as inherently structured information. Though an old modelling technique, its application is still useful and beneficial. Key studies have found and showcase the robustness, effectiveness, and efficiency of integrating fuzzy logic models with other modelling techniques, thereby arriving at and having a better performance. Additionally, the review highlights the roles and contributions of each of the fuzzy logic-based techniques (NFS, ANFIS, fuzzy optimisation, clustering, fuzzy ANP, and AHP) in bioenergy production while dealing with the biomass supply chain. These techniques not only solve optimisation problems and enhance the predictive capabilities but also identify sustainable determinants of bioenergy systems. Despite the advantages brought about by the fuzzy logic application on bioenergy systems, the study has some limitations, which can be addressed in further studies. First, the question remains how dependable, reliable, and accurate the data used in the study are. The various studies used in the review were submitted to a peer-reviewed journal, which is the primary source of the data. Hence, this process passed through the editorial and peer review processes. However, the manual and software usage that might result in ambiguity of the results or data cannot be ignored. Therefore, future studies should focus on the fuzzy logic-based method and the statistical mean in gathering data. Due to the complexity and in-depth understanding of fuzzy logic models, for instance in bioenergy production to achieve a desired optimum level in terms of sales, future studies should consider the various bioenergy models that will result in the desired optimal level, especially as it concerns the supply chain, possibly with multiple biomasses and products. Additionally, further studies need to be conducted that focus on determining the best-suited hybrid fuzzy model in studies related to bioenergy and biomass. Also, having seen the performance and effectiveness of fuzzy logic, in terms of predictive models, on the other hand, forecasting models using fuzzy logic need to be carried out in the same study. These areas of consideration for the future will attempt to provide greater efficiency, scalability, and sustainability of bioenergy production. The improvement of the interpretability of fuzzy logic models will be an interesting area to consider for future study. This is necessary for the needs of transparency in decision-making. Conducting such research will enhance and encourage developing methods, thereby making the models more explainable, which is of importance. Hence, a sensitivity analysis and other visualisation tools can assist in understanding the relationship between inputs and outputs. By so doing, the models are said to be trusted and reliable and, therefore, can be used in the industry. A future study focusing on the different bioenergy systems via the application of fuzzy logic, such as biofuel production, will be recommended for a more robust framework. This is necessary, as expanding the range of biomass feedstock in terms of modelling will contribute to and enhance the scalability and generalisation of fuzzy logic applications.