Forecasting Of Biogas And Biomethane Outputs From Anaerobic Co-Digestion Using Multilayer Perceptron Artificial Neural Networks (Mlp-Ann)

Abstract: The intricate, nonlinear interactions between several process parameters make it difficult to forecast biogas and biomethane (bioCH₄) yields in anaerobic digestion systems with any degree of accuracy. These dynamics are frequently not well captured by conventional kinetic models, particularly in co-digestion systems with heterogeneous substrates. This was addressed by developing and testing a Multilayer Perceptron Artificial Neural Network (MLP-ANN) for predicting cumulative biogas and bioCH₄ volumes from the anaerobic co-digestion of soymilk dregs and cow manure based on key operational parameters: pH, Substrate Mixing Ratio (SMR), and Hydraulic Retention Time (HRT) while the temperature was constant (33 ± 1 °C). An MLP architecture with a 3-3-2 network structure was employed, comprising three input neurons, two hidden layers, and two output neurons. The model was trained and validated using standardized input variables, with hyperbolic tangent activation in the hidden layers and identity activation in the output layer. The ANN model demonstrated excellent predictive accuracy, achieving average R² values of 0.980 for biogas and 0.976 for BioCH₄ during five-fold cross-validation, with correspondingly low mean absolute error (MAE) and root mean square error (RMSE) values. Parameter estimates indicated that HRT had the most significant influence on gas production, while SMR and pH served as important supporting factors. Practical recommendations include optimizing HRT and SMR settings and maintaining stable pH levels to maximize biogas system efficiency. The study highlights the importance of ANN models for improving operational planning and optimizing biogas production processes. The developed model offers a robust tool for predicting energy recovery outcomes in anaerobic digestion systems and supports the broader adoption of machine learning in biogas process optimization.

Anaerobic Co-digestion, Artificial Neural Networks, Biogas Output Prediction

1. Angelidaki, I., Xie, L., Luo, G., Zhang, Y., Oechsner, H., Lemmer, A., ... & Kougias, P. G. (2019). Biogas upgrading: current and emerging technologies. Biofuels: alternative feedstocks and conversion processes for the production of liquid and gaseous biofuels, 817-843. [Google Scholar] [Crossref]

2. Avinash, L. S., & Mishra, A. (2024). Comparative evaluation of Artificial Intelligence-based Models and Kinetic Studies in the Prediction of Biogas from Anaerobic Digestion of MSW. Fuel, 367, 131545. [Google Scholar] [Crossref]

3. Cruz, I. A., Chuenchart, W., Long, F., Surendra, K. C., Andrade, L. R. S., Bilal, M., ... & Ferreira, L. F. R. (2022). Application of machine learning in anaerobic digestion: Perspectives and challenges. Bioresource Technology, 345, 126433. [Google Scholar] [Crossref]

4. Farizawani, A. G., Puteh, M., Marina, Y., & Rivaie, A. (2020). A review of artificial neural network learning rule based on multiple variants of conjugate gradient approaches. In Journal of Physics: Conference Series, 529(2) p. 022040. IOP Publishing. [Google Scholar] [Crossref]

5. Gupta, R., Zhang, L., Hou, J., Zhang, Z., Liu, H., You, S., ... & Li, W. (2023). Review of explainable machine learning for anaerobic digestion. Bioresource technology, 369, 128468. [Google Scholar] [Crossref]

6. Gustafsson, M., Meneghetti, R., Souza Marques, F., Trim, H., Dong, R., Al Saedi, T., Rasi, S., Thual, J., Kornatz, P., Wall, D., Berntsen, C., Saxegaard, S., Lyng, K.A., Nägele, H.J., Heaven, S., Bywater, A. (2024). A perspective on the state of the biogas industry from selected member countries. Gustafsson, M., Liebetrau, J. (Ed.) IEA Bioenergy Task 37, 2024:2 [Google Scholar] [Crossref]

7. Hornick, S. W., & Preparata, F. P. (1991). Deterministic P-RAM simulation with constant redundancy. Information and Computation, 92(1), 81-96. [Google Scholar] [Crossref]

8. IBM SPSS (2021). Statistics for Windows, Version 28.0. Armonk, NY: IBM Corp. [Google Scholar] [Crossref]

9. Ibrahim, M., Haider, A., Lim, J. W., Mainali, B., Aslam, M., Kumar, M., & Shahid, M. K. (2024). Artificial neural network modelling for the prediction, estimation, and treatment of diverse wastewaters: A comprehensive review and future perspective. Chemosphere, 142860. [Google Scholar] [Crossref]

10. Itodo, I. N., Bala, E. J., & Sambo, A. S. (2021). Biogas technology in Nigeria. CRC Press. [Google Scholar] [Crossref]

11. Jang, H. M., et al. (2018). Prediction of methane production potential of anaerobic co-digestion of sewage sludge and food waste using artificial neural networks. Bioresource Technology, 248, 179–187. [Google Scholar] [Crossref]

12. Komarysta, B., Dzhygyrey, I., Bendiuh, V., Yavorovska, O., Andreeva, A., Berezenko, K., ... & Maidanskyi, I. (2023). Optimizing biogas production using artificial neural network. Eastern-European Journal of Enterprise Technologies, 122(8). [Google Scholar] [Crossref]

13. Kougias, P. G., & Angelidaki, I. (2018). Biogas and its opportunities—A review. Frontiers of Environmental Science & Engineering, 12, 1-12. [Google Scholar] [Crossref]

14. Ling, J. Y. X., Chan, Y. J., Chen, J. W., Chong, D. J. S., Tan, A. L. L., Arumugasamy, S. K., & Lau, P. L. (2024). Machine learning methods for the modelling and optimisation of biogas production from anaerobic digestion: a review. Environmental Science and Pollution Research, 31(13), 19085-19104. [Google Scholar] [Crossref]

15. Mohd-Ali, J., Hussain, M. A., Tade, M. O., & Zhang, J. (2015). Artificial Intelligence techniques applied as estimator in chemical process systems – A literature survey. Expert Systems with Applications, 42(14), 5915–5931. https://doi.org/10.1016/j.eswa.2015.03.023 [Google Scholar] [Crossref]

16. Montesinos-Lopez, O. A., Montesinos López, A., & Crossa, J. (2022). Multivariate statistical machine learning methods for genomic prediction (p. 691). Springer Nature. [Google Scholar] [Crossref]

17. Olatunji, K. O., Madyira, D. M., Ahmed, N. A., Adeleke, O., & Ogunkunle, O. (2023). Modelling the biogas and methane yield from anaerobic digestion of Arachis hypogea shells with combined pretreatment techniques using machine learning approaches. Waste and Biomass Valorization, 14(4), 1123-1141. [Google Scholar] [Crossref]

18. Orkuma, J. G., Samuel, D. A., Eziefula, B. I., & Okoro, O. M. (2024). Enhancing Biogas Yield by Co-Digestion of Cattle Dung and Acha (Digitaria Exilis) Hulls. International Journal of Innovative Science and Research Technology, 9 (12). https://doi.org/10.5281/zenodo.14603613 [Google Scholar] [Crossref]

19. Orkuma, J. G., Samuel, D. A., Eziefula, B. I., & Yakubu, D. K. (2025). Assessment of Biogas potential of Codigested Cow Manure with Soymilk Dreg. International Journal of Innovative Science and Research Technology, 10 (3). http://doi.org/10.38124/ijisrt/25mar2025 [Google Scholar] [Crossref]

20. Sarma, M., Sarma, K. K., Sarma, M., & Sarma, K. K. (2014). Fundamental considerations of ANN. Phoneme-Based Speech Segmentation using Hybrid Soft Computing Framework, 47-75. [Google Scholar] [Crossref]

21. Surra, E., Bernardo, M., Lapa, N., Esteves, I., Fonseca, I., Mota, J.P., (2019). Biomethane production through anaerobic co-digestion with Maize Cob Waste based on a biorefinery concept: A review. Journal of Environmental Management, 249: 1-21. https://doi.org/10.1016/j.jenvman.2019. 109351 [Google Scholar] [Crossref]

• Exploring the Concept of Generative Artificial Intelligence: A Narrative Review
• Design, Development, and Evaluation of a Critiquing-Based Mobile-Web Employment Recommender System
• Integrating Bhagavad Gita Principles with Modern Supply Chain Management: A Framework for Ethical and Resilient Operations
• Soilless Indoor Farming: A Systematic Review of Iot-Based Monitoring Systems and Physiochemical Characterization Methods for Lactuca Sativa
• Financial Awareness: A Survey of Students in Bhopal