Artificial Intelligence in Biofuels: Progress, Trends, and Directions

ABSTRACT

Biofuels have the potential to improve the sustainability of transportation fuels. Production systems for biofuels are complex, and data‐driven artificial intelligence (AI) modeling offers advanced capabilities for prediction, optimization, and quality control. However, large‐scale applications of AI in biofuels production remain in their early stages compared to laboratory research. This article discusses how AI‐based modeling is used for various bioconversion technologies in the effort to improve their efficiency, dependability, and management of biofuels. Furthermore, the applications of AI technology in various types of biofuels, including biodiesel, bioethanol, biobutanol, biomethanol, biohydrogen, biogas, and algal biofuels, are critically discussed. The benefits and drawbacks of applying AI‐based modeling to manage, optimize, control, monitor, and predict biofuels yields are comprehensively investigated. Additionally, the use of fuzzy logic, genetic algorithms (GAs), artificial neural networks (ANNs), expert systems (ES), adaptive neuro‐fuzzy inference system (ANFIS), hybrid AI techniques, and other AI‐based methods to increase biofuels production yield and quality, as well as end‐user system performance, are thoroughly reviewed. Finally, a concise summary of the present state of research milestones is provided, and most recent state‐of‐the‐art studies are discussed. The technology readiness level analysis indicates that integrating AI with established technologies is the most effective commercial approach.

AI techniques are applied across various stages of biofuel production to improve prediction accuracy and process optimization. These methods have been utilized for a wide range of biofuel types, including biodiesel, bioethanol, biobutanol, biomethanol, biohydrogen, algal biofuels, biogas, and biomethane systems. The graphical abstract provides a general overview of AI applications in biofuel production.

Abbreviations

ACO

ant colony optimization

AI

artificial intelligence

AL

active learning

ANFIS

adaptive neuro‐fuzzy inference system

ANN

artificial neural network

BRANN

Bayesian regularized artificial neural network

CAGR

compound annual growth rate

CNN

convolutional neural network

DL

deep learning

DT

decision tree

DOE

design of experiments

ES

expert systems

FAME

fatty acid methyl ester

FNN

forward neural network

GA

genetic algorithm

GBDT

gradient boosting decision tree

IoT

internet of things

LCA

life cycle assessment

MAE

mean absolute error

ML

machine learning

MLDPAF

machine learning‐driven predictive analytic framework

MLPNN

multilayer perceptron neural network

OVAT

one‐variable‐at‐a‐time

PI

process intensification

PSO

particle swarm optimization

RBFN

Radial‐Basis functional network

RF

random forest

RMSE

root mean square error

RNN

recurrent neural network

RSM

response surface methodology

R ²

coefficient of determination

SHAP

Shapley additive explanations

SVM

support vector machine

TGs

triglycerides

TRL

technology readiness level

XAI

explainable artificial intelligence

XGB

extreme gradient boosting

1. Introduction

The adverse ecological impacts of fossil fuels, energy dependence on foreign countries [1], environmental sustainability concerns and the release of greenhouse gases (GHGs) by fossil fuels [2], economic stability, and national security have triggered the necessity for development of alternative energy sources [3]. The global community is currently largely dependent on nonrenewable energy sources, and the level of consumption continues to rise steadily [4]. Biofuels derived from plant biomass can be used as a replacement for fossil fuels in the production of various types of energy such as domestic and commercial power [5], transportation [6], and heating [7]. This is due to their greater ecological advantages, accessibility, lower emissions during combustion compared to that of fossil fuels, renewability, and sustainability [8]. Biofuels have also been demonstrated to be safer to handle than petroleum fuel due to their low volatility [9]. By utilizing native energy crops, biofuels can help to improve energy security [10].

Global energy demand is projected to increase by 28% between 2015 and 2040. Renewable energy is expected to grow at the fastest rate and its consumption rising at a rate of about 2.3% per year [11]. Furthermore, biofuels are expected to have a market share of $330.21 billion by 2030, representing a compound annual growth rate (CAGR) of about 10.14% from 2022 to 2030 [12]. According to studies, annual global primary production of biomass is 4500 × 10¹⁸ J [13], and only about 12.37% of it could cover world's energy needs, which was 556.63 × 10¹⁸ J as of 2020. Thus, to fulfill global energy demands, maintain a sustainable environment, and reduce carbon footprint, production of renewable energy resources such as biofuels is crucial [14]. Biofuels have a closed carbon cycle considering that carbon dioxide produced during combustion of biofuels is used by energy crops during photosynthesis. Plants mainly capture energy from the sun by a process called photosynthesis and store it in the form of carbohydrates. Biofuels utilize green plants and other plant residues to generate energy in the form of gaseous liquid or solid fuels [4, 6, 9, 14]. Figure 1 shows an overview of four generations of biofuels classified according to feedstock type and production approach. First‐generation biofuels are derived from edible crops such as sugarcane, sugar beet, corn, sorghum, soybean, and canola, as well as animal fats [4]. Second‐generation biofuels utilize nonfood biomass, including agricultural residues, wood materials, grasses, Jatropha, waste vegetable oil, and municipal solid waste [4]. Third‐generation biofuels are produced from high‐yield aquatic biomass, primarily microalgae and seaweed, through biotransformation processes. Fourth‐generation biofuels are still in the developmental stage. They employ advanced biotechnological strategies such as genetically engineered microorganisms and metabolic engineering to capture atmospheric CO₂ and achieve complete conversion of substrates into fuel products [4].

FIGURE 1.

Four generations of biofuels. First generation: Edible crops (e.g., sugarcane, corn, soybean) and animal fats. Second generation: Nonfood biomass and residues (e.g., straw, wood, grass, waste oils). Third generation: Microalgae and seaweed‐based biofuels. Fourth generation: Advanced biotechnology‐driven systems using engineered microorganisms to capture CO₂ and fully convert substrates into fuels.

Depending on the type of biomass, various conversion techniques can be used to produce biofuels. Biodiesel, bioethanol, biomethanol, biobutanol, biogas or biomethane, and biohydrogen are some of the most common biofuels [4, 6, 9, 14]. Primary bio‐alcohols, such as methanol, ethanol, propanol, and butanol, have contributed much to the biofuels sector [15]. Cellulosic biofuels are preferred for bio‐alcohol production due to their countless benefits. Economic advantages and available biomass resources favor the development of bio‐alcohols.

There are two main processes by which biomass can be converted into biofuels: biochemical and thermochemical conversions. Biochemical conversion produces liquid and gaseous fuels such as biodiesel, bioethanol, and biogas. Meanwhile, thermochemical conversion produces gaseous, liquid, and solid products, including syngas and charcoal. With the advancement of technology, most industrial processes are moving toward automation, which boosts the process's operating speed and efficiency.

Advances in artificial intelligence (AI) have reduced the amount of manual computing required. AI systems seek to grasp human thinking to develop smart machines that can solve some complex issues. The ability of machines, mainly computers, to perform activities by mimicking human intelligence has been significantly improved in recent years. Technological breakthroughs have also aided the development of computerized machines capable of performing hundreds of calculations per second, something that the human brain may not be capable of. AI is based on neural, evolutionary, and statistical learning theories, among which neural learning theory is the most common. AI has been adopted in many industrial applications to daily life tasks and titled as a promising leap toward modern digital technology. AI is also being used in the biofuels sector for upgrading production techniques, management of the demand and supply of biofuels, and data generation. Furthermore, AI provided various advanced mechanisms for the efficient production of biofuels with optimal utilization of natural resources, and modeling and optimization of biofuels production and biological systems [16].

The use of biofuels is considerably below its potential and has been limited on an industrial scale due to feedstock variability, supply chain reliability, low yield, low conversion efficiency, conversion process economics, and high cost. To overcome these obstacles, several AI techniques have been adopted to enhance the efficiency of biofuels systems as well as to address the challenges related to biofuels research.

The production of biofuels has also been improved by using various modeling methods and process optimization strategies [17]. For instance, biohydrogen production was enhanced 85% by using response surface methodology (RSM) when compared to that of the previously attained [18]. Different approaches have been used for the optimization and modeling of biological processes, including factorial design of experiments (DOE), one‐variable‐at‐a‐time (OVAT), and RSM. Although these approaches are widely used but have many limitations, the factorial DOE approach is labor‐intensive, time‐consuming, and resource demanding. OVAT also ignores the interactive output of parameters that is the major limitation of this approach [19]. RSM neglects the less significant parameters and their interactive results on biological processes [20].

AI studies are mainly characterized based on their input and output variables, size of datasets, AI techniques/algorithms, and execution [10, 21]. Moreover, AI consists of five major branches, which are given as fuzzy logic, genetic algorithm (GA), artificial neural network (ANN), expert systems (ES), and different hybrid systems [22].

AI‐based modeling helps in generating datasets with the least uncertainties that are difficult to measure and need a lot of human effort. Therefore, it aids in improving the traditional biomass conversion models and end‐use biofuel models, along with addressing the challenges related to computing techniques in biofuel supply chain design.

AI is primarily used in four areas in biofuels: (1) predicting the properties of biomass, (2) predicting the performance of biomass conversion processes, including the optimal technologies and conversion pathways, (3) predicting the properties of produced biofuels and their performance, and (4) supply chain optimization and modeling. AI‐based modeling aids in the generation of datasets with least uncertainty that are difficult to measure and require a lot of human effort. As a result, it aids in the improvement of traditional biomass conversion models and end‐use biofuel models, as well as addressing the challenges related to computing techniques in biofuel supply chain design.

AI can be implemented by utilizing techniques like machine learning (ML), fuzzy logic, GA, heuristic algorithms, ant colony optimization (ACO), and particle swarm optimization (PSO) [23, 24, 25, 26]. The GA technique is a stochastic search strategy inspired by natural evolutionary events. ML, a subset of AI, has an algorithm that learns and improves from experience without specifically being programmed. There are two types of ML processes that are commonly used, and this includes statistical learning and connectivism. The connectivism ML consists of techniques such as forward neural network (FNN), recurrent neural network (RNN), radial‐basis functional network (RBFN), and convolutional neural network (CNN) [27, 28]. These techniques are collectively called ANNs, which are exclusively and most widely used in biofuels and other renewable energy sectors. ANNs are used as essential modeling tools for the nonlinearities of processes and their abilities to learn from previous data [29]. ANNs are widely used to check the chemical dynamics in reactors and many biological processes effectively [30]. Accurate bioprocessing models and industrialization of biofuels require ANNs [16] that stimulate the interaction and linkages of bioprocesses and enhance the abilities of analysis, learning, and adaptation [31]. ANNs are the understanding of neural functions of the human brain. These mimic the learning process by understanding the arithmetic modeling of the neurological network. ANNs contain input, hidden, and output layers. A hidden layer can be one or more than one [31].

Sewsynker‐Sukai et al. [16] centered their review article on the effectiveness of ANNs in modeling and optimizing the biofuel manufacturing process. In addition, the authors have also discussed the performance of other techniques (e.g., RSM) in comparison with that of ANNs, as well as a discussion of the architectures of the created ANN models [16]. The system's sustainability and dependability are improved by reducing cost and time in the biofuels production and consumption processes. As a result, ANN might be a valuable technique for dealing with biofuels and controlling their production and consumption procedures.

Kessler et al. [32] evaluated the cetane number from various biofuel samples using furanic additions. The results were evaluated using root mean squared error (RMSE) values. The use of ANNs as a predictive approach for predicting cetane quantity with a low error rate was reported to be effective [32].

There is another model in ML that is called “statistical learning,” which is based on learning through statistical methods. These previously mentioned ML techniques vary from each other because of their fundamental principles, structures, and performance abilities. For example, RBFN is less sensitive to noise in the data as compared to FNN; therefore, it makes the learning process faster and easier; however, RBFN needs a greater number of hidden node data, which makes its application in some specific conditions and limits its usage [10, 33, 34].

The use of ML in the field of biofuels has also been extensively researched [35, 36]. ML‐driven predictive analytic framework (MLDPAF) is also studied for the production of energy from waste. A neural network was used to forecast the amount of waste in a recent study, and then by utilizing an enhanced ML model, the waste collection was improved, complying with the energy costs [37]. The simulation showed satisfactory results by reducing the waste by 90% after utilizing the designed method [37]. The forecasting of generation of biomass and other waste materials is also performed through ML techniques [38, 39]. Similarly, an optimization tool, GA, was used in conjunction with ANN to forecast optimal process routes and conditions for improved and cleaner biomass production [40].

Heuristics are also a subset of AI that uses stochastic methods for searching, which are executed through learning‐based models, methods, and experiences [41, 42]. Evolutionary algorithms and swarm intelligence are the two main types of heuristics. GA, evolution methods, and differential evolution are all examples of evolutionary algorithms. Swarm intelligence, on the other hand, has significantly different algorithm styles. It includes PSO and ACO [43, 44]. The ACO algorithm is inspired by the foraging behavior of ants in colonies in which they seek out the shortest pathways between their nest and food sources [45]. According to literature, heuristics demonstrate a strong ability to provide optimal solutions for complex problems with the least uncertainty. It can be used to solve problems like biofuels supply chain planning and scheduling. Castillo‐Villar [46] summarized case studies that were performed using metaheuristic algorithms, including population‐based and trajectory‐based methods, for the supply chain design and optimization. Metaheuristics was reported to be effective for supply chain planning, scheduling, and optimization of production process [46]. By using population‐based techniques, problems associated with integrated supply chain planning, process production, and facility location have been addressed, whereas trajectory‐based metaheuristic techniques have effectively been applied to tackle issues associated with scheduling in the biofuels sector.

AI and ML technologies are not just promising research tools but are becoming essential components of sustainable environmental and energy solutions, offering opportunities to address global challenges through intelligent system optimization and control. AI and ML applications in optimizing cellulosic biomass fermentation processes for sustainable biofuel production have been reviewed [47]. The study analyzes 50 recent research papers to identify optimal modeling approaches and optimization techniques for bioethanol and biohydrogen production [47]. Furthermore, a comprehensive review evaluating the effectiveness of AI models in predicting GHG solubility in ionic liquids with particular emphasis on CO₂ and H₂S capture technologies has also been conducted [48]. Both reviews demonstrated that AI models, particularly ANNs, consistently outperformed traditional approaches in predicting complex system behaviors and optimizing process conditions. The convergence of findings across GHG capture and biofuel production domains suggests broad applicability and transformative potential of AI and ML technologies in addressing critical environmental sustainability and energy challenges.

AI can also be utilized to consistently predict the potential of biofuels by using geographic information. The AI forecasting method can determine the quantities, amount of production, wasted products, distribution, and potential of energy for different biomass resources [49]. AI is also used for enhancing the productivity of energy crops and microalgae cultivation for biofuels production purpose. It can be achieved by implementing most suitable AI algorithms like active learning (AL), meta‐learning, and semi‐supervised learning [50, 51].

Figure 2 illustrates the range of AI/ML models employed across different biofuel production pathways, highlighting their specific areas of application. The diagram shows how different AI/ML algorithms, such as ANNs, support vector machines (SVMs), random forests (RFs), GAs, adaptive neuro‐fuzzy systems (ANFIS), PSO, extreme gradient boosting (XGB), and deep learning (DL), are integrated with various biofuel categories, including biodiesel, biomethanol, bioethanol, biobutanol, biohydrogen, biogas, biomethane, and algal biofuels. These models are employed for key tasks, such as yield prediction, process optimization, quality assessment, cost analysis, environmental impact evaluation, feedstock selection, and real‐time process control. The interconnected nature of the design emphasizes the versatility of AI/ML models across various biofuel systems, facilitating enhanced predictive capabilities, improved operational efficiency, and informed decision‐making for sustainability.

FIGURE 2.

Overview of AI/ML models employed across different biofuel production pathways and their main application. The figure illustrates how models, such as ANN, SVM, RF, GA, ANFIS, PSO, XGB, and DL, are utilized for yield prediction, process optimization, quality assessment, cost analysis, environmental impact evaluation, feedstock selection, and real‐time control across different biofuel types. ANFIS, adaptive neuro‐fuzzy inference system; ANN, artificial neural networks; DL, deep learning; GA, genetic algorithm; PSO, particle swarm optimization; RF, random forest; SVM, support vector machine; XGB, extreme gradient boosting.

This comprehensive review article provides a full assessment of the current status of modeling and optimization of biofuels generation, consumption, and environmental consequences. The benefits and drawbacks of applying AI‐based modeling and simulation to manage, optimize, control, monitor, and predict biofuels yields are comprehensively discussed. The use of AI in five major areas has been examined, including predicting the properties of biomass feedstock, predicting and optimizing the performance of the conversion process from various feedstocks, predicting the properties of produced biofuel, predicting and optimizing the performance of biofuels in end‐use systems, and optimal supply chain modeling. There are currently no well‐defined and clear criteria for selecting and characterizing AI models for biofuels systems. As a result, this article reviewed the applications of AI technologies as a viable alternative to traditional modeling methodologies in major biofuels, including biodiesel, bioethanol, biobutanol, biomethanol, biohydrogen, biogas, and algal biofuels. The strengths and shortcomings and future aspects of AI are also highlighted. To conclude this comprehensive review, an in‐depth discussion on the emerging trends alongside the anticipated future advancements and outlooks for the application of AI in biofuels systems is undertaken.

2. AI in Biodiesel Production

The mixture of fatty acid alkyl esters generated from renewable resources such as leftover animal fats, waste oils, and vegetable oils makes biodiesel [4, 14, 52]. The steps involved in biodiesel production are extraction of oil, pretreatment of feedstock, esterification, separation of product, washing of biodiesel, recovery of unreacted alcohol, and purification of biodiesel (Figure 3) [4, 52]. Biodiesel has a significant potential to provide a sustainable solution for the transportation fuels [1, 8, 53]. The most prevalent method for producing biodiesel is transesterification, in which a short‐chain alcohol (e.g., methanol and ethanol) is used to alkylate transesterifying triglycerides (TGs) with the help of a catalyst to produce fatty acid alkyl esters (i.e., biodiesel) and glycerol. In brief, the transesterification process replaces the glycerol in transesterifying TGs with primary alcohol. The conversion of TGs to diglycerides, monoglycerides, and then glycerol occurs in three steps, each of which is reversible. Water and an ester molecule are generated in each step. When the reaction is finished, one TG molecule yields three ester molecules. The biodiesel production process, as shown in Figure 3, consists of a complex network with multiple stages, including the transesterification of feedstock (vegetable oil, animal fat, algae oil, etc.), the refining of the reacted mixture, separation of glycerol, recovery of methanol, and biodiesel purification. In each of these unit operations, there are inherent control issues to be addressed, in particular when it comes to processing low quality or fluctuating feedstock, such as waste oil or high FFA feedstocks. The existing process control techniques are often inadequate to ensure optimal operation of these dynamic systems, which are less flexible and more sensitive to variation. Although the chemistry behind the process is well understood, improving process efficiency, reducing costs, and minimizing environmental impacts remain a challenge and a very important focus for the application of AI.

FIGURE 3.

Schematic of the biodiesel production process using transesterification. Feedstocks, such as vegetable oil, animal fat, waste cooking oil, or algae oil, are reacted with methanol in the presence of a catalyst to form crude biodiesel and crude glycerol. The biodiesel undergoes refining to meet fuel quality standards, whereas the glycerol is purified through refining, enabling methanol recovery for reuse. This integrated process maximizes resource efficiency and minimizes waste generation.

Waste or nonedible oils are comprised of a large amount of free fatty acids (FFAs) that are not suitable for the transesterification process [1, 8, 14]. Pretreatment is required before the base‐catalyzed reaction to eliminate or convert the bulk of FFA to fatty acid esters [52]. Because a single‐stage acid catalyzed esterification pretreatment may not be enough to convert FFA content of fats and oils, two or more pretreatment methods with water removal after each step have been employed [52]. Moreover, bifunctional catalysts can also be used to solve this issue, but these catalysts suffer from high temperature and low reaction rate due to simultaneously catalyzing both esterification and transesterification processes [54].

The production of biodiesel is currently limited because of the high production costs, and the complex and nonlinear behavior of the production process and usage. Moreover, accurate and speedy modeling tools and methods are required for reactor design, monitoring, optimization, control, and automation. AI has various applications in biodiesel, ranging from its production in the reactor to its combustion in the engine. By using AI technologies, the limitations of feedstock pretreatment can be resolved, and in the near future, biodiesel can play a significant role in supplying alternate fuel for the transport industry, domestic, and power sectors. In other words, AI has provided fast and accurate modeling of the system by utilizing a single technique or a hybrid approach.

Table 1 provides a comprehensive overview of AI and ML applications in biodiesel research, covering studies published between 2010 and 2025. It highlights major applications in biodiesel production and includes methodologies employed, performance metrics, and main findings.

TABLE 1.

A comprehensive overview of artificial intelligence (AI) and machine learning (ML) in biodiesel research: a systematic analysis of methods, metrics, and applications.

Authors (Refs. no.)	Biofuel type	AI/ML method	Application	Performance metrics	Key findings
Aydın et al. [55]	Biodiesel	ANN + RSM	Performance and emission prediction	Optimization of compression ignition engine performance	Combined ANN–RSM approach effectively predicted engine performance and emissions for biodiesel–diesel blends
Satyanarayana and Muraleedharan [56]	Biodiesel	ANN	Acid value prediction	Prediction of acid values for high FFA oils	ANN successfully predicted acid values of vegetable oils with high free fatty acids
Sai Bharadwaj et al. [57]	Biodiesel	RSM vs. ANN	Free fatty acid optimization	Comparative study of RSM and ANN performance	ANN showed superior performance over RSM for biodiesel production from rubber seed oil
Ofoefule et al. [58]	Biodiesel	ANN + RSM	Esterification and transesterification modeling	Comparative analysis of ANN vs. RSM	ANN provided better modeling accuracy for African pear seed oil biodiesel production
Jena et al. [59]	Biodiesel	Statistical modeling	Production from high FFA oils	Optimization of mahua and simarouba oil mixture	Successfully produced biodiesel from high FFA oil mixture using statistical optimization
Rajendra et al. [60]	Biodiesel	ANN + GA	Pretreatment process optimization	Prediction of optimal pretreatment parameters	ANN–GA combination effectively optimized pretreatment process parameters
Silitonga et al. [61]	Biodiesel	ANN + ACO	Production optimization	Optimization of Cerbera manghas biodiesel	ANN integrated with ant colony optimization achieved optimal biodiesel production conditions
Ong et al. [62]	Biodiesel	Optimization algorithms	Oil mixture optimization	Calophyllum‐ceiba oil mixture optimization	Optimized biodiesel production from mixed oil feedstock with enhanced characteristics
Betiku et al. [63]	Biodiesel	RSM vs. ANFIS vs. ANN	FFA reduction via esterification	Comparative predictive capability evaluation	ANFIS showed superior predictive capability over RSM and ANN for palm kernel oil esterification
Hariram et al. [64]	Biodiesel	ANN	Two‐stage transesterification prediction	Performance assessment of ANN predictions	ANN successfully predicted Calophyllum inophyllum biodiesel production through two‐stage process
Faizollahzadeh Ardabili et al. [23]	Biodiesel	Fuzzy Logic	Cetane number prediction	Prediction using carbon number and fuel properties	Fuzzy logic method effectively predicted cetane number using various biodiesel fuel properties
Sajjadi et al. [65]	Biodiesel	RSM + ANN–GA	Ultrasound‐assisted transesterification	Analysis and optimization of palm oil transesterification	ANN–GA optimization outperformed RSM for ultrasound‐assisted alkaline transesterification
Rajkumar et al. [66]	Biodiesel	ANN + Multi‐objective GA	Engine operation optimization	Integration with combustion modeling	Integrated ANN–MOGA approach effectively optimized biodiesel blend engine operation
Naderloo [67]	Biodiesel	AI + LabVIEW	Hydrodynamic cavitation reactor control	Energy ratio analysis	AI‐controlled hydrodynamic cavitation reactor improved energy efficiency in biodiesel production
Kumar et al. [68]	Biodiesel	ANFIS + RSM	Jatropha–algae oil synthesis	Engine performance and emission analysis	ANFIS–RSM combination optimized biodiesel synthesis from jatropha–algae oil blend
Cirak and Demirtas [69]	Biodiesel	ANN	Engine torque prediction	Torque prediction accuracy	ANN successfully predicted engine torque for biodiesel‐fueled engines
Onukwuli et al. [70]	Biodiesel	ANN–GA vs. RSM	Chrysophyllum albidum seed oil optimization	Comparative analysis of optimization methods	ANN–GA showed superior performance over RSM for biodiesel synthesis optimization
Singh et al. [71]	Biodiesel	AI + optimization	Fatty acid composition correlation	Correlation assessment between composition and properties	AI successfully correlated fatty acid composition with biodiesel fuel properties
Jahirul et al. [72]	Biodiesel	PCA + ANN	Chemical composition‐property correlation	Investigation of composition‐property relationships	PCA–ANN combination effectively correlated chemical composition with biodiesel properties
Oǧuz et al. [73]	Biodiesel	ANN	Diesel engine performance prediction	Engine performance prediction accuracy	ANN effectively predicted diesel engine performance using biofuel blends
Mwenge and Rutto [74]	Biodiesel	Machine learning	Animal fat biodiesel production modeling	Predictive modeling accuracy	ML‐based predictive modeling successfully optimized biodiesel production from animal fats
Díez‐Valbuena et al. [75]	Biodiesel	Preference learning	Cetane number modeling	Property prediction accuracy	Preference learning approach effectively modeled biodiesel cetane number
Valbuena et al. [76]	Biodiesel	Machine learning	Iodine value prediction	Iodine value prediction accuracy	ML techniques successfully predicted biodiesel iodine value with high accuracy
Corral‐Bobadilla et al. [77]	Biodiesel	AI + LCA	Waste cooking oil optimization	Life cycle assessment integration	AI approach optimized biodiesel production from waste cooking oil using LCA and market dynamics
Maneedaeng et al. [78]	Biodiesel	AI techniques	Lubricity investigation	Fatty acid profile impact on lubricity	AI techniques effectively investigated impact of fatty acid profiles on biodiesel lubricity

ML technology has many applications in modeling and optimization of feedstocks pretreatment. According to literature, data‐driven ML techniques have proven to be highly efficient because of their predicting abilities compared to that of traditional techniques to model systems with such a high complexity [55]. ANN is a commonly used technology among the available techniques of ML that is used in modeling and optimization of biodiesel production processes.

Many researchers utilized the multilayer perceptron neural network (MLPNN) method with a hidden layer to predict the fatty acid methyl ester (FAME) yield during the process of transesterification from different feedstocks under mechanical agitation and in the presence of catalysts. The FAME content was properly predicted by the MLPNN models generated in these studies. Several authors have compared the RSM method to the MLPNN technique in simulating the mechanically stirred methylation process of different oils catalyzed by various catalysts [56, 57, 58]. They all agreed that the proposed MLPNN models with a single hidden layer were capable of accurately modeling the FAME yield.

MLPNN method accurately predicted the final acid value (AV) of rubber seed oil in the esterification process in a published study [56]. Sai Bharadwaj et al. [57] used feedstock of rubber seed oil and compared the precision of MLPNN and RSM. They predicted that the final AV of the MLPNN model was more accurate compared to that of the RSM model. Ofoefule et al. [58] predicted that the MLPNN model was slightly improved when compared to that of the RSM model. When it comes to prediction of FFA reduction in the feedstock, Ofoefule et al. [58] reported that the MLPNN model was somewhat better than the RSM model. Jena et al. [59] and Rajendra et al. [60] have also optimized FFA levels using the combination of MLPNN and GA.

The MLPNN modeling system was also used in conjunction with the ACO algorithm for modeling and optimization of the FFA reduction from oil feedstock in the process of esterification and to estimate biodiesel production from two nonedible esterified oils [61, 62]. This also optimized the reaction conditions of mechanically agitated transesterification. The authors reported that MLPNN–ACO methods could properly optimize and model the variables in the bioprocess [61, 62].

To optimize and model the FFA reduction in the esterification process of palm oil, Betiku et al. [63] used the RSM, ANFIS–GA, RSM–GA, and MLPNN–GA methods. All of the generated MLPNN, ANFIS, and RSM models had equal generalization capabilities; however, the GA approach outperformed the RSM model in terms of optimization. MLPNN–GA model was also used to accurately simulate and optimize the biodiesel production from glycerolysis of FFA‐rich biodiesel feedstocks (i.e., crude Jatropha curcas oil) by reducing their ultimate AVs [54].

A combination of the Bayesian regularized ANN (BRANN) model and the GA method was used to improve the various reaction conditions of esterified oil [64]. The created BRNN–GA and MLPNN–GA models were shown to be capable of accurately modeling and estimating the optimal response circumstances [64].

A fuzzy logic‐based technique was used by Faizollahzadeh et al. [23] for predicting biodiesel fuel cetane number, saponification number, number of double bonds (DB), and an iodine value (IV). The generated model's performance was calculated using the determination coefficient and RMSE. The created model showed excellent accuracy in testing and training stages, but the most important aspect of this technique was its short processing time and manageable application [23].

The applications of ML technology in biodiesel production can be characterized based on a variety of factors, such as feedstock type, operating mode, catalyst type, and reactor type.

The transesterification reactor is the most important component of a biodiesel manufacturing plant because its performance has a significant impact on the facility's economic viability and environmental sustainability. The immiscibility of oil and alcohol is the major issue with the transesterification reaction, which drops the mass transfer coefficients and, as a result, biodiesel production [65]. Many machine‐based learning models were developed to assist the transesterification process. Currently, ML technique is generally being utilized in the research of biodiesel for modeling different processes of biodiesel production, including transesterification process and hydrodynamic cavitation method [79]. Hydrodynamic cavitation method is cheaper, speedier, has higher production, and requires almost half the energy as compared to other conventional methods. The energy ratio (which is the ratio of output energy to input energy) of the produced biodiesel in hydrodynamic cavitation method was also analyzed, and best operating conditions for the reactor were determined by employing ANFIS, ANN, and RSM methods. It was reported that RSM and ANFIS gave improved results when compared to that of ANN considering the following parameters: performance, speed, and simplicity [66, 67].

Several studies have used ML techniques to model physio‐chemical properties of biodiesel and IC engines based on biodiesel. Some studies have been focused on utilizing ANN to forecast engine performance, engine torque, and exhaust emissions of biodiesel‐based IC engines with high accuracy [68, 80]. The results indicated that the predicted outcome of ANN and experimental outcome were almost consistent, making the accuracy of ANN predicted results to be 95% [80].

The study performed by Cirak [69] suggested that ML technology could be implemented to entire production chain of biodiesel, including feedstock selection and preparation, pretreatment of feedstock, oil extraction, synthesis of biodiesel, refining of biodiesel, quality control and assessment, storage of biodiesel, distribution, and application in IC engine. ML technology should be implemented first on lab‐scale and then extended toward industrial application. It should be noted that hybrid ML schemes have higher potential over single ML models because they have better interpolation and extrapolation abilities and can predict variables in a better way. It is also worth noting that stochastic metaheuristics should be used to optimize topology and training parameters of ML models to enhance their reliability and accuracy of results [46, 55, 70, 79].

Using an ANN, Singh et al. [71] investigated the underlying association between fatty acid composition and biodiesel fuel qualities and established the complex nonlinear relationship between the fuel parameters and the major fatty acids contained in biodiesel composition. The authors created a modeling and optimization framework for identifying fatty acids that affected the biodiesel's physicochemical qualities. Furthermore, the GA and PSO techniques were used to determine the best proportion of biodiesel in a biodiesel blend that might be utilized in diesel engines without modification by optimizing the mix ratio [71]. The presence of a high R ² and a low RMSE indicated that the ANN model reflected the fundamental relationship. The results showed that a 1% increase in arachidic acid resulted in an increase of 21.74 MJ/kg in heating value and a loss of 7.17 in cetane number in biodiesel fuel [71].

Jahirul et al. [72] looked at the relationship between biodiesel fuel qualities and the ANN ability to predict important biodiesel fuel properties based on chemical composition. Principal component analysis was used to study the relationship between individual fuel characteristics and chemical composition, and the graphical analysis of the data revealed a complex relationship between chemical composition and biodiesel properties [72]. The polyunsaturated fatty fraction and the average number of DB were the most relevant chemical composition characteristics in this investigation, affecting all biodiesel properties.

The application of ML technique in biodiesel production systems is speedily increasing as a result of the need for fine‐quality biodiesel, lower energy and labor costs, decreasing chemical and water consumption, automation of plants, and suitability of biodiesel for engine application [73, 81]. As a result, the suitability of ML schemes, including sophisticated DL approaches, for easy modeling of laboratory‐scale biodiesel experiments has been demonstrated, and no more research appears to be required. Instead, research efforts should be focused on using ML to monitor, regulate, and optimize large‐scale biodiesel production. It is worth noting due to that there are not enough studies on utilizing ML techniques to boost production efficiency, economics, stability, and viability of biodiesel reactors in real‐time process monitoring and management.

In a recent study, Mwenge and Rutto [74] assessed the knowledge gap in applying AI techniques (i.e., ANN and ANFIS) to predict the biodiesel production from animal fats catalyzed by blast furnace slag (BFS) geopolymer. The ANN and ANFIS models proved to be reliable for predicting the biodiesel yield. The ANFIS model outperformed the ANN model with a high R ² of 0.9857 and a lower MSE of 2.9386 compared to 0.9781 and 6.2106, respectively, for the ANN model [74]. These results demonstrate the potential of AI techniques, particularly ANFIS, for optimizing biodiesel production for waste valorization, sustainable energy, and advanced biodiesel yield prediction.

Researchers have also studied the influence of different FAMEs on the cetane number of biodiesels [75]. By focusing on ranking rather than precise prediction, the proposed preference learning algorithm provided a valuable tool for guiding the development of new biodiesel and assessing their suitability early in the process, thereby reducing costs. The ability to visualize biodiesel in a latent space also provides insights into the relationships between FAME composition and cetane number, highlighting the dominant role of unsaturation. Traditional regression algorithms showed poor performance in predicting cetane number with low coefficients of determination (below 0.45). In contrast, the proposed SNPLV system achieved a high accuracy of nearly 78% in correctly classifying pairwise comparisons, indicating its effectiveness for ranking biodiesels [75].

Maneedaeng et al. [76] developed a model for predicting the IV of biodiesel and to provide guidance on the experimental methodology required to create a generalized model, considering the size of datasets commonly used in literature. The study also investigated the importance of different attributes used for IV prediction. The authors used a database of 266 biodiesel examples collected from 2002 to 2022, sourced from over 100 different feedstocks (first, second, and third generations). The study applied various ML techniques, including decision trees (DTs), RF, XGB, SVM, and ANN, along with different attribute combinations such as DB, FAME distribution, and other compositional indicators. It was concluded that the XGB algorithm provided the best results, achieving the highest R ² (0.784) and lowest MSE and MAE among the tested models. It was also reported that although the number of DB is crucial, the FAME distribution significantly improved prediction accuracy, accounting for 70% of the prediction relevance.

Researchers have also modeled and optimized biodiesel production from waste cooking oil (WCO) by integrating GAs with life cycle assessment (LCA) and market dynamics analysis [77]. The primary findings indicated that under optimal conditions the transesterification process achieved a maximum biodiesel yield of 97.76%. The study concluded that optimizing the chemical process in biodiesel production from WCO can achieve high yield and HHV along with feasible environmental mitigation strategies.

The effects of fatty acid profile on biodiesel lubricity with AI techniques have also been studied [78]. The authors developed an AI model to study the effect of the biodiesel fatty acid profile of 15 different feedstocks on tribological properties, including wear scar diameter, friction coefficient, and film formation. It was found that the lubricity of biodiesel, as expressed by wear scar diameter and film formation, increased with increasing levels of unsaturation, in particular with biodiesels high in monounsaturated and polyunsaturated fatty acids. In contrast, biodiesels with high saturated fatty acid content had larger wear scar diameters and poorer film formation, resulting in higher friction and wear. The AI model showed high predictive power (R ² = 0.949 for oil wear scar diameter and 0.904 for methyl ester wear scar diameter) and low MAPE values (e.g., 1.39% for oil wear scar diameter) [78].

The role of AI is only expected to grow in biodiesel production. Currently, most of the AI methods discussed here are used at the lab or pilot scale. But with further development they could be fully integrated into large‐scale biodiesel plants to help operators monitor processes in real time. There is also a growing interest in combining AI with environmental assessment tools.

3. AI in Biomethanol Production

The major application of biomethanol is fuel in the transportation sector. Methanol, ethanol, propanol, and butanol are primary bio‐alcohols. In this section, the applications of AI in biomethanol are discussed. Biomethanol, alone, is not used in the IC engines, whereas it is used to make fuel blends with diesel. Like the rest of the bio‐alcohols, there have been many attempts in using AI to enhance the production and yield of biomethanol, but the research is still limited compared to that of other bio‐alcohols and biofuels.

An overview of a gasification‐based methanol production process is shown in Figure 4a. When compared to the use of fossil fuels, the pretreatment of feedstock, gasification, and gas conditioning/cleaning procedures are different when using biomass [4]. A typical schematic of the synthesis of biomethanol using biogas is shown in Figure 4b. A process design with the option of purging the CO₂ isolated into the synthesis stage is also included because it has been demonstrated that the presence of CO₂ reduces energy consumption. Another option is the “dry reforming” method, which substitutes CO₂ for some of the steam used in the steam reforming of methane.

FIGURE 4.

(a) A general schematic of gasification‐based biomethanol production, where biomass is converted into synthesis gas through gasification, followed by purification and catalytic conversion into methanol. (b) A general schematic of reformer‐based biomethanol production in which biogas or syngas from renewable sources undergoes steam or autothermal reforming, purification, and catalytic synthesis to yield biomethanol.

As depicted in Figure 4a,b, each route (biomass gasification or reforming of biogas) consists of a series of complex and interdependent steps that must be controlled and optimized. Figure 4a represents a general process schematic of gasification‐based biomethanol production. In it, the raw material, biomass, is transformed into synthesis gas by the processes of gasification, followed by purification and catalytic synthesis of methanol. Figure 4b represents a general process schematic of reformer‐based biomethanol production. In it, the raw material of biogas (or syngas obtained from renewable sources) undergoes steam or autothermal reforming, purification, and catalytic synthesis steps to produce biomethanol. Feedstock variability, fluctuating reaction conditions, and the demand for cleaner synthesis processes add to the technical complexity and price sensitivity of the process. AI has the potential to address all these challenges. AI can help maximize biomethanol yields and process efficiency.

Techniques, like ANN, fuzzy logic, and PSO, have been used to tune critical reaction parameters with impressive results. Beyond individual unit operations, AI is being used to assess the performance of an overall system so as to optimize yield, energy efficiency, environmental impact, and economic feasibility. Table 2 presents a comprehensive overview of AI and ML applications in biomethanol research. It summarizes key studies on the use of AI/ML in biomethanol production, detailing the methodologies used, performance metrics, and key findings.

TABLE 2.

A comprehensive overview of artificial intelligence (AI) and machine learning (ML) in biomethanol research: a systematic analysis of methods, metrics, and applications.

Authors (Refs. no.)	Biofuel type	AI/ML method	Application	Performance metrics	Key findings
Kasmuri et al. [82]	Biomethanol	Advanced nonlinear neural network	Production from sugarcane bagasse via pyrolysis	Integrated Simulink control system	Advanced neural network‐Simulink system effectively controlled biomethanol production from bagasse
Fózer et al. [83]	Biomethanol	ANN	Hydrothermal gasification sustainability assessment	Sustainability metrics evaluation	ANN‐supported sustainability assessment of biomethanol production via hydrothermal gasification
Yousef et al. [84]	Biomethanol	Fuzzy modeling + PSO	Sugarcane bagasse biomethanol production	Operating parameter optimization	Fuzzy–PSO combination optimized operating parameters for enhanced biomethanol production
Suvarna et al. [85]	Biomethanol	Machine learning framework	CO2 hydrogenation to methanol	Space‐time yield prediction	Generalized ML framework predicted space‐time yield of methanol from thermocatalytic CO2 hydrogenation
Xi et al. [86]	Biomethanol	AI‐based scheduling	Steel mill gas utilization	Energy scheduling optimization	AI‐based energy scheduling optimized steel mill gas utilization for carbon neutrality

Kasmuri et al. [82] used thermochemical pyrolysis process to develop a control and monitoring system for the production of biomethanol. The reactor was modeled by utilizing an advanced model reference controller integrated with a neural network and Simulink interface [82]. The results indicated that developing the model reference with neural control system achieved better control to manipulate reaction temperature for batch pyrolysis [82]. The best fit performance achieved an R ² of 0.9806 for biomethanol production. Consequently, the dynamic nonlinearity of controlling and regulating the input temperature of the pyrolysis batch reactor to the linear behavior of output of biomethanol yield was verified [82]. It was reported that ANN predictions were accurate enough to produce maximum biomethanol production [82].

Similarly, the studies performed by Fózer et al. [83] dealt with integrating ANN in hydrothermal gasification (HTG) for sustainable evaluation of biomethanol production. In this research, economic and environmental performances of biomethanol production were analyzed using ANN for modeling catalytic and noncatalytic HTG [83]. In the training process, Levenberg–Marquardt (L–M) and Bayesian regularization algorithms were applied to the thermocatalytic conversion for different types of feedstocks like biomass and wastes. The HTG was reported as a favorable process alternative for biomethanol synthesis because of the possibilities of GHG emission mitigation, heat integration, and variable synthesis gas composition.

Biomethanol production can be reached at the highest value by optimizing parameters in pyrolysis. Yousef et al. [84] studied increasing the biomethanol production using sugarcane bagasse by optimizing the working parameters of the reactor using AI techniques [84]. A robust model was built to maximize biomethanol production from fuzzy logic techniques by using experimental data. The PSO method was then used to forecast the reactor's optimal operating parameters [84]. It was reported that three parameters (i.e., temperature, reaction duration, and nitrogen flow) affected the biomethanol production from sugarcane bagasse [84]. During the optimization from PSO model, these parameters were considered the set of decision variables for PSO optimization to achieve maximum biomethanol production. The results with fuzzy‐based model demonstrated a better fitting with experimental results as compared to previous ANN predictions. The results showed that fuzzy‐based model increased the prediction accuracy by 78.16% compared to that of ANN technique [84]. Furthermore, without modifying the system architecture or model, the PSO optimizer improved the yield of biomethanol production by 20% over that attained experimentally [84]. This study revealed the great success of AI in enhancing the production quantity of biomethanol, whereas there is not much work done in enhancing the operating parameters of biomethanol‐based engines using AI.

Ensemble ML algorithms like XGB have demonstrated high accuracy in predicting methanol space‐time yield from thermocatalytic CO₂ hydrogenation, outperforming other models [85]. Gradient boosted regression trees (GBRTs) with Bayesian optimization, coupled with PSO, have been used by researchers to identify effective models for steel mill gas utilization systems [86]. Significant reductions in renewable power curtailments and CO₂ emissions were achieved, enabling the production of 126 t of methanol from CO₂ [86].

BRANNs have been applied to design sustainable chemical processes for green methanol production from H₂ and CO₂, considering Earth's ecological capacity [87]. This approach significantly enhanced the sustainability of fossil‐based chemicals by allowing careful selection of hydrogen sources.

The recently presented steam gasification of pinecone biomass to produce hydrogen and biomethanol was motivated by environmental issues such as pine beetle infestation in British Columbia [88]. Aspen Plus and MATLAB software were mostly used for the modeling and simulation of the integrated energy system. Beetle‐infested pinecones were harvested without the involvement of humans through image processing techniques and robotic harvesting. Efficient collection and reduced transportation costs have also been considered. The main performance indexes are 0.17 kg/s of methanol and 0.025 kg/s of hydrogen production. The overall pinecone biomass conversion rate to biomethanol was around 57.5% [88].

AI models can provide a competitive advantage over traditional techniques by rapidly and accurately predicting outcomes for nonlinear computational tasks. However, challenges remain in the availability and accessibility of data on feedstock characteristics, reaction kinetics, and process parameters, as well as the interpretation and comprehension of the decision‐making process. Beyond modeling yield or reactor performance, future research should explore AI‐driven control systems for full‐process automation, real‐time monitoring, and adaptive optimization.

4. AI in Bioethanol Production

Bioethanol is considered a renewable fuel and used as an additive to petrol. Bioethanol fuel has great potential in environmental conservation by reducing the use of fossil fuels, therefore mitigating global warming, reducing carbon footprint production, and maintaining a sustainable environment [89, 90]. It is an alcohol that is produced by the microbial fermentation process of lignocellulosic biomass (sugar, corn, molasses, wheat, etc.). Currently, the majority of bioethanol is made from sugar‐based products or starch derived from sugarcane, corn, or wheat [91]. Bioethanol is the leading biofuel in the world, with the United States and Brazil producing the most. Enhanced bioethanol synthesis has been considered a strategy to help countries meet their goal to decrease GHG emissions. It is predicted that using bioethanol as a fuel for transportation will result in a decrease of 86% in the emission of GHG [92]. Table 3 provides a comprehensive overview of AI and ML applications in bioethanol research. It analyzes key studies on bioethanol production, highlighting major application areas, the AI/ML methods used, performance metrics, and key findings.

TABLE 3.

Comprehensive overview of artificial intelligence (AI) and machine learning (ML) applications in bioethanol production: methodologies, performance metrics, and key findings.

Authors (Refs no.)	Biofuel type	AI/ML method	Application	Performance metrics	Key findings
Pereira et al. [92]	Bioethanol	AI Framework	Industrial production enhancement	Production increase metrics	AI framework successfully increased industrial bioethanol production efficiency
Ahmadian‐Moghadam et al. [93]	Bioethanol	ANN	Ethanol concentration prediction	Concentration prediction accuracy	ANN effectively predicted ethanol concentration in biofuel production processes
Betiku and Taiwo [94]	Bioethanol	RSM vs. ANN	Breadfruit starch hydrolysate optimization	Comparative modeling performance	ANN outperformed RSM in modeling bioethanol production from breadfruit starch
Concu et al. [95]	Bioethanol	PTML model	Enzyme subclass modeling	Proteome mining accuracy	PTML model effectively mined proteome of biofuel‐producing microorganisms
Sebayang et al. [96]	Bioethanol	ANN + ACO	Sorghum grain bioethanol optimization	Production optimization metrics	ANN–ACO integration optimized bioethanol production from sorghum grains
Dave et al. [97]	Bioethanol	ANN–GA	Ulva prolifera biomass fermentation	Integrated modeling approach	ANN–GA approach effectively modeled fermentative bioethanol production from algae biomass
Konishi [98]	Bioethanol	Deep learning	Production estimation from volatile compositions	Estimation accuracy from hydrolysates	Deep learning estimated bioethanol production from volatile compositions in lignocellulosic hydrolysates
Silitonga et al. [99]	Bioethanol	Kernel‐based ELM	Biodiesel–bioethanol–diesel blend evaluation	Engine performance and emission evaluation	Kernel‐based extreme learning machine evaluated engine performance of biodiesel–bioethanol blends
Ezzatzadegan et al. [100]	Bioethanol	AI modeling	Oil palm trunk sap fermentation	Fermentation modeling accuracy	AI modeling successfully characterized oil palm trunk sap fermentation for bioethanol
Liu et al. [101]	Bioethanol	Interpretable ML	Carbon source design optimization	Bioethanol yield enhancement	Interpretable ML optimized carbon source design to enhance bioethanol yield in gas fermentation
Mello et al. [102]	Bioethanol	Data‐centric AI	Ethanol production forecasting in Brazil	Forecasting accuracy	Data‐centric AI methodology effectively forecasted ethanol production in Brazil
Yeboah et al. [103]	Bioethanol	Machine learning‐based modeling	Consolidated bioprocessing with microbial consortium	Performance evaluation of ML modeling approach	ML‐based modeling approach demonstrated effective performance in consolidated bioprocessing with microbial consortium for bioethanol production
Niaze et al. [104]	Bioethanol	Data‐driven ANN model	Industrial bioethanol concentration optimization	Model construction and optimization for concentration enhancement	Data‐driven ANN model successfully optimized industrial bioethanol production concentration with significant improvements

Like other biofuels, work is being done on the execution of AI in the production and applications of bioethanol. Bioethanol production can be increased at a reduced cost through the applications of AI [61]. The usage of ANNs for optimization and modeling in the production of bioethanol is relatively restricted. The influence of concentration of molasses and dead and living yeast cells on the synthesis of bioethanol using Saccharomyces cerevisiae has been studied by Ahmadian‐Moghadam et al. [93]. The model was capable of detecting patterns in experimental data and effectively predicting bioethanol yield [93].

The influence of concentration of breadfruit hydrolysate, pH, and hydraulic retention duration on bioethanol synthesis was also investigated using RSM and ANN models by Betiku and Taiwo [94]. In this study, ANN was compared with RSM. The average difference between the estimated and observed values showed that the utilization of ANN was better in predicting bioethanol yield as compared to RSM model [94]. It was also confirmed that ANN was better than RSM in both data fitting and predictive abilities [94].

Concu et al. [95] described employing different ML algorithms for estimating protein function during a conversion process as a kind of enzyme to consider in bioethanol production. A single method including numerous multilayer perceptron (MLP) designs was used in the proposed ML methods [95]. The specificity, accuracy, and sensitivity of the results were assessed [95]. The buried layer of each method had a distinct number of neurons. The suggested MLP method's accuracy was satisfactory, as was its increased sustainability [95].

The implementation of ANN for the production and optimization of operating parameters in enzymatic hydrolysis and fermentation has also been studied [96]. In this study, the desirability function of the designed model was integrated with ACO for predicting optimal operating parameters to maximize reducing sugars and ethanol production [96]. The results showed that the concentrations of reducing sugars and ethanol through AI and experimental means were equal on average. It depicts that AI is an efficient technique to optimize bioethanol production, and it has the potential to reduce the cost, time, and effort linked with the traditional and experimental techniques [96].

Because of its high carbon content and zero lignin composition, biomass from a green macroalgae, Ulva prolifera, was used in another study to produce bioethanol [97]. Forecasting capability of ANN integrated with GA was used in this bioethanol production process. Following the deployment of ANN–GA, the greatest experimental bioethanol yield attained was 0.242 ± 0.002 g per gram reducing sugar (g/g RS), which was close to predicted value of yield (i.e., 0.239 g/g RS), making the system efficient [97].

According to a reported study by Konishi et al. [98], deep neural network, along with asymmetric auto encoder–decoder (AAE), can prove to be useful in bioethanol fermentation process. The study also demonstrated the significance of hydrolysates, which are typically lost in traditional or conventional procedures, for the synthesis of bioethanol.

The engine parameters for an IC engine based on biodiesel–bioethanol fuel blend have also been studied using ML techniques [73]. The engine performance parameters and exhaust emissions parameters were also predicted at full throttle conditions using Kernel‐based extreme learning machine (K‐ELM) in the study performed by Silitonga et al. [99]. The predicted results from K‐ELM and experiments were close to each other that confirmed the reliability of the proposed system [99]. The prediction of engine noise of bioethanol‐based IC engine was also analyzed using ANFIS, ANN, and RSM models [105, 106]. It was reported that RSM model gave better results when compared to that of ANFIS and ANN [104, 105].

In a separate work, researchers studied if the ANFIS model could accurately forecast bioethanol produced by biomass fermentation of oil palm trunk sap [100]. The PSO approach was used to adjust an optimized ANFIS model for predicting and simulating bioethanol concentration. pH, temperature, total sugar, and fermentation duration were the four input variables. Between the experimental and projected values, the coefficient of determination, mean square error, and root mean square error were 0.9991, 0.0013, and 0.0363, respectively. The results showed that the suggested ANFIS model is a useful tool for predicting bioethanol concentration during the fermentation process. As shown in Figure 5, the process of producing bioethanol is a system of different interrelated stages that begin with biomass handling and then move onto the stages of biomass pretreatment, hydrolysis, glucose and pentose fermentation, ethanol recovery, and lignin utilization. The process parameters and conditions of each of these stages are typically time and resource expensive to optimize in a traditional experimental manner. Each stage may be sensitive to variations in the feedstock characteristics and/or process conditions that can make the optimization process difficult and resource expensive. The high predictive performances of models, such as ANN, support vector regression (SVR), and hybrids models (e.g., ANN–GA or ANFIS), provide the necessary predictive power to optimize the key variables and achieve maximum yields without time and resource intensive trial‐and‐error experimentation.

FIGURE 5.

Bioethanol production process diagram. The process begins with biomass handling and pretreatment to break down structural barriers and enhance enzymatic accessibility. Cellulose hydrolysis converts polysaccharides into fermentable sugars, which are subsequently processed via glucose and pentose fermentation pathways. Ethanol is recovered through distillation, whereas lignin and other nonfermentable components are directed toward value‐added utilization to maximize process efficiency and sustainability.

An interpretable ML framework has been successfully developed that effectively captured the complex relationships between optimization descriptors and process attributes for gas fermentation to bioethanol [101]. Six ML algorithms used included gradient boosting decision tree (GBDT), RF, XGB, Gaussian process regression (GPR), SVR, and MLP. SVR provided the most accurate predictions with an R ² of 0.84 for the test data, outperforming other models like MLP, which showed a higher risk of overfitting. The SVR model also had the smallest MAE and MSE values on the test set, indicating its superior generalization performance. The interpretability tools, Shapley additive explanations (SHAP) and PDP, provided crucial insights into the influence of various carbon sources and experimental conditions, identifying key parameters for optimizing bioethanol production.

D‐AI²‐M, a data‐centric AI (DAI) methodology for forecasting monthly ethanol production in Brazil's principal producing states using time series data, has been introduced [102]. By selecting optimal AI models for ethanol production time series, the authors attempted to improve logistical planning to address the limitations of commonly used traditional econometric models like ARIMA. The methodology emphasizes the importance of handling and improving input data for AI models, particularly through preprocessing techniques such as sliding windows (SW), global min–max (GMM) normalization, differentiation (DIFF), and adaptive normalization (AN). The results demonstrated that D‐AI²‐M significantly improved AI models performance, effectively identifying the best DAI and AI models combinations for specific scenarios and achieving superior forecasting performance overall [102].

