The role of machine learning to boost the bioenergy and biofuels conversion

Enhancing biomass conversion to bioenergy with machine learning: Gains and problems

The growing concerns about environmental sustainability and energy security, such as exhaustion of traditional fossil fuels and global carbon footprint growth have led to an increasing interest in alternative energy sources, especially bioenergy. Recently, numerous scenarios have been proposed regarding the use of bioenergy from different sources in the future energy systems. In this regard, one of the biggest challenges for scientists is managing, modeling, decision-making, and future forecasting of bioenergy systems. The development of machine learning (ML) techniques can provide new opportunities for modeling, optimizing and managing the production, consumption and environmental effects of bioenergy. However, researchers in bioenergy fields have not widely utilized the ML concepts and practices. Therefore, a comparative review of the current ML techniques used for bioenergy productions is presented in this paper. This review summarizes the common issues and difficulties existing in integrating ML with bioenergy studies, and discusses and proposes the possible solutions. Additionally, a detailed discussion of the appropriate ML application scenarios is also conducted in every sector of the entire bioenergy chain. This indicates the modernized conversion processes supported by ML techniques are imperative to accurately capture process-level subtleties, and thus improving techno-economic resilience and socio-ecological integrity of bioenergy production. All the efforts are believed to help in sustainable bioenergy production with ML technologies for the future.

Machine learning for high solid anaerobic digestion: Performance prediction and optimization

Biogas production through anaerobic digestion (AD) is one of the complex non-linear biological processes, wherein understanding its dynamics plays a crucial role towards process control and optimization. In this work, a machine learning based biogas predictive model was developed for high solid systems using algorithms, including SVM, ET, DT, GPR, and KNN and two different datasets (Dataset-1:10, Dataset-2:5 inputs). Support Vector Machine had the highest accuracy (R2) of all the algorithms at 91 % (Dataset-1) and 87 % (Dataset-2), respectively. The statistical analysis showed that there was no significant difference (p = 0.377) across the datasets, wherein with less inputs, accurate results could be predicted. In case of biogas yield, the critical factors which affect the model predictions include loading rate and retention time. The developed high solid machine learning model shows the possibility of integrating Artificial Intelligence to optimize and control AD process, thus contributing to a generic model for enhancing the overall performance of the biogas plant.

Machine learning methods for the modelling and optimisation of biogas production from anaerobic digestion: a review

Biogas plant operators often face huge challenges in the monitoring, controlling and optimisation of the anaerobic digestion (AD) process, as it is very sensitive to surrounding changes, which often leads to process failure and adversely affects biogas production. Conventional implemented methods and mechanistic models are impractical and find it difficult to model the nonlinear and intricate interactions of the AD process. Thus, the development of machine learning (ML) algorithms has attracted considerable interest in the areas of process optimization, real-time monitoring, perturbation detection and parameter prediction. This paper provides a comprehensive and up-to-date overview of different machine learning algorithms, including artificial neural network (ANN), fuzzy logic (FL), adaptive network-based fuzzy inference system (ANFIS), support vector machine (SVM), genetic algorithm (GA) and particle swarm optimization (PSO) in terms of working mechanism, structure, advantages and disadvantages, as well as their prediction performances in modelling the biogas production. A few recent case studies of their applications and limitations are also critically reviewed and compared, providing useful information and recommendation in the selection and application of different ML algorithms. This review shows that the prediction efficiency of different ML algorithms is greatly impacted by variations in the reactor configurations, operating conditions, influent characteristics, selection of input parameters and network architectures. It is recommended to incorporate mixed liquor volatile suspended solids (MLVSS) concentration of the anaerobic digester (ranging from 16,500 to 46,700 mg/L) as one of the input parameters to improve the prediction efficiency of ML modelling. This review also shows that the combination of different ML algorithms (i.e. hybrid GA-ANN model) could yield better accuracy with higher R2 (0.9986) than conventional algorithms and could improve the optimization model of AD. Besides, future works could be focused on the incorporation of an integrated digital twin system coupled with ML techniques into the existing Supervisory Control and Data Acquisition (SCADA) system of any biogas plant to detect any operational abnormalities and prevent digester upsets.

Artificial neural networks prediction and optimization based on four light regions for light utilization from Synechocystis sp. PCC 6803

Light is crucial in microalgae growth. However, dividing the microalgae growth region into light and dark regions has limitations. In this study, the light response of Synechocystis sp. PCC 6803 was investigated to define four light regions (FLRs): light compensation region, light limitation region, light saturation region, and photoinhibition region. The proportions of cells' residence time in the FLRs and the number of times cells (NTC) passed through the FLRs in photobioreactors were calculated by using MATLAB. Based on the FLRs and NTC passed through the FLRs, a growth model was established by using artificial neural network (ANN).The ANN model had a validation R2 value of 0.97, which was 76.36% higher than the model based on light-dark regions. The high accuracy of the ANN model was further verified through dynamic adjustment of light intensity experiments.This study confirmed the importance of the FLRs for studying microalgae growth dynamics.

The adsorption routes of 4IR technologies for effective desulphurization using cellulose nanocrystals: Current trends, challenges, and future perspectives

The combustion of liquid fuels as energy sources for transportation and power generation has necessitated governments worldwide to direct petroleum refineries to produce sulphur-free fuels for environmental sustainability. This review highlights the novel application of artificial intelligence for optimizing and predicting adsorptive desulphurization operating parameters and green isolation conditions of nanocellulose crystals from lignocellulosic biomass waste. The shortcomings of the traditional modelling and optimization techniques are stated, and artificial intelligence's role in overcoming them is broadly discussed. Also, the relationship between nanotechnology and artificial intelligence and the future perspectives of fourth industrial revolution (4IR) technologies for optimization and modelling of the adsorptive desulphurization process are elaborately discussed. The current study surveys different adsorbents used in adsorptive desulphurization and how biomass-based nanocellulose crystals (green adsorbents) are suitable alternatives for achieving cleaner fuels and environmental sustainability. Likewise, the present study reports the challenges and potential solutions to fully implementing 4IR technologies for effective desulphurization of liquid fuels in petroleum refineries. Hence, this study provides insightful information to benefit a broad audience in waste valorization for sustainability, environmental protection, and clean energy generation.

Machine learning assisted rational design of antimicrobial peptides based on human endogenous proteins and their applications for cosmetic preservative system optimization

Preservatives are essential components in cosmetic products, but their safety issues have attracted widespread attention. There is an urgent need for safe and effective alternatives. Antimicrobial peptides (AMPs) are part of the innate immune system and have potent antimicrobial properties. Using machine learning-assisted rational design, we obtained a novel antibacterial peptide, IK-16-1, with significant antibacterial activity and maintaining safety based on β-defensins. IK-16-1 has broad-spectrum antimicrobial properties against Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, and Candida albicans, and has no haemolytic activity. The use of IK-16-1 holds promise in the cosmetics industry, since it can serve as a preservative synergist to reduce the amount of other preservatives in cosmetics. This study verified the feasibility of combining computational design with artificial intelligence prediction to design AMPs, achieving rapid screening and reducing development costs.

Real-time operation of municipal anaerobic digestion using an ensemble data mining framework

This study presents a novel approach for real-time operation of anaerobic digestion using an ensemble decision-making framework composed of weak learner data mining models. The framework utilises simple but practical features such as waste composition, added water and feeding volume to predict biogas yield and to generate an optimised weekly operation pattern to maximise biogas production and minimise operational costs. The effectiveness of this framework is validated through a real-world case study conducted in the UK. Comparative analysis with benchmark models demonstrates a significant improvement in prediction accuracy, increasing from the range of 50-80% with benchmark models to 91% with the proposed framework. The results also show the efficacy of the weekly operation pattern, which leads to a substantial 78% increase in biogas generation during the testing period. Moreover, the pattern contributes to a reduction of 71% in total days required for feeding and 30% in total days required for pre-feeding.

