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

Ensemble machine learning prediction of anaerobic co-digestion of manure and thermally pretreated harvest residues

This study aimed to clarify the statistical accuracy assessment approaches used in recent biogas prediction studies using state-of-the-art ensemble machine learning approach according to 10-fold cross-validation in 100 repetitions. Three thermally pretreated harvest residue types (maize stover, sunflower stalk and soybean straw) and manure were anaerobically co-digested, measuring biogas and methane yield alongside eight thermal preprocessing and biomass covariates. These were the inputs to an ensemble machine learning approach for biogas and methane yield prediction, employing three feature selection approaches. The Support Vector Machine prediction with the Recursive Feature Elimination resulted in the highest prediction accuracy, achieving the coefficient of determination of 0.820 and 0.823 for biogas and methane yield prediction, respectively. This study demonstrated an extreme dependency of prediction accuracy to input dataset properties, which could only be mitigated with ensemble machine learning and strongly suggested that the split-sample approach, often used in previous studies, should be avoided.

Assessing microbial stability and predicting biogas production in full-scale thermophilic dry methane fermentation of municipal solid waste

Compared to typical anaerobic digestion processes, little is known about both sludge microbial compositions and biogas production models for full-scale dry methane fermentation treating municipal solid waste (MSW). The anaerobic sludge composed of one major hydrogenotrophic methanogen (Methanoculleus) and syntrophic acetate oxidizing bacteria (e.g., Caldicoprobacter), besides enrichment of MSW degraders such as Clostridia. The core population remained phylogenetically unchanged during the fermentation process, regardless of amounts of MSW supplied (∼35 ton/d) or biogas produced (∼12000 Nm3/d). Based on the correlations observed between feed amounts of MSW from 6 days in advance to the current day and biogas output (the strongest correlation: r = 0.77), the best multiple linear regression (MLR) model incorporating the temperature factor was developed with a good prediction for validation data (R2 = 0.975). The proposed simple MLR method with only data on the feedstock amounts will help decision-making processes to prevent low-efficient biogas production.

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.

Boosting thermophilic anaerobic digestion with conductive materials: Current outlook and future prospects

Thermophilic anaerobic digestion (TAD) can provide superior process kinetics, higher methane yields, and more pathogen destruction than mesophilic anaerobic digestion (MAD). However, the broader application of TAD is still very limited, mainly due to process instabilities such as the accumulation of volatile fatty acids and ammonia inhibition in the digesters. An emerging technique to overcome the process disturbances in TAD and enhance the methane production rate is to add conductive materials (CMs) to the digester. Recent studies have revealed that CMs can promote direct interspecies electron transfer (DIET) among the microbial community, increasing the TAD performance. CMs exhibited a high potential for alleviating the accumulation of volatile fatty acids and inhibition caused by high ammonia levels. However, the types, properties, sources, and dosage of CMs can influence the process outcomes significantly, along with other process parameters such as the organic loading rates and the type of feedstocks. Therefore, it is imperative to critically review the recent research to understand the impacts of using different CMs in TAD. This review paper discusses the types and properties of CMs applied in TAD and the mechanisms of how they influence methanogenesis, digester start-up time, process disturbances, microbial community, and biogas desulfurization. The engineering challenges for industrial-scale applications and environmental risks were also discussed. Finally, critical research gaps have been identified to provide a framework for future research.

Mechanisms, methods and applications of machine learning in bio-alcohol production and utilization: A review

Bio-alcohols have been proven promising alternatives to fossil fuels. Machine learning (ML), as an analytical tool for uncovering intrinsic correlations and mining data connotations, is also becoming widely used in the field of bio-alcohols. This article reviews the mechanisms, methods, and applications of ML in the bio-alcohols field. In terms of mechanisms, we describe the workflow of ML applications, emphasizing the importance of a well-defined research problem and complete feature engineering for a robust model. Prediction and optimization are the main application scenarios. In terms of methods, we illustrate the characteristics of different ML models and analyze their applicability in the bio-alcohol field. The role of ML in the production of bio-methanol by pyrolysis and gasification, as well as in the three stages of fermentation for bioethanol production are highlighted. In terms of utilization, ML is used to optimize engine performance and reduce emissions. This review provides guidance on how to use novel ML methods in the bio-alcohol field, showing the potential of ML to streamline work in the whole biofuel field.