Methane Yield Prediction for Anaerobic Digestion using Machine Learning Models

Anaerobic digestion (AD) systems exhibit complex, nonlinear interactions between operational parameters, making methane yield prediction challenging using traditional analytical methods. This study evaluates multiple machine learning (ML) techniques to model methane production from an industrial reactor dataset containing operational and environmental variables. A structured workflow incorporating correlation analysis, dimensionality reduction, clustering, and supervised learning was implemented to identify key predictors and assess model performance. Reactor temperature emerged as the dominant factor influencing methane yield, while most other variables showed weak direct correlations. Principal component analysis and K-means clustering revealed distinct operational regimes associated with different performance levels. Baseline linear regression models achieved moderate predictive accuracy (R² ≈ 0.5), while nonlinear models—including ensemble methods and artificial neural networks—provided only marginal improvements, suggesting dataset limitations rather than algorithmic constraints. The results highlight the importance of richer biochemical and temporal data for improving predictive modelling and supporting data-driven optimization of industrial AD processes.

Machine Learning, Anaerobic Digestion, Methane Yield, ANN

1. “Statistical Review of World Energy - American Gas Association.” Accessed: Jan. 17, 2026. [Online]. Available: https://www.aga.org/research-policy/resource-library/statistical-review-of-world-energy/?utm_source=chatgpt.com [Google Scholar] [Crossref]

2. T. Z. D. De Mes, A. J. M. Stams, J. H. Reith, and G. Zeeman, “Methane production by anaerobic digestion of wastewater and solid wastes.” [Google Scholar] [Crossref]

3. H. Rutland, J. You, H. Liu, L. Bull, and D. Reynolds, “A Systematic Review of Machine-Learning Solutions in Anaerobic Digestion,” Dec. 01, 2023, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/bioengineering10121410. [Google Scholar] [Crossref]

4. T. M. Anacleto et al., “Methane yield response to pretreatment is dependent on substrate chemical composition: a meta-analysis on anaerobic digestion systems,” Sci. Rep., vol. 14, no. 1, Dec. 2024, doi: 10.1038/s41598-024-51603-9. [Google Scholar] [Crossref]

5. J. Meng, L. Shi, Y. Hu, Z. Wang, Z. Hu, and X. Zhan, “Integration of anaerobic digestion and electrodialysis for methane yield promotion and in-situ ammonium recovery,” Bioresour. Technol., vol. 402, Jun. 2024, doi: 10.1016/j.biortech.2024.130770. [Google Scholar] [Crossref]

6. A. H. Bhatt and L. Tao, “Economic perspectives of biogas production via anaerobic digestion,” Bioengineering, vol. 7, no. 3, pp. 1–19, Sep. 2020, doi: 10.3390/bioengineering7030074. [Google Scholar] [Crossref]

7. S. Ö. Cinar, S. Cinar, and K. Kuchta, “Machine Learning Algorithms for Temperature Management in the Anaerobic Digestion Process,” Fermentation, vol. 8, no. 2, Feb. 2022, doi: 10.3390/fermentation8020065. [Google Scholar] [Crossref]

8. R. Llanos-Lizcano, L. Senila, and O. C. Modoi, “Evaluation of Biochemical Methane Potential and Kinetics of Organic Waste Streams for Enhanced Biogas Production,” Agronomy, vol. 14, no. 11, Nov. 2024, doi: 10.3390/agronomy14112546. [Google Scholar] [Crossref]

9. S. Mokraoui, A. Halilu, M. A. Hashim, and M. K. Hadj-Kali, “Modeling and simulation of biomass anaerobic digestion for high biogas yield and CO2 mineralization,” Mater. Renew. Sustain. Energy, vol. 12, no. 2, pp. 105–116, Aug. 2023, doi: 10.1007/s40243-023-00233-8. [Google Scholar] [Crossref]

10. E. Piercy, X. Sun, P. R. Ellis, M. Taylor, and M. Guo, “Temporal Dynamics of Microbial Communities in Anaerobic Digestion: Influence of Temperature and Feedstock Composition on Reactor Performance and Stability.” [Google Scholar] [Crossref]

11. M. Mohammadianroshanfekr, M. Pazoki, M. B. Pejman, R. Ghasemzadeh, and A. Pazoki, “Kinetic modeling and optimization of biogas production from food waste and cow manure co-digestion,” Results in Engineering, vol. 24, Dec. 2024, doi: 10.1016/j.rineng.2024.103477. [Google Scholar] [Crossref]

12. O. J. Sinayobye et al., “Optimizing Biogas Production with Machine Learning: A Comparative Study of Predictive Models,” 2024. [Online]. Available: https://www.jisem-journal.com/ [Google Scholar] [Crossref]

13. D. A. Samuel, B. I. Eziefula, G. Orkuma, and A. Usman, “Forecasting of Biogas and Biomethane Outputs from Anaerobic Co-digestion Using Multilayer Perceptron Artificial Neural Networks (MLP-ANN),” International Journal of Multidisciplinary Approach Research and Science, vol. 3, no. 02, pp. 561–568, May 2025, doi: 10.59653/ijmars.v3i02.1529. [Google Scholar] [Crossref]

14. T. Z. D. De Mes, A. J. M. Stams, J. H. Reith, and G. Zeeman, “Methane production by anaerobic digestion of wastewater and solid wastes.” [Google Scholar] [Crossref]

15. “Artificial Neural networks for predicting methane content in biogas from livestock waste”. [Google Scholar] [Crossref]

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