Bridging artificial intelligence and fucoxanthin for the recovery and quantification from microalgae

Fucoxanthin is a carotenoid that possesses various beneficial medicinal properties for human well-being. However, the current extraction technologies and quantification techniques are still lacking in terms of cost validation, high energy consumption, long extraction time, and low yield production. To date, artificial intelligence (AI) models can assist and improvise the bottleneck of fucoxanthin extraction and quantification process by establishing new technologies and processes which involve big data, digitalization, and automation for efficiency fucoxanthin production. This review highlights the application of AI models such as artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS), capable of learning patterns and relationships from large datasets, capturing non-linearity, and predicting optimal conditions that significantly impact the fucoxanthin extraction yield. On top of that, combining metaheuristic algorithm such as genetic algorithm (GA) can further improve the parameter space and discovery of optimal conditions of ANN and ANFIS models, which results in high R2 accuracy ranging from 98.28% to 99.60% after optimization. Besides, AI models such as support vector machine (SVM), convolutional neural networks (CNNs), and ANN have been leveraged for the quantification of fucoxanthin, either computer vision based on color space of images or regression analysis based on statistical data. The findings are reliable when modeling for the concentration of pigments with high R2 accuracy ranging from 66.0% - 99.2%. This review paper has reviewed the feasibility and potential of AI for the extraction and quantification purposes, which can reduce the cost, accelerate the fucoxanthin yields, and development of fucoxanthin-based products.

Analysis of the conversion of cellulose present in lignocellulosic biomass for biofuel production

Among the steps for the conversion of biomass into bioenergy, there is enzymatic hydrolysis. However, factors such as composition, formation of inhibitors, inhibition and enzymatic deactivation can affect the yield and productivity of this process. Lignocellulosic biomass is composed of cellulose, hemicellulose and lignin. However, lignin is organized in a complex and non-uniform way, promotes biomass recalcitrance, which repress the enzymatic attack on cellulose to be converted into glucose, and, consequently, the production of biofuel. Thus, a challenge in enzymatic hydrolysis is to model the reaction behavior. In this context, this study aims to evaluate the performance in enzymatic hydrolysis for the conversion of cellulose present in sugarcane bagasse into glucose. Therefore, modeling and optimization will be proposed to produce high glucose concentration rates. Therefore, a previously developed study will be used, in which the authors proposed a kinetic model for the hydrolysis step. However, as a differential to what has been proposed, the calculation will be carried out evaluating the evaporation, in order to maximize the response to the glucose concentration. Thus, considering evaporation and optimized kinetic parameters, it was possible to obtain high rates of glucose concentration at 204.23 $g.L^{-1.

Exploration of the interaction mechanism of lignocellulosic hybrid systems based on deep eutectic solvents

The interactions of three deep eutectic solvents (DES) choline chloride-glycerol (ChCl-GLY), ChCl-lactic acid (ChCl-LA) and ChCl-urea (ChCl-U) with cellulose-hemicellulose and cellulose-lignin hybrid systems were investigated using the simulated computational approach. Aiming to simulate DES pretreatment of real lignocellulosic biomass in nature. DES pretreatment could disrupt the original hydrogen bonding network structure among the lignocellulosic components and reconstruct the new DES-lignocellulosic hydrogen bonding network structure. ChCl-U had the highest intensity of action on the hybrid systems, removing 78.3% of the hydrogen bonds between cellulose-4-O-methyl Gluconic acid xylan (cellulose-Gxyl) and 68.4% of the hydrogen bonds between cellulose-Veratrylglycerol-b-guaiacyl ether (cellulose-VG), respectively. The increase of urea content facilitated the interaction between DES and lignocellulosic blend system. Finally, the addition of appropriate water (DES:H₂O = 1:5) and DES formed the new DES-water hydrogen bonding network structure more favorable for the interaction of DES with lignocellulose.