AI is also anticipated to play a significant role in the future of bioethanol production through its integration with real‐time process monitoring and control systems. With the increasing availability of data through sensors and the digitization of industrial processes, AI models can transition from being predictive tools to adaptive systems that learn and optimize continuously in response to changing inputs.

5. AI in Biobutanol Production

Biobutanol is a four‐carbon structured compound that is produced by the fermentation of simple sugar in biomass feedstock. Lignocellulosic biomass is an appropriate feedstock to produce butanol because of its low cost and year‐round availability. Like bioethanol, biobutanol is produced through microbial fermentation and can be made from the same range of sugar, starch, or cellulose feedstocks. The strains of Clostridium acetobutylicum and Clostridium beijerinckii are the most used species in biobutanol production. As these organisms produce acetone and ethanol in addition to butanol, the process is commonly known as “acetone–butanol–ethanol (ABE) fermentation.” Thus, biobutanol can be produced as a by‐product during the production of ethanol and acetone [107, 108, 109]. Biobutanol can also be produced from agricultural waste, corn, potatoes, and other wide ranges of feedstocks.

Butanol is a potential alternative to methanol and ethanol with several benefits over both bio‐alcohols. The main use of biobutanol is as fuel in internal combustion engines, but until now it has not been used 100% in engines. The studies performed by Muñoz [110] showed that the biobutanol produced from microalgae biomass has a potential to replace bioethanol, and it is emerging as an advanced biofuel. Biobutanol has larger energy content, is less susceptible to moisture contamination, and is less corrosive as compared to methanol and ethanol. It can be easily blended with gasoline due to its physical characteristics and larger hydrocarbon chain than both methanol and ethanol. Already present gasoline supply systems can be used for its distribution. Some researchers have suggested it as a substitute for gasoline [111]. Gasoline blended with 30% n‐butanol can spark ignition in a single‐cylinder engine [112]. Currently, research is ongoing to incorporate AI in the production and utilization of biobutanol. Considering the importance of biobutanol, the research on the incorporation of AI in the field of biobutanol is limited. Figure 6 shows the biobutanol production process diagram. The pathway begins with biomass pretreatment to release fermentable sugars, followed by hydrolysis and microbial fermentation using solventogenic microorganisms. The fermentation broth undergoes separation and purification steps to recover biobutanol. Integration of feedstock utilization, fermentation optimization, and efficient recovery enhances yield and process sustainability.

FIGURE 6.

Steps involved in fermentative production of biobutanol from biomass. The pathway begins with biomass pretreatment to release fermentable sugars, followed by hydrolysis and microbial fermentation using solventogenic microorganisms. The fermentation broth undergoes separation and purification steps to recover biobutanol. Integration of feedstock utilization, fermentation optimization, and efficient recovery enhances yield and process sustainability.

ML methods can be used to evaluate the variations in output if sufficient experimental data are present. ANNs have many applications for internal combustion engines and are used to predict their performance. Although AI techniques have been used for combustion engines with various fuels, spark ignition with biobutanol is scarcely reported. Table 4 presents a comprehensive overview of AI and ML applications in biobutanol research. It summarizes key studies on biobutanol production, detailing major application areas, the methodologies used, performance metrics, and key findings.

TABLE 4.

Comprehensive overview of artificial intelligence (AI) and machine learning (ML) applications in biobutanol production: methodologies, performance metrics, and key findings.

Authors (Refs. no.)	Biofuel type	AI/ML method	Application	Performance metrics	Key findings
Zuo et al. [111]	Biobutanol	Support vector regression	Engine performance and emission prediction	SVR prediction accuracy	SVR effectively predicted performance and emissions of spark ignition engine with butanol–gasoline blends
Yadav et al. [113]	Biobutanol	Optimization algorithms	Simultaneous biobutanol and biohydrogen production	Simultaneous enhancement optimization	Optimization algorithms enhanced simultaneous production of biobutanol and biohydrogen
Elmeligy et al. [114]	Biobutanol	ANN as metamodels	Multi‐objective optimization	Metamodel accuracy for optimization	ANN metamodels effectively supported multiobjective optimization of biobutanol production
Gürgen et al. [115]	Biobutanol	ANN	Cyclic variability prediction	Variability prediction accuracy	ANN predicted cyclic variability in diesel engine fueled with n‐butanol and diesel blends

Biobutanol blended with gasoline reduced the brake thermal efficiency due to inappropriate combustion phasing. SVR model could predict parameters of the engine. SVR model was proved to be better than ANN [111].

Yadav et al. [113] investigated how to improve biobutanol synthesis by optimizing single and multi‐parameters for better substrate energy recovery. In this study, the experiment was performed by employing a composite design [113]. In addition to the experiment, the results were also analyzed by AI techniques like ANN and RSM. The predicting capabilities of both techniques for optimizing parameters were compared and analyzed [113]. The results showed that ANN yielded a higher value of biobutanol production and was found better than RSM in yielding high volume of biobutanol because of its better coefficient of determination (R ²) and lower RMSE value [113].

The research performed by Thibault et al. [114] focused on the biobutanol production process optimization. The physical optimization of processes was noted to be time intensive, and metamodels or surrogated models were recommended as a solution [114]. The authors also explored how to optimize the biobutanol production process using an integrated ABE fermentation membrane pre‐evaporation approach [114]. ANN was used as a metamodel to reduce the time and predict the optimal operating conditions for the production process [114]. According to the metamodel, the proposed methodology was successful since the number of actual simulations for process was 2500 times smaller [114].

Biobutanol is generally used in compression ignition (CI) engines because of its long ignition delay time, high content of oxygen, and high volatile nature to improve the air‐fuel mixing and lowering the emissions of NO _X and soot [116, 117].

Gürgen et al. [115] studied the cyclic variability of engine with changing the diesel and biobutanol blend using ANN model. They performed an experiment by operating the engine with 10 different speeds and full load conditions using 6 different compositions of butanol–diesel mix [115]. Moreover, the coefficient of variation (COV) of the indicative mean effective pressure (IMEP) was used to determine the cyclic variability of 100 engine cycles. Computed data were used to train the ANN system. The ANN model was trained using L–M and scaled conjugate gradient (SCG) algorithms [115]. The predictions of ANN system regarding diesel–butanol blend and engine performance were consistent with experimental results that showed its success and applicability in biobutanol industry [115]. In future, more research needs to be done to integrate AI in optimizing the working parameters of biobutanol‐based engines to achieve better efficiency.

6. AI in Biohydrogen Production

Biohydrogen has diverse applications ranging from transportation to electricity generation [4]. Because it emits no carbon, biohydrogen has significant potential to reduce global warming [118]. The high energy density and the fact that it only produces water as the by‐product of burning biohydrogen is seen as a promising possible substitute for traditional fossil fuels, and it has the potential to significantly improve environmental circumstances. However, due to the low output observed, the commercialization of this process has been limited [29]. Like several other biofuels, the production of biohydrogen is time–consuming, and production rates are slow. According to a comprehensive research report by MRFR, biohydrogen market share is expected to reach $10.22 billion by 2028, showing a CAGR of 57.41% from 2021 to 2028 [119]. It is worth noting that as of 2020, biohydrogen had a market value of $0.41 billion. Figure 7 is a schematic diagram of biohydrogen production methods by thermochemical and biochemical processes. Thermochemical methods include pyrolysis and gasification that can further be performed by supercritical water technology or conventional thermal processing technology. The biochemical processes include microbial electrolysis, photo‐fermentation (light fermentation, indirect photolysis, and direct photolysis), and dark fermentation (anaerobic fermentation). These production routes indicate the wide range of flexibility and applicability for biological and thermal processes to produce sustainable biohydrogen from biomass resources. These processes also bring their own sets of technological challenges, ranging from fine tuning of microbial consortia to stringent control of temperature, light, and pressure. As a result, such process issues have remained a constraint for large‐scale commercial operations with biohydrogen production rates and process robustness being far below commercial expectations.

FIGURE 7.

Overview of biohydrogen production techniques integrating thermochemical and biochemical pathways. Thermochemical routes include pyrolysis and gasification that can be conducted via supercritical water processing or conventional thermal methods. Biochemical approaches encompass microbial electrolysis, photo‐fermentation (light fermentation, indirect photolysis, and direct photolysis), and anaerobic fermentation (dark fermentation). These diverse methods highlight the versatility of biological and thermal processes for sustainable biohydrogen generation from biomass resources.

The dark fermentation method involves the use of microbes to break down organic materials in anaerobic conditions for biohydrogen production. The parameters of the production process need to be optimized; however, conventional experimental means are unable to achieve the goals because of complexity of the variables involved in it. To optimize the operating parameters of biohydrogen reactors, biohydrogen also needs to have integrated AI models. The reaction conditions are determined by the biohydrogen production reactor, and biohydrogen reactors vary greatly owing to the many types of reactions. It is necessary to start the development of new equipment for constant production of biohydrogen to adapt the progressive production technology and improve the efficacy of biohydrogen production. With the various applications of AI technology, it will be possible to establish constant biohydrogen automatic control, which will enhance the precision and regulate the reaction conditions, which results in a higher rate of biohydrogen generation [120]. Table 5 presents a comprehensive overview of AI and ML applications in biohydrogen research. It analyzes a range of studies focused on biohydrogen production, highlighting key application areas, the AI/ML methodologies used, performance metrics, and major findings.

TABLE 5.

Comprehensive overview of artificial intelligence (AI) and machine learning (ML) applications in biohydrogen production: methodologies, performance metrics, and key findings.

Authors (Refs. no.)	Biofuel type	AI/ML method	Application	Performance metrics	Key findings
Wang and Wan [121]	Biohydrogen	GA + neural network + RSM	Fermentative hydrogen production optimization	Process optimization metrics	GA–NN–RSM combination optimized fermentative hydrogen production process
Nikhil et al. [122]	Biohydrogen	ANN	H2 production rate prediction	Production rate prediction accuracy	ANN‐based model predicted H2 production rates in sucrose‐based bioreactor system
Rosales‐Colunga et al. [123]	Biohydrogen	ANN	Genetically modified Escherichia coli fermentation	Hydrogen production estimation	ANN estimated hydrogen production in genetically modified E. coli fermentations
Xie et al. [124]	Biohydrogen	Intelligent models	Hydrogen solubility in bio‐alcohols	Model reliability comparison	Intelligent models showed superior reliability over thermodynamic models for hydrogen solubility
Whiteman and Kana [125]	Biohydrogen	ANN vs. RSM	Sugarcane molasses biohydrogen production	Comparative modeling efficiency	ANN showed superior modeling efficiency over RSM for biohydrogen production from molasses
Alalayah et al. [126]	Biohydrogen	ANN	Bio‐hydrogen production prediction	Prediction model merits	ANN demonstrated significant merits as prediction model for biohydrogen production
Camberos et al. [127]	Biohydrogen	Neuronal modeling	Two‐stage anaerobic digestion	Biofuel production modeling	Neuronal modeling effectively characterized two‐stage anaerobic digestion for biofuels
Taheri et al. [128]	Biohydrogen	AI modeling	Transmembrane pressure prediction	Pressure prediction in membrane bioreactor	AI modeling predicted transmembrane pressure in anaerobic membrane bioreactors during biohydrogen production
Monroy et al. [129]	Biohydrogen	Neural networks	Photo‐fermentation process modeling	Indoor and outdoor batch process modeling	Neural networks modeled biohydrogen production by batch photo‐fermentation with immobilized consortium
Nasr et al. [130]	Biohydrogen	ANN	Biohydrogen production modeling	Production modeling accuracy	ANN application effectively modeled biohydrogen production processes
Ai et al. [131]	Biohydrogen	GA‐optimized NN + RSM	Photo‐fermentation optimization	Corncob biohydrogen production optimization	GA‐optimized neural network and RSM optimized photo‐fermentation biohydrogen production from corncob
Zhang et al. [132]	Biohydrogen	Interpretable deep learning	Photo‐fermentation process optimization	Process optimization framework	Interpretable deep learning framework optimized photo‐fermentation biological hydrogen production
Shi et al. [133]	Biohydrogen	Machine learning	Process optimization from waste resources	Novel optimization approach	ML‐based novel process optimization studied biohydrogen production from waste resources
Shomope et al. [134]	Biohydrogen	Multilayer perceptron ANN	Dark fermentation prediction	MLP–ANN prediction accuracy	MLP–ANN predicted biohydrogen production from dark fermentation of organic waste biomass
Bandpey et al. [135]	Biohydrogen	Categorical boosting machine learning	Dark fermentation biohydrogen production estimation	Improved estimation accuracy using robust algorithm	Categorical boosting ML algorithm provided improved and robust estimation of dark fermentation biohydrogen production
Wang et al. [136]	Biohydrogen	Grey relational analysis + machine learning	Sucrose anaerobic hydrogen production prediction	Integrated GRA–ML prediction accuracy	Integration of grey relational analysis with ML enhanced sucrose anaerobic hydrogen production prediction accuracy
Sydney et al. [137]	Biohydrogen	Artificial neural network (short‐chain fatty acid‐based)	Biohydrogen production optimization	ANN tool development for production optimization	Short‐chain fatty acid‐based ANN tools were successfully developed and applied to biohydrogen production optimization

The practice of ANNs for optimization and modeling of biohydrogen generation has been popular in recent years. Wang and Wan [121] used a neural network model to explore the impacts of concentration of glucose, pH, and temperature on fermentative biohydrogen generation. This model was successful in linking the input parameters and output of degradation efficiency for substrate, and average biohydrogen yield.

Process model, optimization, and a predictive management system can be extremely helpful in bioprocesses for hydrogen production. ANN was effectively used to control hydrogen production at a large scale for 450 days [122]. Sucrose, acid, alcohol, biomass concentration, hydraulic retention time, alkalinity (ALK), pH, and recycle time were the input parameters in this research [122]. The unsteady interactions among various parameters of the biological process and the biohydrogen generation rate were estimated by the ANN model; a decent agreement between the experimental and the predicted values was observed [122]. An economically viable bioprocess system can be developed by using soft sensor application of ANN for the performance prediction of biohydrogen production [122].

Biohydrogen production can also be predicted and monitored in real time by utilizing ANN models. Colunga et al. [123] estimated the production of biohydrogen. Input parameters for this research included pH, dissolved CO₂, and oxidation–reduction potential. The prediction from the model was appropriate to predict biohydrogen production at a low cost [123]. Similarly, Xie et al. [124] compared the reliability of thermodynamic modeling and AI for the solubility of biohydrogen in furfuryl alcohol. General regression neural network was considered the best model [124].

RSM and ANN models were also comparatively assessed on the basis of various parameters in which the ANN model showed a better value of the coefficient of determination [125]. Similarly, ANN and Box–Wilson designs were comparatively assessed on the basis of input parameters of pH, temperature, and glucose concentration; ANN model was proved to be more precise [126].

An RNN technique was also devised for estimating unmeasurable variables throughout the anaerobic digestion (AD) process in order to quantify hydrogen and methane production [127]. The reason for this was the recurrent ANNs method's capacity to anticipate the behavior of unfamiliar and complex systems [127]. The technique was a method that benefitted from both external disturbances and parameter uncertainty. In addition, the mean squared error (MSE) was used to assess the results. The suggested RNN technique gave successful performance in the face of a complicated system. Furthermore, the technique offered a high level of sustainability [127].

Taheri et al. [128] studied the treatment of wastewater using AI techniques. AI has a lot of potential to optimize the input parameters in the treatment of wastewater. Their study was focused on predicting the transmembrane pressure (TMP) as a main operational variable in the process of anaerobic membrane bioreactor sequencing batch reactor for producing biohydrogen [128]. The authors used ANFIS and ANN for their research. The adaptive ANFIS was trained by using the hybrid algorithms for TMP prediction [128]. A more accurate prediction was achieved by using the Gauss membership function with four membership numbers [128]. Furthermore, for feed‐forward training of the ANN model, a backpropagation algorithm was utilized. Accurate results were achieved by employing an L–M training algorithm with nine neurons in the hidden layer [128]. Both models, ANFIS and ANN, gave accurate results for TMP with R ² values of 0.93 and 0.88, respectively. The RMSE for ANFIS was lower than that of ANN model. It exhibited that ANFIS made a better prediction for TMP as compared to the ANN model, so it had a better performance in predicting TMP [128].

Monroy et al. [129] discussed the use of immobilized photo‐bacteria to produce biohydrogen. The ANN model was trained using a set of experimental fermentations performed indoors on a batch under 30°C and different operating conditions of metals concentration, initial pH, and light intensity [129]. After that, the ANN model was validated on the outdoor fermentation where light intensity varied during the process. The biohydrogen yield was predicted by the model for the changed parameters [129]. The model showed accurate predictions in outdoor conditions that demonstrated the reliability, diversity, and generalization capacity of the model [129].

Yadav et al. [113] worked on increasing the biohydrogen production by optimizing single and multi‐parameter for improving the recovery of substrate energy. In this study, the experiment was performed by employing a composite model design. In addition to the experiment, the results were analyzed by AI techniques, including ANN and RSM [113]. The capabilities of both techniques in predicting biohydrogen yield by optimizing parameters were compared and analyzed [113]. The results showed that ANN yielded a higher value of biohydrogen production when compared to that of RSM because of its higher determination coefficient (R ²) and lower RMSE value [113].

By using a feed‐forward neural network with backpropagation configuration, Nasr et al. [130] reported the use of ANN for the predictive modeling of biohydrogen production. The experimental and anticipated biohydrogen productions were found to be highly linked, according to the authors. The error between the experimental data and the associated projected data MSE was computed in the ANN training process and then propagated backward through the network in each cycle. In order to lower the error, the algorithm adjusts the weights between the input, hidden layers, and output neurons, and the method is repeated until the difference between the experimental and predicted data meets a particular error condition.

Ai et al. [131] employed an innovative approach to enhancing photo‐fermentative biohydrogen production (PFHP) from corncob by integrating microbial optimization and advanced modeling techniques. The authors developed a novel bacterial consortium (HAU‐M2) by combining Enterobacter hormaechei with photosynthetic bacteria HAU‐M1 to improve hydrogen yields through synergistic microbial interactions and biosurfactant secretion. Using Box–Behnken experimental design, the authors compared traditional RSM with a GA optimized neural network (GANN) for process optimization. The GANN model demonstrated superior prediction accuracy and yielded the highest hydrogen production (51.96 mL/g TS) under optimal conditions, which was higher than the RSM model's predicted optimal value, representing a 55% improvement in energy recovery efficiency in the GANN experimental group over the control. The study concluded that the GANN model was highly effective for predicting and optimizing PFHP, and the combination of HAU‐M1 with E. hormaechei significantly improved hydrogen production potential.

Zhang et al. [132] successfully developed a comprehensive DL framework for simulating and predicting photobiological hydrogen production (PFHP). The authors developed a novel computational framework by integrating a CNN for spatial feature extraction, long short‐term memory (LSTM) networks for time‐dependent data capture, and attention mechanisms to focus on critical process variables. It was reported that the CNN–LSTM–attention model significantly outperformed traditional methods by achieving a prediction accuracy of 98% for training data and 85% for testing data [132].

Shi et al. [133] addressed the computationally challenging problem of optimizing biohydrogen production from waste resources, specifically biomass poultry litter and sewage sludge co‐gasification. The authors developed a process optimization framework that integrates ML‐based modeling with mathematical programming to achieve optimal operations, considering multiple sustainability objectives such as economic cost, environmental impact, energy efficiency, and process safety. The key methods employed include a multi‐phase approach integrating Aspen Plus, MATLAB, and GAMS tools. ANN‐based prediction models achieved high accuracy with R ² values of 0.98 and 0.99 for the two subprocesses (upstream and downstream, respectively). The optimization improved the composite sustainability index values to 0.80 and 0.91, surpassing previous optimization results of 0.79 and 0.84.

Shomope et al. [134] studied the predicting biohydrogen yield from the dark fermentation of organic waste biomass. The variability in organic waste composition makes accurate prediction difficult. To address this, the researchers develop an MLP–ANN model to overcome the limitations of existing ML models. The study utilized a dataset of 180 experimental data points from 35 peer‐reviewed journal articles, encompassing various experimental conditions such as substrate type, inoculum type, concentration, pH, and temperature, with hydrogen yield as the output. The most significant findings include the MLP–ANN model's high accuracy, achieving RMSE of 0.3838, MAPE of 0.1938, and R ² of 0.8381. This study is among the first to use historical data to enhance the accuracy and scalability of biohydrogen production models, specifically by MLPs to dark fermentation processes using organic waste. The work concludes that the developed MLP–ANN model is a valuable tool for optimizing the fermentation process [134].

The integration of AI into biohydrogen production processes could redefine the field from batch experiments and manual adjustments to fully autonomous and self‐optimizing networks. In future, AI's role may include yield maximization and balancing economic, environmental, and safety objectives.

7. AI in Biogas and Biomethane Production

Biogas is produced from the AD [138] that happens when bacteria digest organic matter (OM) in the absence of oxygen. It occurs naturally by the digestion of OM in effluent ponds and artificially by the digestion of OM in engineered digesters [139, 140]. AD process pathway as seen in Figure 8 involves series of biological processes by which organic wastes such as agricultural residues, livestock manure, municipal, and industrial by‐products are biochemically transformed into methane‐rich biogas. The AD process begins with feedstock preparation and goes through hydrolysis, acidogenesis, acetogenesis, and methanogenesis stages and results in the production of methane‐rich biogas. The residual digestate can be further utilized as a nutrient‐rich biofertilizer closing the loop in waste‐to‐energy conversion.

FIGURE 8.

Process flow for anaerobic digestion of biomass illustrating the sequential stages from feedstock input to biogas and digestate production. The process begins with feedstock preparation and proceeds through hydrolysis, acidogenesis, acetogenesis, and methanogenesis, converting organic matter into methane‐rich biogas. The residual digestate can be further utilized as a nutrient‐rich biofertilizer, closing the loop in waste‐to‐energy conversion.

Generally, raw biogas consists of 40%–60% methane gas, and most of the remainder comprises carbon dioxide. The upgraded methane from biogas may be utilized to generate heat and power, as well as car fuel. The optimization of the biological process might increase the synthesis and use of biogas as a substitute for traditional fossil fuels. The integration of AI tools in the production and application of biogas is being researched and implemented at industrial level. AI can contribute to this process by being able to learn from datasets with different conditions and record the interaction among these parameters (e.g., feedstock type, pH, temperature, chemical oxygen demand [COD], and volatile fatty acid [VFA]). With AI tools, the researchers will be able to modulate conditions dynamically, improve methane production, shorten lag phase, and enhance stability even when faced with changing environmental and feedstock conditions.

Table 6 presents a comprehensive overview of AI and ML applications in biogas and biomethane research. It summarizes key studies on their production, outlining major application areas, the AI/ML techniques employed, performance metrics, and key findings.

TABLE 6.

Comprehensive overview of artificial intelligence (AI) and machine learning (ML) applications in biogas and biomethane production: methodologies, performance metrics, and key findings.

Authors (Refs. no.)	Biofuel type	AI/ML method	Application	Performance metrics	Key findings
De Clercq et al. [141]	Biogas	Machine learning	Industrial‐scale production prediction	Accurate prediction on several years of daily biogas production data from Chinese projects	ML‐powered software accurately predicted biogas production using industrial‐scale Chinese data
Ozkaya et al. [142]	Biogas	Neural network	Methane fraction prediction	Field‐scale landfill bioreactor prediction	Neural network predicted methane fraction in biogas from field‐scale landfill bioreactors
Qdais et al. [143]	Biogas	ANN + GA	Waste digester modeling and optimization	Production modeling and optimization	ANN–GA combination modeled and optimized biogas production from waste digester
Elnekave et al. [144]	Biogas	ANN	UASB reactor performance prediction	Citrus juice wastewater treatment prediction	ANN predicted UASB reactor performance in citrus juice wastewater treatment
Strik et al. [145]	Biogas	MATLAB neural network	Trace compounds prediction	Anaerobic digestion trace compound prediction	MATLAB neural network toolbox predicted trace compounds in biogas from anaerobic digestion
Mahanty et al. [146]	Biogas	ANN + statistical regression	Industrial sludge co‐digestion characterization	Co‐digestion modeling comparison	ANN and statistical regression characterized co‐digestion of industrial sludges for biogas production
Najafi and Faizollahzadeh Ardabili [147]	Biogas	ANFIS + ANN + logistic methods	Spent mushroom compost biogas estimation	Comparative method evaluation	ANFIS, ANN, and logistic methods estimated biogas production from spent mushroom compost
Ghatak and Ghatak [148]	Biogas	ANN	Mixed lignocellulosic co‐substrates prediction	Production curve behavior prediction	ANN predicted biogas production curve behavior from mixed lignocellulosic co‐substrates
Almomani [149]	Biogas	ANN	Chemically treated agricultural waste prediction	Co‐digested waste biogas prediction	ANN predicted biogas production from chemically treated co‐digested agricultural waste
Gopal et al. [150]	Biogas	RSM + ANN	Waste recycling optimization	Sustainable energy perspective optimization	RSM–ANN optimization strategies improved biogas production by recycling waste for sustainable energy
Heydari et al. [151]	Biogas	ANN + ANFIS	Spearmint essential oil wastewater treatment	UASB reactor biogas production prediction	ANN and ANFIS predicted biogas production from spearmint essential oil wastewater in UASB reactor
Cinar et al. [152]	Biogas	AI integration	Biogas plant operation	Plant operation optimization	AI integration optimized biogas plant operation processes
Mougari et al. [153]	Biogas	ANN + kinetic modeling	Organic waste anaerobic digestion	Biogas and methane production prediction	ANN and kinetic modeling predicted biogas and methane production from organic waste anaerobic digestion
Waewsak et al. [154]	Biogas	Neural‐fuzzy control	Anaerobic hybrid reactor monitoring	Process response and control monitoring	Neural‐fuzzy control system monitored and controlled anaerobic hybrid reactor for biogas production
Ruan et al. [155]	Biogas	Fuzzy neural networks	Full‐scale internal circulation reactor modeling	Biodegradation and biogas production modeling	Fuzzy neural networks modeled biodegradation and biogas production in full‐scale anaerobic reactor
Olabi et al. [156]	Biogas	AI	Methane production maximization from wastepaper	Methane production optimization	AI application maximized methane production from wastepaper
Chen et al. [157]	Biogas	Automated ML framework	Industrial‐scale dry anaerobic digestion	Scalable and interpretable prediction	Automated ML framework provided scalable biogas prediction and optimization for industrial digestion
Putra et al. [158]	Biogas	ML + metaheuristic algorithms	State estimation via spectral analysis	Biogas plant state estimation	ML–metaheuristic combination estimated biogas plant state using spectral analysis
Tufaner et al. [159]	Biogas	ANN + ANFIS	Combined microbial electrolysis cell‐anaerobic digestion	Combined system biogas production modeling	ANN and ANFIS modeled biogas production in combined microbial electrolysis‐anaerobic digestion system
Song et al. [160]	Biogas	Machine learning	Methane production from lignocellulosic wastes	Methane production prediction	ML‐based prediction of methane production from lignocellulosic wastes
Yalcinkaya and Yucel [161]	Biogas	Explainable ML	Municipal wastewater sludge and grease trap waste	Enhanced biogas production prediction	Explainable ML enhanced biogas production from municipal wastewater sludge and grease trap waste
Rahimieh et al. [162]	Biomethane	ANN + GA optimization	VFA complex feed anaerobic digestion	Biomethane production optimization	GA‐optimized ANN modeled anaerobic digestion of VFA complex feed for biomethane production
Wang et al. [163]	Biogas/Biomass	Machine learning models	Organic waste composting maturity prediction	Maturity prediction and key factor identification	ML models successfully predicted maturity and identified key factors in organic waste composting for bioenergy applications
Adeleke et al. [164]	Biomethane	Machine learning model	Biomethane potential evaluation based on biochemical composition	Evaluation accuracy based on biomass biochemical composition	ML model effectively evaluated biomethane potential based on biochemical composition of biomass with high predictive accuracy

Clercq et al. [141] evaluated multiple models to perform predictive analysis to optimize the AD fermentation process at industrial‐scale production of biogas. Their work was focused on enhancing the industrial production of biogas by designing a user graphical interface to ML models to predict biogas output by providing a set of waste inputs. XGB, k‐nearest neighbors’ regression (KNN), logistic regression, SVM, and RF were the ML models used in the study [141]. The results indicated that the KNN model predicted the output biogas yield from the test set with the maximum accuracy of 87%. Researchers also created an online ML application to help biogas project operators improve their analytical abilities [141].

The application of ANNs in biogas generation has received a lot of attention. For example, Levstek and Lakota [165] evaluated how ANNs may be utilized to predict compounds in biogas from AD. These researchers compiled a list of the important studies on utilizing ANNs to analyze and forecast biogas components during biogas generation. In a separate study, the effects of temperature (°C), chloride, conductivity, sulfate, ALK, COD, pH, and leachate on the methane fraction in biogas were examined by Ozkaya et al. [142]. The ANN model was developed using field‐scale bioreactors to capture the influence of inputs on methane fraction. These models have been demonstrated to be adaptable and might be used in extensive biogas production [166].

A backpropagation neural network (BPNN) and a sigmoid function were used by Qdais et al. to replicate the digestive process during biogas generation [143]. With temperature, total solids, pH, and total volatile solids (VSs) as input parameters, the ANN model efficiently trapped the patterns in the dataset and proved its efficacy [143].

Elnekave et al. [145] utilized three distinct ANNs to simulate the influence of initial COD, flow rate, and initial total suspended solids on final COD, volumetric load, and final suspended solids for the synthesis of biogas. The BPNN model produced the greatest predictions and achieved good COD removal efficiency.

In a separate study, Strik et al. [145] examined the impact of varying amounts of trace chemicals on biogas generation during AD in an effort to uncover the problems encountered in the progress of this technology. It was concluded that the performance of biogas production might withstand based on the neural network model's forecast [145].

The nutritional balance, processing capacity, and total biogas output can all benefit from the co‐digestion of waste organic materials. For ANN modeling of biological water and wastewater treatment processes, a feed‐forward neural network with the backpropagation method is typically used. Mahanty et al. [146] utilized feed‐forward BPNN method to study the co‐digestion of industrial sludges from various sources, such as paper, petrochemical, chemical, food, and automotive industries. The model predicted that chemical industry sludges were showing the greatest influence on particular methane yield, whereas automotive industry's sludges had the least impact [146]. As compared to the regression model, the authors found that the model based on ANNs performed better in terms of prediction and analysis [146].

By using AI tools, Najafi et al. [147] projected the production of biogas from spent mushroom compost (SMC). The authors utilized ANN, ANFIS, and logistic models to simulate the biogas production process. The independent variables in their investigation were the retention duration (RT), reactor temperature (T), and carbon‐to‐nitrogen ratio (C/N). The ANFIS model had the lowest RMSE of 0.1940 and the highest coefficient of determination (R ²) of 0.9998 at 35°C mesophilic temperature, according to the findings of this study [147]. The test results were also similar for thermophilic temperature situation. Therefore, it made ANFIS as the best performing AI model to predict biogas production from SMC in both mesophilic and thermophilic situations [147]. The operator's training skills determine the accuracy of AI technologies such as ANN and ANFIS. In the case of reasonable prediction of the process, logistic models should be employed, whereas when high accuracy is required, intelligence methods should be employed.

The flow rate of biogas was predicted using a neural network model by Beltramo et al. [126]. The ant colony algorithm (ACA) was used to identify the process’ significant parameters. The findings suggested that this method might be successfully used to control substrate composition, flow rate, and biogas production.

The production of biogas through modeling and optimizing reactors working with mix substrates cow manure (CM), sawdust, banana stem, paper waste, and rice bran using ANN coupled with GA has also been studied by Kana et al. [17]. The ANN system was trained using data from 25 mini‐pilot fermentations of biogas. For GA optimization, the model served as a fitness function. The optimized profile produced 10.280 L of biogas, an increase of 8.64%, and the lag period was reduced to 3 days from 8 days in the nonoptimized bioprocessing. The combination of ANN and GA was found to be effective in predicting the process's nonlinear behavior [17].

The management of solid waste and the use of AI in improving system efficiency were also explored. Waste management was described as a highly nonlinear process as it involved various climatic, linear, social, economic, technical, demographic, environmental, and legislative parameters [167]. As a result, using AI algorithms, it was possible to forecast the output of biogas production from a variety of inputs [167].

Ghatak et al. [148] investigated biogas production from lignocellulosic biomass using readily available cattle manure and simple energy conversion processes. They used ANN models to estimate biogas generation utilizing cattle manure as a co‐substrate separately with bamboo dust, sawdust, and sugarcane bagasse in both mesophilic and thermophilic temperature conditions [148]. The results of the ANN model were 99.7% accurate, with a coefficient of determination (R ²) of 0.997 and an RMSE of 0.10, indicating a 10% difference from experimental data. The model projected that a blend of sugarcane bagasse and cattle manure would produce the optimal biogas [147].

Similarly, Almomani [149] employed AI approaches to forecast the production of biogas from agricultural solid waste (ASW). The authors described creating an ANN method to optimize cumulative methane production (CMP) from ASW, CM, and their mixture under both thermophilic and mesophilic temperature profiles [149]. The modeled ANN had three layers, 15 neutrons, and 260 epochs and was able to properly forecast the CMP with 99.1% (R ² = 0.991) of data within 10% (RMSE = 0.10) of the mean experimental data. The feasibility and accuracy of using ANN to forecast and optimize biogas production were demonstrated in this study [149].

Gopal et al. [150] studied the use of optimization and pretreatment strategies to boost biogas production from flower waste. RSM and ANN were used to boost biogas production. To create prediction models, pH, temperature, substrate concentration, and time for agitation were used as model variables. Biological, hydrothermal, chemical, and physical approaches were used to investigate the pretreatment of withered flowers. Statistical optimization and pretreatment approach dramatically enhanced biogas production. When compared to the RSM model, the optimal parameters for the biogas generation process rose from the ANN model. It showed that the ANN model was more efficient and accurate at predicting biogas yield than the RSM model.

Heydari et al. [151] used multilayer ANFIS and feed‐forward BPNN to estimate biogas output in an upflow anaerobic sludge blanket reactor (UASB). The authors used spearmint essential oil to cleanse wastewater and generate biogas. The coefficient of determination (R ²) for ANN and ANFIS models was 0.975 and 0.956, respectively [151]. The RMSE for ANN model was 0.234%, whereas the RMSE for the ANFIS model was 0.315% [151]. This research also revealed that both the ANN and ANFIS models produced significant findings, indicating that biogas production in a lab‐scale UASB reactor using spearmint essential oil wastewater treatment could be predicted with high accuracy [151].

Cinar et al. [152] stated that AD of biomass for biogas conversion is a complex sequential process of biological reactions that necessitates real‐time monitoring to avoid process inhibitors. According to the authors, the biogas industry has to establish a self‐sufficient, decision‐making, and automatic AD system in order to improve their current processes and methodologies, such as lean philosophy [152].

Similarly, Mougari et al. [153] used ANN and the modified Gompertz (MG) equation to construct an integrated‐based model for the prediction of biogas (cumulative) and methane production yield from AD of diverse organic wastes. The implementing ANN model used digestion time, C/N, VS to total solid ratio, and carbon content as input features [153]. Furthermore, GA was employed to improve the model's learning by optimizing kinetic parameters and ANN structure. An RMSE of 0.46% and an R ² of 0.9998 were obtained using the GA–ANN model [153]. The model was accurate enough to estimate total biogas and methane yields, and it could be used to scale up AD units and the technoeconomic parameters associated with them.

The implication of AI technology in the production of biogas has also been studied by Waewsak et al. [154]. The authors applied neural‐fuzzy control for wastewater treatment and biogas production in an anaerobic hybrid reactor. For predicting variables such as pH, total volatile acids (TVA), and ALK, an ANN with a backpropagation algorithm was used, whereas the current daytime was used as the input for fuzzy logic to compute the feed flow rate [154]. It was used for managing and monitoring the process reaction under varied operational conditions in the initial, overload influent feeding, and recovery phases [154]. According to the results, the ANN–fuzzy control system exhibited significant potential to regulate anaerobic hybrid reactors under high stability and performance with quick response in these three phases.

