Data-driven and explainable AI (XAI) framework for optimizing methane yield in large-scale biogas production
Guardado en:
| Publicado en: | Sustainable Energy Research vol. 12, no. 1 (Dec 2025), p. 65 |
|---|---|
| Autor principal: | |
| Otros Autores: | |
| Publicado: |
Springer Nature B.V.
|
| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
| Etiquetas: |
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3267580384 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2198-994X | ||
| 024 | 7 | |a 10.1186/s40807-025-00220-9 |2 doi | |
| 035 | |a 3267580384 | ||
| 045 | 2 | |b d20251201 |b d20251231 | |
| 100 | 1 | |a Adeleke, Oluwatobi |u University of Johannesburg, Mechanical Engineering Science, Johannesburg, South Africa (GRID:grid.412988.e) (ISNI:0000 0001 0109 131X) | |
| 245 | 1 | |a Data-driven and explainable AI (XAI) framework for optimizing methane yield in large-scale biogas production | |
| 260 | |b Springer Nature B.V. |c Dec 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The shift to sustainable energy systems necessitates scalable techniques for the valorization of organic waste via biogas production. This study presents a comprehensive data-driven framework encompassing statistical analysis, Explainable AI (XAI), clustering, and predictive modeling of methane yield to gain deeper operational insights into large-scale biogas production. By utilizing the operational data of a large-scale biodigester in Western cape province of South Africa including key biochemical and physicochemical variables such as temperature, pH, total solids (TS), volatile solids (VS), moisture content (MC), and FOS/TAC, key insights were derived through correlation mapping, scatter analysis, SHapley Additive exPlanations (SHAP)-based XAI for ranking digestion operational features, Principal component analysis (PCA) for addressing multicollinearity, and k-means cluster analysis to identify the operational clusters or groups which highlights critical shifts in system stability. Moreover, ensemble learning approaches, namely, XGBoost, Random Forest, as well as Support Vector Machine and Artificial Neural Network, were developed for methane yield prediction. The SHAP-based XAI identified FOS/TAC, volatile solids (VS), and moisture content (MC) as the most influential predictors of methane yield, while PCA explains 74% of the data variance in three Principal components (PCs), with PC1 dominated by VS, MC, and temperature as key drivers of methane yield. K-means clustering uncovered three distinct operational clusters, offering actionable guidance for feedstock management and process stabilization. Feedstock regression further established municipal solid waste (MSW) as the optimal input for maximizing methane output, with processed organic waste (POW) serving as an effective co-substrate. XGBoost achieved the best performance with an RMSE value of 1.18, followed by Random Forest (RMSE = 1.83), demonstrating the robustness of ensemble models in handling non-linear operational datasets. The research methodology is limited by its reliance on past operational data from a single digester and a lack of direct optimization experiments. However, the research strongly demonstrates the potential of data-driven approaches not only as powerful standalone tools but also as vital complements to experimental investigations. By transforming raw plant data into actionable intelligence, this study offers a scalable methodology for improving energy recovery, enhancing process control, and guiding sustainable development in industrial-scale bioenergy applications. | |
| 651 | 4 | |a South Africa | |
| 653 | |a Biogas | ||
| 653 | |a Organic wastes | ||
| 653 | |a Yield forecasting | ||
| 653 | |a Data smoothing | ||
| 653 | |a Principal components analysis | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Sustainable energy | ||
| 653 | |a Methane | ||
| 653 | |a Optimization | ||
| 653 | |a Energy recovery | ||
| 653 | |a Municipal solid waste | ||
| 653 | |a Volatile solids | ||
| 653 | |a Raw materials | ||
| 653 | |a Moisture content | ||
| 653 | |a Renewable energy | ||
| 653 | |a Machine learning | ||
| 653 | |a Strategic planning | ||
| 653 | |a Energy consumption | ||
| 653 | |a Process control | ||
| 653 | |a Prediction models | ||
| 653 | |a Clustering | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Sustainable development | ||
| 653 | |a Statistical methods | ||
| 653 | |a Alternative energy sources | ||
| 653 | |a Ensemble learning | ||
| 653 | |a Sustainability | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Municipal waste management | ||
| 653 | |a Explainable artificial intelligence | ||
| 653 | |a Circular economy | ||
| 653 | |a Food waste | ||
| 653 | |a Water content | ||
| 653 | |a Case studies | ||
| 653 | |a Cluster analysis | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Solid waste management | ||
| 653 | |a Support vector machines | ||
| 653 | |a Process controls | ||
| 653 | |a Variables | ||
| 653 | |a Waste to energy | ||
| 653 | |a Energy efficiency | ||
| 653 | |a Research methods | ||
| 653 | |a Solid wastes | ||
| 653 | |a Systems stability | ||
| 653 | |a Neural networks | ||
| 653 | |a Vector quantization | ||
| 653 | |a Economic | ||
| 700 | 1 | |a Jen, Tien-Chien |u University of Johannesburg, Mechanical Engineering Science, Johannesburg, South Africa (GRID:grid.412988.e) (ISNI:0000 0001 0109 131X) | |
| 773 | 0 | |t Sustainable Energy Research |g vol. 12, no. 1 (Dec 2025), p. 65 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3267580384/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3267580384/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3267580384/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |