Machine Learning Approach for Determining and Optimizing Influential Factors of Biogas Production from Lignocellulosic Biomass

Lignocellulose-Derived Energy Materials and Chemicals: A Review on Synthesis Pathways and Machine Learning Applications

Lignocellulose biomass, Earth's most abundant renewable resource, is crucial for sustainable production of high-value chemicals and bioengineered materials, especially for energy storage. Efficient pretreatment is vital to boost lignocellulose conversion to bioenergy and biomaterials, cut costs, and broaden its energy-sector applications. Machine learning (ML) has become a key tool in this field, optimizing pretreatment processes, improving decision-making, and driving innovation in lignocellulose valorization for energy storage. This review explores main pretreatment strategies - physical, chemical, physicochemical, biological, and integrated methods - evaluating their pros and cons for energy storage. It also stresses ML's role in refining these processes, supported by case studies showing its effectiveness. The review examines challenges and opportunities of integrating ML into lignocellulose pretreatment for energy storage, underlining pretreatment's importance in unlocking lignocellulose's full potential. By blending process knowledge with advanced computational techniques, this work aims to spur progress toward a sustainable, circular bioeconomy, particularly in energy storage solutions.

Artificial intelligence-based modeling of biogas production in a combined microbial electrolysis cell-anaerobic digestion system using artificial neural networks and adaptive neuro-fuzzy inference system

Accurate prediction of biogas production is essential for optimizing process performance, enhancing system stability, and enabling efficient resource management in bioenergy applications. The integrated microbial electrolysis cell and anaerobic digestion (MECAD) system is a novel technology that enables higher energy recovery through the application of external voltage, offering advantages over conventional anaerobic digestion (AD). Due to the energy-intensive nature of MECAD, optimizing biogas production is particularly important to ensure energy efficiency and system feasibility. In this study, the biogas production rate of a MECAD system was predicted using two machine learning (ML)-based models: an artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS). The input variables of the models-including pH, oxidation-reduction potential (ORP), total solids (TS) and volatile solids (VS) removal, hydraulic retention times (HRT), organic loading rates (OLR), applied voltage, and current production (CP)-were collected from a MECAD reactor fed with cattle manure and operated under different conditions. The ANN model achieved R2 values of 0.9844 for the testing dataset and 0.9760 for the overall dataset, with corresponding biogas production rates of 171 mL/day and 204 mL/day, respectively. The index of agreement (IA) was 0.9960 for testing and 0.9939 for overall data. Similarly, the factor of two (FA2) values were 0.9962 (testing) and 0.9956 (overall), while the mean bias (MB) was calculated as 10.05 mL/day for testing and 8.52 mL/day for overall data. In comparison, the ANFIS model yielded R2 values of 0.9811 (testing) and 0.9774 (overall), with RMSE values of 188 mL/day and 198 mL/day, respectively. The IA values were 0.9952 and 0.9943; FA2 values were 0.9962 and 0.9987; and MB values were 6.81 mL/day and 2.91 mL/day for testing and overall datasets, respectively. The results demonstrated that both machine learning-based models effectively and accurately predicted the biogas production in a laboratory-scale MECAD reactor utilizing cattle manure as the substrate.

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.

Comparative assessment of microalgal growth kinetic models based on light intensity and biomass concentration

The comprehensive evaluation and validation of mathematical models for microalgal growth dynamics are essential for improving cultivation efficiency and optimising photobioreactor design. A considerable gap in comprehending the relation between microalgal growth, light intensity and biomass concentration arises since many studies focus solely on associating one of these factors. This paper compares microalgal growth kinetic models, specifically focusing on the combined impact of light intensity and biomass concentration. Considering a dataset (experimental results and literature values) concerning Chlorella vulgaris, nine kinetic models were assessed. Bannister and Grima models presented the best fitting performance to experimental data (RMSE ≤ 0.050 d-1; R2≥0.804; d2≥0.943). Cultivation conditions conducting photoinhibition were identified in some kinetic models. After testing these models on independent datasets, Bannister and Grima models presented superior predictive performance (RMSE = 0.022-0.023 d-1; R2 = 0.878-0.884; d2: 0.976-0.975). The models provide valuable tools for predicting microalgal growth and optimising operational parameters, reducing the need for time-consuming and costly experiments.

Machine learning assisted modelling of anaerobic digestion of waste activated sludge coupled with hydrothermal pre-treatment

This study utilizes decision-tree-based models, including Random Forest, XGBoost, artificial neural networks (ANNs), support vector machine regressors, and K nearest neighbors algorithms, to predict sludge solubilization and methane yield in hydrothermal pretreatment (HTP) coupled with anaerobic digestion (AD) processes. Analyzing two decades of published research, we find that ANN models exhibit superior fitting accuracy for solubilization prediction, while decision-tree models excel in methane yield prediction. Pretreatment temperature is identified as pivotal among various variables, and heating time surprisingly emerges as equally significant as holding time for solubilization and surpasses it for methane yield. Contrary to prior expectations, the HTP method's impact on sludge solubilization and AD performance is minimal. This study underscores data-driven models' potential as resource-efficient tools for optimizing advanced AD processes with HTP. Notably, our research spans nearly two decades of lab, pilot, and full-scale studies, offering novel insights not previously explored.

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.

Insights into the recent advances of agro-industrial waste valorization for sustainable biogas production

Recent years have seen a transition to a sustainable circular economy model that uses agro-industrial waste biomass waste to produce energy while reducing trash and greenhouse gas emissions. Biogas production from lignocellulosic biomass (LCB) is an alternative option in the hunt for clean and renewable fuels. Different approaches are employed to transform the LCB to biogas, including pretreatment, anaerobic digestion (AD), and biogas upgradation to biomethane. To maintain process stability and improve AD performance, machine learning (ML) tools are being applied in real-time monitoring, predicting, and optimizing the biogas production process. An environmental life cycle assessment approach for biogas production systems is essential to calculate greenhouse gas emissions. The current review presents a detailed overview of the utilization of agro-waste for sustainable biogas production. Different methods of waste biomass processing and valorization are discussed that contribute towards developing an efficient agro-waste to biogas-based circular economy.