Ruan et al. [155] have also looked into using AI technology for forecasting the quality of effluent and biogas yield. The authors constructed and tested three fuzzy neural network models for treating wastewater from a paper mill [155]. To identify the architecture of the model and optimize the fuzzy rule, they employed a fuzzy subtractive clustering technique, and five total rules were extracted in the form of IF‐THEN format [155]. According to the findings, FNN models had lower RMSE and higher determination coefficient (R ²) values than NN models. As a result, it was determined that FNN models may be used to accurately predict effluent quality and biogas yield in an internal circulation AD reactor employing wastewater from a paper mill [155].

For the production of biogas from paper waste, Olabi et al. [156] integrated fuzzy logic‐based modeling with modern optimization. ANFIS was constructed in the first step utilizing an experimental dataset and fuzzy logic modeling. They used a PSO in the second stage to achieve optimal process conditions. To maximize methane generation, the beating time and feedstock ratio were used as decision variables [156]. The proposed model's findings were compared to those of RSM. The proposed methodology was found to be superior to RSM since it fitted experimental data better, had a higher coefficient of determination, and had a lower RMSE value [156]. As a result, using a fuzzy model to increase the biogas yield from paper waste in extended range conditions is a viable option.

An interpretable and scalable AutoML framework was applied to help design and tune an industrial‐scale dry AD (DAD) system in order to overcome the bottlenecks associated with the process, such as its low efficiency and unstable process in converting organic waste into renewable energy [157]. An integrated IterativeImputer + AutoML framework was conducted based on real‐world process data collected from a 100 t/d industrial DAD process. Optimal results of R ² = 0.91 in data imputation were achieved by the IterativeImputer with KNN estimator. The gradient boosting machine (GBM) model selected from H₂O AutoML outperformed other ML algorithms in biogas prediction with a maximum R ² of 0.92, higher than most reported industrial cases. Biomass amount, liquid level, digestate amount, COD, and ALK were found to be the top five most relevant features that impact biogas production. Furthermore, a real‐time process stability monitor based on an AutoML‐based VFAs/ALK soft sensor was built. Partial dependence plots indicated that either keeping the COD concentration lower than 49 000 mg/L or the pH value higher than 8.2 could avoid the digester failure in practice, ultimately presenting a data‐driven solution for smart control and increased energy recovery from organic waste in industrial‐scale DAD systems [157].

A new method for online state estimation in biogas plants was also investigated by using near‐infrared (NIR) spectroscopy and ML [158]. A framework based on NIR spectroscopy with portable NIR sensors fixed in 3D printers for acquiring spectral data of biogas plant samples for the continuous monitoring of state variables, such as dry matter (DM), OM, VSs, COD, total organic carbon (TOC), and acetic acid concentration, was proposed and implemented. Spectral data were collected and then analyzed, followed by a stepwise data preparation for state variable prediction using deep neural networks optimized with metaheuristic algorithms for hyperparameter selection, such as GAs and PSO. ML models showed promising results with R ² of 0.95 for the estimation of state variables using the integrated optimization framework for the identification of optimal neural networks and their parameters.

An AI‐based approach was applied to model biogas production in the combined microbial electrolysis cell‐AD (MECAD) system using ANN and ANFIS [159]. The authors argued that accurate biogas production prediction in MECAD systems was of utmost importance because the systems, which represent a technology with increased energy recovery due to the application of external voltage compared to traditional AD, are difficult to optimize due to their high energy consumption. The authors found that both ML approaches could highly accurately and effectively predict biogas production in MECAD systems.

A workflow combining a data‐driven mechanistic approach to predict methane production from lignocellulosic waste during AD was reported by Chao Song et al. [160]. A total of 157 lignocellulosic waste samples of different types were used to train ML models to predict cumulative methane yield as the target output. The tree‐based pipeline optimization tool (TPOT), which is an automated ML framework that performs feature selection, data preprocessing, model selection, and hyperparameter tuning in an automated pipeline, was used to develop the best ML model. It was found that extra trees regressor was the best performing algorithm with R ² of 0.869, MAE of 32.7 mL CH₄/g VS, and RMSE of 44.6 mL CH₄/g VS on the test dataset. The best model interpretability was obtained using SHAP values, from which lignin content, organic loading rate, and nitrogen content were reported as the most important predictive features. Lignin content had a negative correlation with cumulative methane yield due to its complex structure and recalcitrant nature, which are difficult for microbes to degrade.

In addition, the explainable ML models were also applied to foresee biogas production and to select critical parameters during anaerobic co‐digestion of municipal wastewater sludge and grease trap waste [161]. After a series of candidate ML algorithms, such as Gaussian process (GP), least squares boosting (LSBoost), RF, SVM, and ANN, and a variety of feature selection methods, including SHAP, sequential backward selection (SeqB), F‐test, and increase in node purity (IncNodePurity), were applied, the results indicated that the SHAP was the best feature importance method to select the most critical parameters. Despite the fact that the ensemble LSBoost with all the variables was the best performing model in terms of coefficient of determination (R ² = 0.9555), the GP model with SHAP‐selected variables was the superior performer (R ² = 0.9577) in a more simple and interpretable form. The explainable ML models used in the study were able to capture a good level of predictive accuracy and transparency, allowing users to have a deeper understanding of how input variables affect biogas production that can be of great use in process optimization and control of anaerobic co‐digestion systems [161].

Rahimieh et al. [162] focused on the AD of a complex feed made of different VFAs for biomethane production using ANN–GA to model and predict complex interactions of VFAs with methane in biogas. The VFA complex feed included VFAs with concentrations of acetate, propionate, and butyrate using an ANN–GA hybrid approach to model and simulate the intermediate stages of AD processes and their influence on biogas production efficiency. The results in this research work were used to model and predict the biogas production from VFAs using an ANN–GA hybrid approach. The authors also explained that the use of ANN–GA enabled them to not only predict biogas production trends, but it also provided deeper insights into the biochemical mechanisms behind VFAs metabolism during AD. They concluded that propionate and butyrate had an important role in balancing the microbial community as well as providing extra sources of energy to methanogenic microorganisms.

Long‐term research directions for AI‐enabled AD will include autonomous, self‐regulating biofuels systems that don't require frequent human input. Fully autonomous systems will be able to dynamically optimize operations, including by balancing co‐digestion strategies, preventing inhibitory conditions, and maximizing energy output while also predicting yields. The next step will be to combine explainable AI models with state‐of‐the‐art sensing technology, like NIR spectroscopy, for accurate and transparent continuous real‐time monitoring.

8. AI in Algae‐Based Biofuels

Microalgae are unicellular organisms having the ability to convert nutrients, CO₂, and sunlight, into lipids, carbohydrates, protein, and a variety of other industrially essential compounds through photosynthesis. Microalgae have a variety of advantages over oil crops (such as soybeans), including ease of cultivation, low resource requirements, and the ability to remediate wastewater [168]. As the third‐generation biofuels feedstocks, microalgae have a carbon‐neutral lifetime, do not compete with food or agricultural products, are capable of high‐density growth, and they can be grown all year because growing conditions can be controlled, unlike plant biomass, which is only available during certain seasons [169]. Because there are numerous microalgae species and over 72 500 of them have been thoroughly studied, many microalgae can be developed and cultivated, each with its own set of capabilities, providing a variety of benefits [9]. Microalgae can also be grown in high‐density cultivation systems and can be processed for use as raw material for pharmaceuticals, natural colors, and protein‐rich foods, among others. Various microalgae species can create a wide range of fuels (e.g., hydrogen, jet fuel, biodiesel, and bioethanol) and bioproducts (e.g., antioxidants, bioactive compounds, vitamins, pigments, proteins, lipids, and polysaccharides, among others) [4]. Microalgae biomass pretreatment for biofuels production is shown in Figure 9b. As can be seen in Figure 9a,b, their versatility also enables their biomass to be channeled to various fuel pathways, such as lipids for biodiesel production through transesterification and carbohydrates to produce bioethanol or biogas through saccharification and fermentation as well as to generate co‐products, such as biochar, biohydrogen, and nutraceuticals. The potential of such biorefineries lies in the ability to optimize the whole system (instead of one output) and to ensure that no fraction of biomass is left aside of the process and does not contribute to economic and environmental benefits.

FIGURE 9.

(a) Conversion of CO₂ to lipids, carbohydrates, and valuable bioproducts by using sunlight. Pathways for converting CO₂ into high‐value algae products through photosynthetic cultivation, yielding lipids, carbohydrates, and specialty bioproducts for applications in food, feed, bioenergy, and nutraceuticals. (b) Schematic of microalgal biomass pretreatment showing sequential lipid and carbohydrate extraction followed by transesterification, saccharification, and fermentation steps to produce biodiesel, bioethanol, and biogas.

This is where AI can help. Learning algorithms at their most advanced stage could be used by researchers and the industry to select the best strains, modify growth conditions to induce lipid accumulation, predict the lipid yield, and even predict stability on cultivation systems. The integration of AL, meta‐learning, and computer vision could be used to lower the dependency on laborious experimentation and enable precise control of complex processes.

Algal biofuels have a lower environmental impact because they are made from algae rather than sugarcane or corn. Despite this, industrial‐scale biofuels production from microalgae biomass is still a major challenge. Low production yields and high costs are among them [170]. Given these obstacles, optimizing the output of biomass and lipid profile can be critical to biodiesel production from microalgae.

Recently, AI technology has been utilized to enhance and optimize the production of algal biofuels. AL, an ML framework, can be utilized to provide effective sampling approaches in microalgae research, lowering the number of tests required to reach optimal results [51]. Meta‐learning is a subclass of ML that has been shown to be beneficial in microalgae research. Meta‐learning is a well‐known concept, and the method primarily relies on metadata to accelerate or automate ML operations.

Table 7 provides a comprehensive overview of AI and ML applications in algae‐based biofuels research. It summarizes key studies, highlighting major application areas, the methodologies used, performance metrics, and key findings.

TABLE 7.

Comprehensive overview of artificial intelligence (AI) and machine learning (ML) applications in algae‐based biofuels production: methodologies, performance metrics, and key findings.

Authors (Refs. no.)	Biofuel type	AI/ML method	Application	Performance metrics	Key findings
Teng et al. [51]	Algae‐based biofuels	AI	Microalgae biotechnology digitalization	Genetics, systems, and products perspective	AI provided digitalized perspective on microalgae genetics, systems, and products for biofuels
Rizwan et al. [171]	Algae‐based biofuels	Optimization algorithms	Microalgae‐based biorefinery design	Economic optimization	Optimization algorithms designed optimal microalgae‐based biorefinery considering economics and challenges
Teng et al. [172]	Algae‐based biofuels	Deep neural networks	Chlorella vulgaris catalytic thermal degradation	Evolving DNN optimization	Evolving deep neural networks optimized catalytic thermal degradation of Chlorella vulgaris
Mohamed et al. [173]	Algae‐based biofuels	RSM vs. ANN	Tetraselmis sp. medium optimization	Mixotrophic condition optimization	Comparative analysis of RSM and ANN for Tetraselmis sp. medium optimization under mixotrophic conditions
Galv et al. [174]	Algae‐based biofuels	Modeling techniques	Haematococcus pluvialis biomass production	Biomass production modeling	Modeling techniques characterized Haematococcus pluvialis biomass production
Ching et al. [175]	Algae‐based biofuels	AI methods	Chlorococcum infusionum vacuum drying	Vacuum drying characteristics modeling	AI methods modeled vacuum drying characteristics of Chlorococcum infusionum for algal biofuel production
Kumar et al. [176]	Algae‐based biofuels	ANN	Jatropha–algae biodiesel blend prediction	Oil yield prediction	ANN predicted jatropha–algae biodiesel blend oil yield with high accuracy
Mayol et al. [177]	Algae‐based biofuels	AI	Environmental impact prediction	Life cycle perspective assessment	AI predicted environmental impact of microalgae to biofuels chains using life cycle perspective
Kivanc et al. [178]	Algae‐based biofuels	Dynamic and stochastic optimization	Algae cultivation process optimization	Cultivation process optimization	Dynamic and stochastic optimization optimized algae cultivation process for biofuel production
Liyanaarachchi et al. [179]	Algae‐based biofuels	ANN	Chlorella vulgaris cultivation optimization	Biodiesel production view optimization	ANN optimized Chlorella vulgaris cultivation conditions for enhanced biodiesel production
Uguz et al. [180]	Algae‐based biofuels	Novel ML approaches	Real‐time algal monitoring	Real‐time monitoring accuracy	Novel ML approaches enabled real‐time algal monitoring for biofuel applications

Microalgae biotechnologies encompass a wide range of applications, including wastewater remediation and CO₂ sequestration. The ANN model has been created in this field to forecast water purification. A three‐layer FBN was used to model the removal of textile dye using microalgae [181]. The results revealed that the model can accurately explain the dye removal process.

Many technologies are being investigated for the conversion of microalgae, including thermochemical methods, biochemical methods, and physical or chemical extraction methods. Because microalgae have a variety of chemical compositions, their conversion mechanism is difficult to anticipate. AI technology has enabled experimental conversion to create accurate predictions and ideal conditions in the face of uncertainty. However, using AI algorithms to improve extremely time‐dependent conversion technologies like fermentation and AD should be reviewed. Furthermore, AI algorithms should consider microalgae strains, genomes, microstructure, and so on because these factors might have a significant impact on microalgae performance. Predictive control of the microalgae system is another important use of AI in algae biofuels. The construction of a dynamic model of microalgae is the main difficulty for model predictive control (MPC). Some kinetic models have proven to be beneficial in controlling and optimizing crop systems; however, to be validated in applications, kinetic models need parameter adjustment. Alternatively, an ANN can be trained to learn the dynamics of microalgae systems and then used for MPC. Within the disciplines of process integration, microalgae are an excellent process optimization unit because of their efficiency in industrial waste treatment and production of a variety of value goods. Researchers have optimized a typical microalgae biorefinery for a single Chlorella vulgaris strain while accounting for residual microalgae and discovered that the gross operating margin (GOM) was below breakeven, making pure C. vulgaris cultivation commercially unviable for the sole biofuel production purpose [171].

The improvement of microalgae processes will necessitate large‐scale data analysis and optimization. So, additional potential technologies, microalgae strains, utility integration, and microalgae system dynamics are considered. AI technology can speed up optimization time, give predictive analytics, and discover system dynamics and uncertainty in microalgae‐based biorefineries. Microalgae systems can remove contaminants from petrochemical wastewater and use them for growth. The microalgae can then be turned into petrochemical products like jet fuel using the refinery that currently exists. Processes, like ethanol fermentation, sugarcane mills, simultaneous lutein and biodiesel recovery, and many more, could benefit from the use of microalgae technologies.

AI technology is gaining traction in microalgae biofuels research field because they can give helpful informatics on high uncertainty biosystems. Microalgae are extremely varied microorganisms that necessitate a large amount of data and knowledge from a variety of domains. Although supervised and unsupervised learning have commonly been used in microalgae biofuels research, advanced ML frameworks such as AL can also be utilized to provide effective sampling approaches and lowering the number of experiments needed to reach optimal results. Although the AL method has been used to classify microalgae, it has yet to catch on in other microalgae study fields. Meta‐learning, a subclass of ML, has recently demonstrated its efficacy in microalgae research. Meta‐learning is a well‐defined concept that focuses on using information to speed up or automate ML activities.

Meta‐learning with neuro‐evolution has been used to anticipate the best thermal methods for converting microalgae [172]. Meta‐learning algorithms can also be used to predict algal growth, which could lead to more microalgae applications in the future.

RSM and ANN were studied and compared to determine the influence of glucose content, sodium nitrate, and yeast extract on microalgae species for lipid production enhancement by Mohamed et al. [173]. According to the findings, the adjusted settings increased productivity of the lipid by 173.11 mg/L per day [173]. Although both models adequately captured interactions between input and output parameters, the ANN model was found to be more resilient for nonlinear system prediction [173].

Similarly, Galv et al. [182] used ANN to investigate the effect of pH on biomass concentration and found that the ANN model could properly predict the process outcome. Teng et al. [51] gave a short assessment of how microalgae informatics and AI algorithms may be used to improve current processes and operations. In gene editing and sequencing, AI systems can forecast critical knowledge and anticipate molecular‐level interactions.

Ching et al. [175] integrated AI into the vacuum drying process of algae [175]. They stated that the least squares regression method was previously utilized to capture the data’ linear and nonlinear trends; however, it lacked precision for individual sample points. Therefore, the authors used three models in vacuum drying process, including ANN, XGB machine, and SVM. The results showed that AI models exhibited high accuracy in drying process with RMSE values of 0.0308 and 0.0437 for XGB and ANN, respectively [175].

Kumar et al. [176] have also employed AI approaches to forecast biodiesel generation via the transesterification process from a jatropha–algae oil blend. They used both experimental and modeling methods in their research. Topology 4‐10‐1 was used to train the ANN with L–M algorithm. When the predicted and experimental results were compared, ANN yielded a coefficient of determination of 0.9976 [176]. It confirmed that ANN provided accurate results.

Similarly, Mayol et al. [177] investigated the use of AI for the environmental sustainability of the algal bio‐refineries from the acquisition of raw materials to the end‐of‐life of the product. The ANFIS model was used for predicting the environmental impact. The system identified the major hotspots on various categories of environmental impact. The findings demonstrated a relationship between the system's environmental impact and the input parameters [177].

Recently, ML and computer vision techniques have enabled accurate strain species screening and categorization, resulting in high‐quality microalgae pictures that may be used for biofuel production. Various AI algorithms can also enhance microalgae growth and conversion by decreasing the number of tests and optimizing the conditions. This opens new venues for digitalizing the process of biofuel synthesis from microalgae.

A dynamic and stochastic optimization framework for algae cultivation processes was developed by Kivanc et al. [178]. The framework provided a realistic mathematical representation of system dynamics that accounts for spatial and temporal variations in light intensity, biomass concentration, substrate availability, and nitrogen levels throughout the cultivation system. Furthermore, the optimization framework determined optimal initial conditions for nitrogen concentration, substrate levels, light distribution, biomass density, lipid content, and surface light intensity through a comprehensive scenario analysis evaluating both likely and unlikely cases of model parameter variations. The results of the stochastic optimization process showed an improvement of 11.18% in lipid productivity as compared to the reference case values obtained by deterministic optimization methods. The uncertainty analysis revealed that the parameter variations have a higher impact on the biomass concentration as compared to lipid concentration [177].