Artificial intelligence and machine learning tools for high-performance microalgal wastewater treatment and algal biorefinery: A critical review

The increased water scarcity, depletion of freshwater resources, and rising environmental awareness are stressing for the development of sustainable wastewater treatment processes. Microalgae-based wastewater treatment has resulted in a paradigm shift in our approach toward nutrient removal and simultaneous resource recovery from wastewater. Wastewater treatment and the generation of biofuels and bioproducts from microalgae can be coupled to promote the circular economy synergistically. A microalgal biorefinery transforms microalgal biomass into biofuels, bioactive chemicals, and biomaterials. The large-scale cultivation of microalgae is essential for the commercialization and industrialization of microalgae biorefinery. However, the inherent complexity of microalgal cultivation parameters regarding physiological and illumination parameters renders it challenging to facilitate a smooth and cost-effective operation. Artificial intelligence (AI)/machine learning algorithms (MLA) offer innovative strategies for assessing, predicting, and regulating uncertainties in algal wastewater treatment and biorefinery. The current study presents a critical review of the most promising AI/MLAs that demonstrate a potential to be applied in microalgal technologies. The most commonly used MLAs include artificial neural networks, support vector machine, genetic algorithms, decision tree, and random forest algorithms. Recent developments in AI have made it possible to combine cutting-edge techniques from AI research fields with microalgae for accurate analysis of large datasets. MLAs have been extensively studied for their potential in microalgae detection and classification. However, the ML application in microalgal industries, such as optimizing microalgae cultivation for increased biomass productivity, is still in its infancy. Incorporating smart AI/ML-enabled Internet of Things (IoT) based technologies can help the microalgal industries to operate effectively with minimum resources. Future research directions are also highlighted, and some of the challenges and perspectives of AI/ML are outlined. As the world is entering the digitalized industrial era, this review provides an insightful discussion about intelligent microalgal wastewater treatment and biorefinery for researchers in the field of microalgae.

Prediction of biogas production of industrial scale anaerobic digestion plant by machine learning algorithms

In the anaerobic digestion (AD) process there are some difficulties in maintaining process stability due to the complexity of the system. The variability of the raw material coming to the facility, temperature fluctuations and pH changes as a result of microbial processes cause process instability and require continuous monitoring and control. Increasing continuous monitoring, and internet of things applications within the scope of Industry 4.0 in AD facilities can provide process stability control and early intervention. In this study, five different machine learning (ML) algorithms (RF, ANN, KNN, SVR, and XGBoost) were used to describe and predict the correlation between operational parameters and biogas production quantities collected from a real-scale anaerobic digestion plant. The KNN algorithm had the lowest accuracy in predicting total biogas production over time, while the RF model had the highest prediction accuracy of all prediction models. The RF method produced the best prediction, with an R² of 0.9242, and it was followed by XGBoost, ANN, SVR, and KNN (with R² values of 0.8960, 0.8703, 0.8655, 0.8326, respectively). Real-time process control will be provided and process stability will be maintained by preventing low-efficiency biogas production with the integration of ML applications into AD facilities.

How can vanillin improve the performance of lignocellulosic biomass conversion in an immobilized laccase microreactor system?

Gentle and effective pretreatment is necessary to produce clean lignocellulosic biomass-based fuels. Herein, inspired by the efficient lignin degradation in the foregut of termites, the microreactor system using immobilized laccase and recoverable vanillin was proposed. Firstly, the co-deposition coating of dopamine, hydrogen peroxide and copper sulfate was constructed for laccase immobilization and a high immobilization efficiency of 87.0% was obtained in 30 min. After storage for 10 days, 82.2% activity was maintained in the laccase-loaded microreactor, which is 210.0% higher than free laccase. In addition, 6% (w/w) vanillin can improve lignin degradation in the laccase-loaded microreactor without impairing laccase activity, leading to a 47.3% increment in cellulose accessibility. Finally, a high cellulose conversion rate of 88.1% can be achieved in 1 h with glucose productivity of 2.62 g L^-1 h^-1. These demonstrated that the appropriate addition of vanillin can synergize with immobilized laccase to enhance the conversion of lignocellulosic biomass.

Estimation of Acetic Acid Concentration from Biogas Samples Using Machine Learning

In a biogas plant, the acetic acid concentration is a major component of the substrate as it determines the pH value, and this pH value correlates with the volume of biogas produced. Since it requires specialized laboratory equipment, the concentration of acetic acid in a biogas substrate cannot be measured on-line. The project aims to use NIR sensors and machine learning algorithms to estimate the acetic acid concentration in a biogas substrate based on the measured intensities of the substrate. As a result of this project, it was possible to determine whether the acetic acid concentration in a biogas substrate is higher or lower than 2 g/l using machine learning models.

Applications of artificial intelligence in anaerobic co-digestion: Recent advances and prospects

Anaerobic co-digestion (AcoD) offers several merits such as better digestibility and process stability while enhancing methane yield due to synergistic effects. Operation of an efficient AcoD system, however, requires full comprehension of important operational parameters, such as co-substrates ratio, their composition, volatile fatty acids/alkalinity ratio, organic loading rate, and solids/hydraulic retention time. AcoD process optimization, prediction and control, and early detection of system instability are often difficult to achieve through tedious manual monitoring processes. Recently, artificial intelligence (AI) has emerged as an innovative approach to computational modeling and optimization of the AcoD process. This review discusses AI applications in AcoD process optimization, control, prediction of unknown input/output parameters, and real-time monitoring. Furthermore, the review also compares standalone and hybrid AI algorithms as applied to AcoD. The review highlights future research directions for data preprocessing, model interpretation and validation, and grey-box modeling in AcoD process.

Physics-informed machine learning methods for biomass gasification modeling by considering monotonic relationships

Machine learning methods have recently shown a broad application prospect in biomass gasification modeling. However, a significant drawback of the machine learning approaches is their poor physical interpretability when relying on limited experimental data. In the present work, a physics-informed neural network method (PINN) is developed to predict biomass gasification products (N₂, H₂, CO, CO₂, and CH₄). PINN simultaneously considers regression, structure, and physical monotonicity constraints in the loss function, providing physically feasible predictions. Specifically, the PINN models have outperformed prediction capability (average test R² 0.91-0.97) compared to five other machine learning methods through 50 times random sample classifications. Furthermore, it is demonstrated that the developed models can maintain correct monotonicity even if the feedstock characteristics or gasification conditions are outside the training data. By using a reliable physical mechanism to guide machine learning, the model can ensure better generalizability and scientific interpretability.

Machine learning and statistical analysis for biomass torrefaction: A review

Torrefaction is a remarkable technology in biomass-to-energy. However, biomass has several disadvantages, including hydrophilic properties, higher moisture, lower heating value, and heterogeneous properties. Many conventional approaches, such as kinetic analysis, process modeling, and computational fluid dynamics, have been used to explain torrefaction performance and characteristics. However, they may be insufficient in actual applications because of providing only some specific solutions. Machine learning (ML) and statistical approaches are powerful tools for analyzing and predicting torrefaction outcomes and even optimizing the thermal process for its utilization. This state-of-the-art review aims to present ML-assisted torrefaction. Artificial neural networks, multivariate adaptive regression splines, decision tree, support vector machine, and other methods in the literature are discussed. Statistical approaches (SAs) for torrefaction, including Taguchi, response surface methodology, and analysis of variance, are also reviewed. Overall, this review has provided valuable insights into torrefaction optimization, which is conducive to biomass upgrading for achieving net zero.

Photoenzymatic decarboxylation: A promising way to produce sustainable aviation fuels and fine chemicals

As one of the fastest-growing carbon emission sources, the aviation sector is severely restricted by carbon emission reduction targets. Sustainable aviation fuel (SAF) has emerged as the most potential alternative to traditional aviation fuel, but harsh production technologies limit its commercialization. Fatty acids photodecarboxylase from Chlorella variabilis NC64A (CvFAP), the latest discovered photoenzyme, provides promising approaches to produce various carbon-neutral biofuels and fine chemicals. This review highlights the state-of-the-art strategies to enhance the application of CvFAP in carbon-neutral biofuel and fine chemicals production, including supplementing alkane as decoy molecular, screening efficient CvFAP variants with directed evolution, constructing genetic strains, employing biphasic catalytic system, and immobilizing CvFAP in an efficient photobioreactor. Furthermore, future opportunities are suggested to enhance photoenzymatic decarboxylation and explore the catalytic mechanism of CvFAP. This review provides a broad context to improve CvFAP catalysis and advance its potential applications.