Optimizations of the cultivation conditions of C. vulgaris to be used for biodiesel production were also performed using ANNs [179]. C. vulgaris was grown under varying pH levels and cultivation times. Experimental data on biomass, total lipids, unsaturated lipids, and oleic acid were modeled using multilayer ANNs trained via backpropagation, outperforming traditional RSM (R ² > 0.97 for training, >0.92 for validation). Optimization showed that the highest oleic acid yield (745.21 mg/L) occurs at pH 7.46 after 16 days, alongside high biomass and lipid outputs. Sensitivity analysis also revealed that pH and cultivation time influence different metabolites differently.

A recent study has developed a smart and automated monitoring system that can track algae growth in real time [180]. In this study, researchers used ML and simple digital photography to monitor Scenedesmus dimorphus algae cultures continuously. The system works by taking regular photos of the algae growing in transparent bioreactors and using advanced computer algorithms to analyze the color patterns in these images, essentially teaching the computer to recognize how different colors correlate with algae concentration and biomass levels. The authors tested four different ML approaches, DT, RF, GBMs, and KNNs, and found that DT was best at predicting cell counts (achieving R ² = 0.77), whereas RF was better at estimating dry biomass weight (R ² = 0.66) [180].

Recently, a smart and automated algae growth monitoring system has been proposed [180]. In this work, the authors employ ML and basic digital photography for real‐time continuous monitoring of S. dimorphus algae cultures. Briefly, the system acquires time‐series photographs of the algae growing in transparent bioreactors. Advanced computer algorithms analyze the patterns in the acquired images in order to make the computer understand how the different colors in the images are related to algae concentration and biomass [180]. In particular, the authors assessed the performance of four ML algorithms: DT, RF, GBMs, and KNNs and observed that DTs were the most accurate in predicting cell counts (R ² = 0.77), whereas RF was the most accurate in predicting dry biomass weight (R ² = 0.66) [180].

The future of algae‐based biofuels will likely be shaped by AI‐driven biorefineries that operate as fully integrated, self‐optimizing systems. For example, microalgae cultivation can be guided by digital twins’ models that merge biological kinetics with data‐driven learning, paired with real‐time sensing and predictive control to keep production on track despite natural variability. Reinforcement learning could dynamically adjust light, CO₂, and nutrient supply to simultaneously boost biomass and lipid productivity. The most significant impact will be achieved when these AI‐driven systems are integrated with technoeconomic and life cycle analyses to ensure that the production strategies are profitable and sustainable.

9. Comparative Analysis of AI‐Driven Models With Traditional Approaches

The transition from traditional biofuel production methods to AI‐optimized processes represents a significant shift in sustainable energy production. Production of biofuel through conventional production methods has had a range of associated challenges. These include fluctuation in the feedstock availability and limited process control [183]. On the other hand, the use of AI and ML tools is gaining an upper hand in the simulation, modeling, optimization, and control of biofuel production processes [183, 184]. These approaches have an edge in dealing with complex systems, without the need for having mechanistic information on reaction pathways [183, 184].

The comparative analysis between AI/ML‐driven and traditional models suggests that traditional models are limited by the assumptions of independence between variables and lack of scalability, whereas AI‐driven models can incorporate large datasets, adapt to new data in real time, and improve prediction accuracy continuously. AI/ML models are promisingly superior to traditional modeling approaches like RSM or OVAT. Traditional methods are limited to predefined experimental designs and linear or polynomial regression models. In contrast, AI/ML models can capture nonlinear relationships among variables. AI can also handle data inconsistencies and optimize multiple outputs simultaneously. This review also showed that compared to traditional statistical modeling, AI models demonstrated higher accuracy in yield prediction and optimization under complex processing conditions. For instance, the AI‐optimized process showed significant enhancements in yield that increased biodiesel yield to between 84% and 98%. Statistically, the data were validated by high Pearson correlation coefficients (r) and coefficient of determination (R ²), which was approximately 1 [185]. Maximum biodiesel yield of 97.76% was obtained when the process was optimized using AI [77].

Comparative analyses were conducted between traditional RSM and AI‐based optimization methods such as combination of ANN–GA for the process efficiency gains [70]. Results showed that AI‐based optimizations led to significant reductions in terms of time and energy consumption. Reaction time was 9.40% lower when using ANN–GA optimization as compared to conventional RSM‐based methods, and energy consumption was 15.88% less while using 16.82% lower amount of catalyst [70]. Higher biodiesel yield of 1.86% was obtained using ANN–GA optimization over RSM optimized conditions [70].

AI algorithms were shown to be able to optimize the process parameters (such as temperature, pressure, and enzyme dosage) for lower energy consumption and higher biofuel yields [186]. The applications of AI in process control, integration of AI with internet of things (IoT) technologies, and real‐time monitoring systems for automated process control have also been studied [186, 187]. The studies reported that the integration of AI with technologies such as IoT was able to provide real‐time monitoring and management of bioprocesses [187]. AI and ML technologies were also applied in various aspects of microalgae processes for real‐time monitoring, species identification, optimization of growth conditions, harvesting, and purification of bioproducts [188].

ML was applied for increasing the engine performance and reducing the emission on its utilization in biofuels [189]. AI‐optimized biodiesel production produced samples that conformed to the international standards ASTM D6751 and EN14214 [190]. Experimentally, it has been validated to lead to approximately three times increase in the biomass productivity and approximately seven times increase in the lipid productivity [190].

Economic comparison of AI‐optimized processes versus conventional approaches, such as reduced operational costs and enhanced resource utilization, showed that implementing AI provided cost advantages [191, 192]. AI and ML algorithms were used to improve feedstock utilization, optimize resource management, and enable biofuel production, thus improving the overall biofuel supply chain to minimize environmental impacts [191]. Computational modeling is believed to be a promising low‐cost technology with improved productivity and economics for biofuel processes [192].

AI and ML algorithms were employed to enhance feedstock utilization and optimization of resources and to make biofuel production possible; hence, the overall biofuel supply chain was refined to diminish the environmental impacts [191]. Computational modeling is regarded as a promising low‐cost technology with enhanced productivity and economy of biofuel processes [192].

Conventional biofuel production faces several drawbacks. Complex processes demand quick and accurate modeling instruments for designing, optimizing, and controlling [183]. Production efficiency is also influenced by feedstock variability and inconsistency [193]. The challenging nature of the processes under conventional biofuel production has also resulted in difficulties in management, optimization, and accurate forecasting [194]. Conventional biofuel production also has limited real‐time process variation handling capacity and multi‐parameter optimization [184].

AI has an advantage in pattern recognition, which can be particularly useful in capturing the effects of dynamic feedstock compositions in biomass conversion processes without requiring prior knowledge of reaction mechanisms [184]. Additionally, AI allows for real‐time, online, and dynamic control of biochemical processes, enabling rapid monitoring and adjustment of parameters [184]. ML can be used to simultaneously optimize multiple conflicting objectives, such as increasing lipid and biomass productivity [190]. AI algorithms can also be used to analyze real‐time data from industrial operations, enabling predictive maintenance and automated decision‐making. Hybrid AI models that combine optimization algorithms (GA or PSO) and DL frameworks have been shown to outperform traditional methods in terms of accuracy and adaptability. However, these models require high computational power and extensive training to avoid overfitting, especially in biofuel production systems with limited labeled data. A large number of these studies focus on laboratory‐scale experiments, and the scalability to production‐scale has not been fully demonstrated yet [195]. The complexity of AI models may require high computational power and expertise, which can limit their practical application, especially in smaller operations [188]. Furthermore, some studies do not have long‐term operational data to validate sustained performance improvements.

Table 8 presents a comparative summary of AI/ML‐based models versus traditional modeling approaches in biofuel production, focusing on data requirements, flexibility, adaptability, prediction accuracy, performance characteristics, and other key strengths and limitations.

TABLE 8.

Comparison of artificial intelligence (AI)/machine learning (ML) and traditional modeling methods in biofuels production: data requirements, flexibility, prediction accuracy, and performance characteristics.

Aspect	AI and ML models in biofuels production	Traditional modeling methods (e.g., RSM, DOE)
Purpose	Prediction, optimization, and process control with pattern recognition capabilities [196, 197]	Experimental design optimization and empirical modeling [70, 198]
Primary techniques	Artificial neural networks (ANN), support vector machine (SVM), random forest (RF), genetic algorithm (GA), fuzzy neural networks, decision trees, XGBoost, particle swarm optimization (PSO) [199, 200, 201, 202, 203, 204]	Response surface methodology (RSM), design of experiments (DOE), central composite design (CCD), Box–Behnken design [70, 205]
Data requirements	Moderate to high: Requires substantial datasets for training (typically 100–1000+ data points), can handle diverse data structures and missing values [70, 187, 188]	Moderate: Requires structured experimental design with predetermined factor levels, fewer total experiments, but systematic data collection [198, 206]
Flexibility and adaptability	Very high: Adapts to complex nonlinear relationships, handles multiple biofuel types, accommodates varying feedstock compositions, learns from new data [70, 197]	Limited: Restricted to polynomial relationships, requires predefined mathematical forms, poor adaptability to new conditions [70, 205]
Prediction accuracy	Superior: R 2 values typically 0.95–0.99, correlation coefficients approaching 1.0, mean absolute percentage errors 1%–5% [70, 77, 196, 200, 202, 203, 204]	Good: R 2 values typically 0.85–0.95, adequate for optimization within experimental ranges [70]
Computational requirements	High: Requires significant computational power for training, specialized software, and expertise [185, 197]	Low to moderate: Standard statistical software, less computationally intensive [70]
Model interpretability	Limited: Often operates as “black box,” difficult to interpret decision pathways [185, 197, 203]	High: Clear mathematical relationships, easily interpretable coefficients, and interactions [205]
Real‐time application	Excellent: Suitable for real‐time monitoring and control, adaptive process optimization [187, 197]	Limited: Static models, not suitable for real‐time adaptation [197]
Key strengths	Superior handling of nonlinear, complex relationships [70, 197, 206] Real‐time process monitoring and control [197] Pattern recognition in variable feedstock conditions [197] Multi‐parameter optimization capabilities [186, 187] High prediction accuracy across diverse conditions [77, 196, 200, 202]	Clear cause‐effect relationships [205] Well‐established statistical foundation [70] Lower computational requirements [70] Effective for systematic experimental design [70] Good performance within experimental ranges [70]
Key limitations	Requires large, high‐quality datasets [187, 188] Limited interpretability (“black box” nature) [197] High computational and expertise requirements [197] Risk of overfitting with insufficient data [188] Scalability to industrial applications requires validation [197]	Limited to predefined functional forms [70, 205] Poor performance with highly nonlinear systems [70, 206] Inflexible to new operating conditions [205] Assumes constant experimental conditions [198] Limited real‐time adaptation capabilities [197]
Typical applications	Biodiesel yield optimization [196, 200, 202], bioethanol fermentation control [199], biogas production monitoring [197], multi‐feedstock processing [70, 207]	Process optimization within defined ranges [70], experimental design planning [198], empirical model development [205]

10. Technology Readiness Level (TRL)

The TRL scale was developed by National Aeronautics and Space Administration (NASA) in the 1970s as a systematic methodology for assessing technology maturity and deployment readiness [208, 209, 210]. The scale consists of nine distinctive levels measuring progress from basic research (TRL 1) to commercial deployment and operation (TRL 9). Each level requires specific validation criteria and milestones to demonstrate technological advancement. Higher TRL indicates closer to operational deployment and market readiness.

Conventional biodiesel has achieved full commercial deployment across both first‐ and second‐generation technologies with extensive global production infrastructure operating for over two decades. AI applications in biodiesel production primarily focus on process optimization using ANNs, SVMs, ANFIS, and GAs [211, 212, 213]. Recent studies demonstrate ANN models achieving R ² values of 0.9854 for biodiesel yield prediction, outperforming traditional RSM [211]. ML integration shows promise with Adaboost + SVM models achieving R ² values of 0.971 for biofuel production optimization [214]. However, most applications remain at laboratory and pilot scales rather than full commercial deployment. Although AI models show excellent predictive performance, the evidence of widespread commercial integration in biodiesel plants remains limited. The biodiesel sector offers the highest return on investment potential for AI integration due to its mature commercial foundation combined with clear optimization opportunities through AI enhancement.

Conventional biomethanol production has achieved technology demonstration with pilot‐scale validation [215, 216, 217, 218]. There is limited commercial deployment due to economic barriers [215, 216]. Biomass gasification to methanol represents a near‐term viable pathway with commercially competitive market prices [219]. Gasification and catalytic conversion pathways have been demonstrated at the pilot scale. However, high capital costs and complex process integration limit commercial deployment, presenting both technical and economic challenges. Although syngas‐to‐methanol technology has been proven, it remains economically challenging due to these hurdles [215, 216, 217, 218]. The biomethanol industry demonstrates established production pathways through both thermochemical and biological routes [220]. However, the concept of biomethanol refinery still appears to be in infancy considering technical and economic hurdles that impair commercialization prospects [218]. Several advanced methods have been introduced to enhance methanol production, but further research is required before large‐scale biomethanol production can be achieved [221]. There is limited literature on the application of AI in biomethanol production [197]. Existing research primarily focuses on process simulation and optimization with minimal evidence of pilot‐scale or commercial implementation. This is an emerging field with limited published research and minimal evidence of scaled implementation. Most studies are at analytical validation to component validation (TRL 3–4).

Conventional first‐generation bioethanol production from corn and sugarcane has achieved full commercial maturity at TRL 9 (commercialization stage), and the conventional second‐generation cellulosic bioethanol‐production technologies are at pilot‐plant demonstration stages (TRL 7–8); however, AI‐enabled bioethanol production demonstrates advanced TRL status (3–4). AI applications in bioethanol production include fermentation optimization, process control, and predictive modeling using XGB, RF, DTs, SVS, and GA [222, 223]. It was reported that XGB models achieved R ² values of 0.877 for acetate and 0.647 for ethanol concentration predictions in microbial electrosynthesis [223]. Computer monitoring technology has been implemented in some industrial ethanol production facilities [224]. Although some industrial applications exist, systematic AI integration in bioethanol production is still emerging, with most research focused on optimization rather than operational deployment. This puts it in TRL 3–4 (i.e., laboratory optimization and early pilot validation).

The current state of conventional biobutanol production technologies falls within a TRL range of 6–7, primarily focused on scaling from pilot to demonstration stages, which presents significant technical challenges. However, the market is projected to reach $1.8 billion by 2027 [225, 226, 227]. Research shows that biobutanol production faces significant technical and economic challenges in transitioning from pilot‐scale to industrial‐scale operations [228, 229, 230]. AI and ML applications in biobutanol production are emerging but remain at research and early development stages [231]. Research demonstrates the use of ANNs for process optimization, with studies showing that ANNs have been metamodels to reduce the time needed to circumscribe the Pareto domain and determine optimum operating conditions [114]. These applications represent TRL 2–3 level research activities.

Conventional biohydrogen production from lignocellulosic materials may be low cost, but commercial production has not been realized [232]. The major bottlenecks for large‐scale production include costs of plant establishment and maintenance [233]. Pilot‐scale conventional biohydrogen production systems have been established with sustainability assessments showing energy consumption of 171 530 MJ and CO₂ emissions of 9.37 t when producing 1‐t H₂, with a payback period of 6.86 years [234]. However, considerable efforts are needed from both technical and managing aspects to achieve full‐scale application [232]. Although technology demonstrations and pilot validations have been conducted, commercial deployment remains limited [235, 236]. Most conventional biohydrogen production work is still at TRL 4–6 (i.e., component validation to system demonstration). AI applications in biohydrogen production focus on optimization of dark fermentation, photo‐fermentation, and microbial electrolysis processes. ANNs are being developed for modeling biohydrogen production systems [237]. Computational modeling and laboratory optimization studies focus on process parameter optimization, but no pilot‐scale AI integration has been reported [238]. Promising laboratory research results and early development stage placing this technology in TRL 2–3 [238].

AD has been one of the key processes for efficient energy recovery from renewable organic waste streams for over a century [138]. Conventional technology has reached commercial maturity (TRL 8–9) with full‐scale deployment and demand‐oriented production systems [239, 240]. AD applications range from household biogas production for cooking and lighting to industrial‐scale digesters generating electricity and transportation fuels [241]. Biogas offers significant benefits as a renewable energy source, contributing to decarbonization, waste management, and economic development [242]. AI applications in biogas and biomethane production demonstrate strong potential in AD optimization. ANFIS was reported to have superior performance with RMSE values of 0.670 and R ² values of 0.999 for biogas prediction [243]. Feed‐forward BPNNs have also been used for predicting biogas production from municipal solid wastes [244]. Data‐driven optimization algorithms using Python programming for optimal blending ratios in anaerobic co‐digestion have also been reported [245]. Although there is a strong research foundation and several pilot‐scale demonstrations have been conducted, widespread commercial adoption remains limited. Most efforts are currently at the system validation and pilot demonstration stages, focusing on process control applications (i.e., TRL 5–6) [246, 247], with only a few full‐scale installations in biogas plants.

Most conventional microalgae‐based biofuel production systems are currently at the technology validation and pilot demonstration stages (TRL 3–5). These systems face significant scaling and economic challenges [248, 249, 250], which hinder their rapid and widespread implementation [250]. Microalgae biotechnology cannot be considered a fully consolidated technology due to low rates of biomass productivity [251]. Economic sustainability of commercial‐scale production of microalgae biomass remains uncertain, especially regarding cultivation and harvesting processes [252]. With current technologies, algae biofuel cannot meet economic targets unless paired with high‐value or mid‐value products [253]. Many microalgae industries that initially targeted biofuel have adopted parallel business plans focusing on algae by‐products for cosmetics, nutraceuticals, and animal feed [253]. AI applications in algae‐based biofuels include strain selection, cultivation optimization, and lipid content prediction, with computational modeling used for strain selection and laboratory‐scale process optimization. However, no commercial applications have been reported [183, 187, 254]. ML‐assisted optimization has shown promise for Spirulina cultivation with applications from laboratory to large‐scale production considerations [255]. Research shows potential; however, commercial integration remains limited due to significant scaling challenges and economic constraints. This puts it in TRL 2–3.

Table 9 presents the current TRLs for both AI‐based and conventional biofuel production systems. For each biofuel category, the most accurate TRL is identified based on available data related to experimental validation, process integration, and commercial implementation. Higher TRL systems exhibit measurable benefits, including yield improvement, cost reduction, and enhanced automation. This comprehensive analysis highlights significant differences in technological maturity across major biofuel categories, comparing conventional and AI‐enabled approaches.

TABLE 9.

Technology readiness level (TRL) comparison between conventional and artificial intelligence (AI)‐enabled biofuel production systems based on commercial deployment evidence and technological maturity assessment.

Biofuel category	Conventional TRL	AI‐enabled TRL	Commercial status	Key evidence and justification
Biodiesel	TRL 8–9	TRL 4–5	Conventional: Extensive commercial deployment globally with industrial‐scale facilities (723 kL/day capacity) [256, 257] AI‐enabled: Component validation and pilot‐scale testing with limited commercial deployment [77, 185]	Conventional: Mature transesterification technology with 20+ years of commercial operation. Established global supply chains and proven economics. Industrial‐scale purification systems operational [256, 257, 258, 259] AI‐enabled: ML models achieve 84%–98% yield optimization [185]. Pilot implementations demonstrate excellent predictive capabilities but require industrial‐scale validation. Process control integration at component level [77, 185]
Biomethanol	TRL 5–7	TRL 3–4	Conventional: Technology demonstration with pilot‐scale validation. Limited commercial deployment due to economic barriers [215, 216] AI‐enabled: Laboratory optimization and early component validation [197]	Conventional: Gasification and catalytic conversion pathways demonstrated at pilot scale. High capital costs and complex process integration limit commercial deployment. Technical and economic hurdles Thermochemical and biological routes. Syngas‐to‐methanol technology proven but economically challenging [215, 216, 217, 218] AI‐enabled: Process optimization models for gasification and catalysis. Limited to laboratory studies with focus on reaction optimization and catalyst design. Economic feasibility studies for AI implementation have been conducted [197]
Bioethanol	TRL 7–9	TRL 3–4	Conventional: First‐generation (sugarcane, corn) at TRL 9 with consolidated production. Second‐generation (lignocellulosic) at TRL 7–8 [260, 261, 262] AI‐enabled: Laboratory optimization and early pilot validation [187, 194, 199, 263]	Conventional: Brazil demonstrates TRL 9 first‐generation production [262]. Second‐generation faces cost optimization and scalability challenges but approaching commercial deployment [260, 261, 264, 265] AI‐enabled: AI optimization studies for fermentation and downstream processing. Limited to laboratory and early pilot scale. Process integration challenges remain [187, 194, 199, 263]
Biobutanol	TRL 6–7	TRL 2–3	Conventional: Scaling from pilot to demonstration involves considerable technical challenges. Market is projected to reach $1.8 billion by 2027 [225, 226, 227] AI‐enabled: Research and early development stage [231]	Conventional: ABE fermentation achieves 16.8 g/L/h productivity in fibrous‐bed bioreactors [225]. Product toxicity at 1%–2% concentrations limits yields [226]. Consolidated bioprocessing under development [227]. Economic and fermentation challenges [230, 266] AI‐enabled: Computational modeling for fermentation optimization. Limited laboratory studies on process parameter optimization. No pilot‐scale AI integration reported [231]
Biogas	TRL 8–9	TRL 5–6	Conventional: Full commercial deployment with demand‐oriented production systems [239, 240] AI‐enabled: System validation and pilot demonstrations with process control applications [246, 247]	Conventional: Full‐scale plants operational with model predictive control (MAPE <20%) [239]. Over a century of development in anaerobic digestion technology has led to industrial‐scale digesters for electricity generation and transportation fuels, ranging from household to large‐scale systems. Industrial applications now include electricity generation based on demand [240, 241, 267] AI‐enabled: Advanced control systems in pilot testing. Real‐time optimization models under validation. Integration with existing infrastructure progressing [246, 247]
Biohydrogen	TRL 4–6	TRL 2–3	Conventional: Technology demonstration and pilot validation. Limited commercial deployment [235, 236] AI‐enabled: Research and early development stage [238]	Conventional: Dark fermentation processes are preferred over photo‐fermentation [235]. Pilot‐scale demonstrations but economic viability challenges. Production cost barriers for commercial deployment [232, 234, 236] AI‐enabled: Computational modeling and laboratory optimization studies. Process parameter optimization research. No pilot‐scale AI integration reported [238]
Biomethane	TRL 7–8	TRL 4–5	Conventional: System demonstration approaching commercial deployment. Production costs 2–4 times higher than natural gas [268, 269] AI‐enabled: Component validation and early system integration [270]	Conventional: Upgrading technologies demonstrated at system level. Economic barriers limit widespread deployment. Maritime fuel applications under development [268, 269] AI‐enabled: Process optimization models in development. Limited pilot‐scale integration. Focus on upgrading efficiency improvements [270]
Algae‐based biofuels	TRL 3–5	TRL 2–3	Conventional: Technology validation and pilot demonstrations. Major scaling and economic challenges [248, 249, 250] AI‐enabled: Research and early development with computational modeling [183, 187, 254]	Conventional: Cultivation and harvesting technologies demonstrated but face significant economic barriers. Production costs are prohibitive for commercial deployment. Technical challenges in large‐scale cultivation [248, 249, 250, 253] AI‐enabled: AI optimization studies for cultivation parameters. Computational modeling for strain selection. Laboratory‐scale process optimization. No commercial applications [183, 187, 254]

11. Discussion

The demand for energy and fuel is growing rapidly, and biofuels production technologies continue to encounter hurdles in terms of system performance, high cost, and successful implementation. In general, AI is used in the biofuels industry in five major areas: predicting the properties of biomass/feedstock for a specific type of biofuel and conversion method; predicting and optimizing the performance of the conversion process from various types of feedstocks; predicting the properties of produced biofuel; biofuel performance prediction and optimization in end‐use systems, as well as modeling the ideal supply chain.

AI models can be used to enhance scalability, efficiency, and sustainability across biofuels. On the basis of current reviews of literature, research trends have evolved from primarily covering the basic application of ANN and the integration of different AI methods to more advanced ML, DL, and interpretable AI applications. Ongoing research trends include industrial‐scale applications and increased focus on sustainability and LCAs. As for performance, most studies report high prediction accuracy, and optimization studies typically show significant process improvements. In general, comparative studies have often shown the superiority of AI methods over traditional techniques. Hybrid AI approaches (integrating multiple AI methods) have shown potential for superior performance.

Over the course of this review, it was observed that among the ML techniques, ANN is widely used. In heuristics, GA is mostly used by researchers, whereas the use of ACO and PSO has also been implemented in studies by various researchers depending upon the type of feedstock and methodology. Similarly, in symbolic AI, fuzzy logic has been seen widely implemented in the biofuel production processes, whereas, in hybrid systems, ANFIS is mostly used by researchers. In most of the production processes of biofuel, ANN, ANN–GA, and ANFIS models worked successfully by showing greatest values of coefficients of determination and lowest values for RMSE, which makes them suitable for such complex processes of biofuel production. Moreover, the predictions made by these models were quite accurate and according to the experimental datasets. Therefore, it was demonstrated that AI can improve the yield and overall performance of biofuels systems, which is not possible with the utilization of traditional experimental and statistical methods.

Although using AI for biofuels production has produced considerable opportunities and AI has the featured proficiency in biofuel production, implementing AI in biofuels systems comes with some challenges and a few impediments are yet to be communicated to improve its future applications. For instance, getting large data in small experimental units is challenging because tools related to AI only benefit large data that is not suitable for narrow test runs. Moreover, the ANN model of bioprocesses with fewer data sizes might be tricky in a few occurrences and far‐fetched to give adequate data network training. Additionally, its application for ongoing observing and bioreactors control for biofuel production has been rarely studied. Other difficulties involve determining the best elements that impact the model creation phase, such as preprocessing and data division, network design appropriateness, and model validation.

This review article also gave an overview of how AI technology and microalgae informatics might be used to optimize present techniques for creating desirable microalgae products from genetic data. In gene sequencing and editing, molecular interactions can be predicted using AI approaches. Recent computer vision and ML techniques have enabled accurate strain species screening and categorization, allowing for the use of high‐quality microalgae photos for further investigation. By lowering the number of experiments and optimizing the conditions, various AI algorithms can optimize microalgae culture and conversion. Future research efforts in microalgae research should be focused on unifying microalgae databases while considering the commercial factors and using more advanced ML methodologies. This opens the way for microalgae biotechnologies and a more digitalized and efficient future for algae biofuels.

The development of progressive and composite simulation techniques, net profit, and LCA could be seen at the same platform that could be studied for integration of AI in biofuels systems. Advancement may also involve networking among various biofuels stations and additional communications for the management of such a vast bioresource and the technologies for the synthesis of biofuels.

To develop standardized practical procedures for the selection of the right algorithm and determination of dataset size, more research work should be carried out. This will need a substantial understanding of the algorithms, their impacts, and the size of training datasets to solve the biofuel‐related problems that are highly complex. It will also require more case studies with different feedstock types, conversion technologies, working parameters, types of produced biofuel, data collection, documentation of the data, and sharing the accurate data to get diverse training data samples. Furthermore, more studies should be conducted for process monitoring and controlling of biofuel systems using AI techniques. In AI technologies, experimental data curation is a big issue, and with the availability of accurate data, more advanced research can be performed. In the end, a holistic approach should be taken into consideration when exploring the potential of biofuels for sustainable development.

12. Future Outlook

AI integration into future biofuel production systems at different levels has the potential to improve economic as well as environmental outcomes. AI can be used in the entire biofuels production chain, from the selection of feedstock, pretreatment, conversion, and product separation to purification operation in biofuel. Intelligent models can process the same data streamed in real time from sensors and laboratory experiments to optimize operating conditions, flag up inefficiency, and recommend corrective action. Feedback‐control loops might become feasible due to the wider availability of real‐time data originating from connected sensors, laboratory tests, and/or process control systems throughout the production chain. These data in real time can be consumed and analyzed by an AI model to enable the process to adapt itself in real time. Closed‐loop control of the production process enables the system to independently adjust operation to achieve the desired output and enhance efficiency and yield of biofuel production. Future intelligent biofuel plants can dynamically see through the rich molecular and macroscopic descriptions of feedstocks, process streams, and operations, wherein the analytical instruments are increasingly integrated and self‐optimizing. Future AI‐based smart systems will also be quite modular and reconfigurable with rapid design‐build‐test‐learn (DBTL) capabilities and employ digital feedback for self‐improvement and closed‐loop operation. The more up‐to‐date data that can be made available, for example, from sensors connected digitally, from laboratory experiments, and from control systems, can make predictions and optimizations more accurate. This will facilitate accurate process monitoring and rapid iterative advances in biofuel production. In the short term, the AI systems will optimize current biofuel production processes for maximum yields, lowest costs, and least environmental impact. In the end, incorporation of AI could actually result in entirely new biofuel production technologies and mechanisms that are more efficient, sustainable, and less susceptible to the whims of a changing environment. The AI‐based digital twin is equally important to the upscaling of biofuel production to an industrial level, as new process configurations and thereby control strategies can be easily tested and optimized to an increased degree in a virtual reality before being used in the real process. In the future, we may imagine that smart monitoring and control systems, based on real‐time AI models, can remotely be used to operate these facilities on biofuels from small farm scale to larger refineries. Life cycle integrated AI models may also be used to evaluate second‐generation biofuels, their manufacturing, and environmental impacts to enable low‐cost biofuels from environmentally friendly processes. Challenges include the collection of good quality data, which is required for the construction of precise AI model. Because often lab‐scale studies do not directly reflect operating conditions at larger scales, this may bring in an element of uncertainty in the models. In addition, the decision‐making process in AI models does need to be transparent and understandable, particularly in regulated industries, meaning better interpretability and explainability in models is also required. Inclusion of such sophisticated models in current industrial control systems is technically difficult, and it also requires significant modifications to the infrastructure. The coupling LCA tool to AI for biofuel optimization represents a promising innovation possibility. Such systems would also allow an LCA of biofuels, including production efficacy, cost, and environmental impact from field to use. This compatibility with sustainability objectives is important as the biofuel industry transitions toward environmentally friendly processes. Another interesting stream is moving toward explainable AI (XAI) in this context. As DL models are getting more sophisticated, interpreting and understanding the learned decision by the DNN is crucial, especially for regulatory purposes. In the future, attention may also be directed to designing AI models that are robust and fault‐tolerant and able to deal with the uncertainties and variabilities due to the biological systems and agricultural inputs. This strategy guarantees steady biofuel production irrespective of environmental changes. Over the next decade, it is plausible that AI models will play a key role in biofuels production, refining the process to make it both highly efficient and environmentally friendly. One such potential may be the development of smart biofuels tailored to the requirements of the application in which they are being used. Another one perhaps is AI‐improved supply chain optimization process that will ensure constant quality and feedstock availability independent of season or geography. The successful integration of biofuels in the energy system faces several challenges. Once these issues are addressed, we should expect an increasing share of biofuels in meeting the world energy demand. Research should explore federated learning models, AI‐integrated technoeconomic assessments, and smart digital twins for full‐scale biofuel refineries. AI systems hold significant potential not only to predict outcomes but also to autonomously adjust feedstock parameters and operating conditions in real time.

Within a framework that integrates AI into biofuels production, the models operate through a continuous feedback mechanism that allows constant refinement of predictive accuracy and process efficiency. This involves the collection of real‐time process data from sensors and monitoring systems, which is then analyzed by AI algorithms to adjust control strategies or optimize parameters dynamically. This form of closed‐loop integration enables swift responses to variations in feedstock quality, environmental conditions, or process interruptions, thereby maintaining stable output quality, enhancing conversion efficiencies, and minimizing operational costs. As the system gathers operational data over time, it enhances its predictive accuracy. Figure 10 shows a general AI‐enhanced biofuel synthesis model with integrated feedback loops for biofuel process optimization and control. In this system, feedstock enters the processing reactor where AI algorithms (e.g., neural networks, SVMs, RF, and GA) provide real‐time optimization and predictive capabilities. The continuous feedback loop enables dynamic parameter adjustment, leading to optimized processed biofuel output while managing co‐product streams.

FIGURE 10.

A general AI‐enhanced biofuel production model with integrated feedback loops for biofuel process optimization and control.

13. Conclusion

The demand for high‐quality biofuels, as well as the need to minimize energy and labor costs, reduce chemical and water usage, automate biofuels systems, and ensure biofuels compliance with set standards, is driving interest in applying AI in biofuels systems. AI technology has the potential to alleviate the issues that biofuels systems face. The fundamental goal of bringing AI to biofuels is to monitor, regulate, control, and optimize the production and consumption of biofuels from various sources, identify the best available resources for running biofuels systems, and ensure the availability of feedstock for long‐term use. This article reviewed the application of AI technologies as a viable alternative to traditional modeling methodologies in biodiesel, bioethanol, biobutanol, biomethanol, biohydrogen, biogas, and algal biofuels. Several AI applications in biofuels research have been evaluated and critically analyzed in this article to address function approximation, optimization, monitoring, and control challenges. The benefits and drawbacks of applying AI technology in biofuels production, supply chain elements, and system optimization are also highlighted and discussed. Biofuels produced from biomass are eco‐accommodating, environmentally friendly, have several ecological benefits, can help to reduce global warming, and replace traditional fossil fuels. Various parameters are involved in biomass conversion into biofuels and impact the quality and yield of biofuels, such as pH, temperature, concentration of catalyst, type of catalyst, concentration of lignocellulose, mixing ratio, time, type of feedstock, and many other parameters. Accordingly, biomass sourcing, supply, handling, modeling the synthesis, and forecasting are important to get maximum yield. Monitoring and optimizing these parameters in the complex processes of biofuels conversion can help to improve not only biofuels yield but also biofuels quality. The findings of numerous research on the application of AI in biofuels for optimization showed that AI has great potential in the field of biofuels. AI can improve and optimize reactor performance, predict the optimal process pathway, improve yield quality and quantity, speed up the process, reduce production costs by reducing production time and labor requirements, and optimize fuel blends and engine parameters. AI‐based optimization methods help to overcome the limitations of traditional simulations that are often time‐consuming and computationally intensive due to complex constraints. More research is needed to integrate AI into industrial‐scale biofuels processes, as well as to monitor and operate biofuel systems using AI technology. Even though different applications have been described in literature, ANNs have been popular and are preferred to optimize different parameters of processes involved in biofuel production due to their applicability in multiple areas. Apart from ANNs, different hybrid and ensemble models have also played important roles in bioprocess accuracy and precision because they verify and validate the application of AI in the biofuel industry. The use of systematic methodologies for model building and the construction of small‐scale feasible models would improve biofuel production. AI can help establish a long‐term biofuel production system by regulating, controlling, and monitoring bioreactors. Although AI has been employed for modeling laboratory‐scale biofuels experiments, AI‐based solutions, on the other hand, have had limited success in monitoring and operating commercial biofuels systems. Most AI applications to date have been centered on process optimization, prediction, and control, rather than full‐scale operational implementation. A substantial gap persists between research advancements and commercial adoption across all biofuel categories. A comparison of TRLs across major biofuel types highlighted notable differences in technological maturity between conventional and AI‐enabled approaches. The TRL analysis suggests that the most effective commercial strategy is integrating AI with mature, conventional technologies, rather than developing AI independently. The high capital cost of AI‐based monitoring approaches in commercial biofuels systems, on the other hand, has been a significant impediment to their adoption. As a result, research efforts should be focused on utilizing AI to monitor, operate, improve, and optimize industrial biofuels systems. AI technology, despite its superiority and reliability over traditional modeling methodologies, cannot be considered a general answer to all biofuels research challenges and shortcomings. Rather than serving as a replacement for established approaches, this technology could serve as a useful addition to existing methodologies. Success in AI‐enabled biofuels depends on strategic technology integration not competition between AI and conventional approaches. Furthermore, hybrid AI techniques could be used as potential alternatives to pure AI models in biofuels systems. Future research should focus on finding a means to design and deploy generic AI models that can be used in a variety of situations.

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

The authors have nothing to report.