A comprehensive review on the advances of bioproducts from biomass towards meeting net zero carbon emissions (NZCE)

This review investigates the development of bioproducts from biomass and their contribution towards net zero carbon emissions. The promising future of biomasses conversion techniques to produce bioproducts was reviewed. The advances in anaerobic digestion as a biochemical conversion technique have been critically studied and contribute towards carbon emissions mitigation. Different applications of microalgae biomass towards carbon neutrality were comprehensively discussed, and several research findings have been tabulated in this review. The carbon footprints of wastewater treatment plants were studied, and bioenergy utilisation from sludge production was shown to mitigate carbon footprints. The carbon-sinking capability of microalgae has also been outlined. Furthermore, integrated conversion processes have shown to enhance bioproducts generation yield and quality. The anaerobic digestion/pyrolysis integrated process was promising, and potential substrates have been suggested for future research. Lastly, challenges and future perspectives of bioproducts were outlined for a contribution towards meeting carbon neutrality.

Microalgae cultivation for antibiotic oxytetracycline wastewater treatment

Microalgae-based technology provides a potential approach to biologically treating oxytetracycline (OTC) wastewater due to its environmental friendliness, low cost, and high efficiency. However, the OTC degradation and transformation characteristics by microalgae are still unclear and need further exploration. This study used microalgae Chlorella sorokiniana MB-1 for OTC wastewater treatment. The OTC with an initial concentration less than 50 mg L^-1 promoted microalgae growth, while OTC with a concentration higher than 100 mg L^-1 inhibited microalgae growth significantly. More than 99% OTC was removed with the biomass productivity up to 1.8 g L^-1 when treated OTC with 10 mg L^-1 initial concentration for 7 days. Chlorophyll and total sugar contents decreased, while protein and lipid contents increased compared to the control without OTC. The malondialdehyde content firstly reduced but subsequently enhanced when increased OTC concentration, while superoxide dismutase content gradually enhanced, manifesting that traces of OTC stimulate microalgae antioxidant capacity, while the increasing OTC caused further oxidative damage to microalgae cells. The removal pathways of OTC mainly include photolysis (75.8%), biodegradation (17.8%), biosorption (3.6%), and hydrolysis (2.7%). Overall, removing OTC by microalgae was confirmed to be an excellent technology for treating antibiotics wastewater whilst accumulating microalgae biomass.

Machine learning assisted prediction of biochar yield and composition via pyrolysis of biomass

Biochar production via pyrolysis of various organic waste has potential to reduce dependence on conventional energy sources and mitigate global warming potential. Existing models for predicting biochar yield and compositions are computationally-demanding, complex, and have low accuracy for extrapolative scenarios. Here, two data-driven machine learning models based on Multi-Layer Perceptron Neural Network and Artificial Neuro-Fuzzy Inference System are developed. The data-driven models predict biochar yield and compositions for a variety of input feedstock compositions and pyrolysis process conditions. Feature importance assessment of the input dataset revealed their competitive significance for predicting biochar yield and compositions. Overall, the predictive accuracy of the models was up to 12.7% better than the Random Forest and eXtreme Gradient Boosting machine learning algorithms reported in the literature. The models developed are valuable for environmental footprint assessment of biochar production and rapid system optimization.

Predicting performance of in-situ microbial enhanced oil recovery process and screening of suitable microbe-nutrient combination from limited experimental data using physics informed machine learning approach

Screening of suitable microbe-nutrient combination and prediction of oil recovery at the initial stage is essential for the success of Microbial Enhanced Oil Recovery (MEOR) technique. However, experimental and physics-based modelling approaches are expensive and time-consuming. In this study, Physics Informed Machine Learning (PIML) framework was developed to screen and predict oil recovery at a relatively lesser time and cost with limited experimental data. The screening was done by quantifying the influence of parameters on oil recovery from correlation and feature importance studies. Results revealed that microbial kinetic, operational and reservoir parameters influenced the oil recovery by 50%, 32.6% and 17.4%, respectively. Higher oil recovery is attained by selecting a microbe-nutrient combination having a higher ratio of value between biosurfactant yield and microbial yield parameters, as they combinedly influence the oil recovery by 27%. Neural Network is the best ML model for MEOR application to predict oil recovery (R²≈0.99).