Advancing Biogas Production Forecasting Using Artificial Intelligence: A Comprehensive Review of Models and Applications

Document Type : Review article

Authors

Mechanics of Biosystems Engineering Department, College of Aburaihan, University of Tehran, Tehran, Iran.

10.22103/bbr.2025.25660.1128

Abstract

Artificial intelligence (AI) plays a transformative role in improving the efficiency of biogas production by providing advanced tools for predicting and optimizing anaerobic digestion processes as a sustainable source of organic waste management and renewable energy supply. This study provides a systematic review of the applications of AI in biogas production prediction and, by reviewing recent studies, evaluates statistical, machine learning, and hybrid models and compares the performance of algorithms such as Random Forest and Artificial Neural Networks (ANN). These algorithms have shown outstanding performance in recent studies due to their ability to model nonlinear and dynamic behaviors. However, challenges such as inconsistent data quality, biochemical complexities, and generalizability limitations have limited the full exploitation of these technologies. Through a comprehensive literature review, this study identifies the strengths and weaknesses of existing models and proposes innovative solutions, including the integration of real-time data based on the Internet of Things (IoT), the development of hybrid models, and the utilization of transfer learning. The findings highlight the potential of artificial intelligence in improving the efficiency of biogas systems, reducing operating costs, and supporting sustainable energy planning, and provide directions for the development of intelligent and scalable forecasting tools.

Keywords

Main Subjects

References

Adeoba, M., Pandelani, T., Ngwangwa, H., & Masebe, T. (2025). The Role of Artificial Intelligence Technology in the Fulfilment of Sustainable Development Goals in Biogas Production. CONECT. International Scientific Conference of Environmental and Climate Technologies. https://doi.org/10.7250/CONECT.2025.040
Aguida, M. A., Ouchani, S., & Benmalek, M. (2021). An IoT-based framework for an optimal monitoring and control of cyber-physical systems: application on biogas production system. Proceedings of the 11th International Conference on the Internet of Things (pp. 143-149). https://doi.org/10.1145/3494322.3494341
Angelidaki, I., & Ellegaard, L. (2003). Codigestion of manure and organic wastes in centralized biogas plants: status and future trends. Applied biochemistry and biotechnology, 109(1), 95-105. https://doi.org/10.1385/ABAB:109:1-3:95
Arumugham, V., Ghanimi, H. M., Pustokhin, D. A., Pustokhina, I. V., Ponnam, V. S., Alharbi, M., Krishnamoorthy, P., & Sengan, S. (2023). An artificial-intelligence-based renewable energy prediction program for demand-side management in smart grids. Sustainability, 15(6), 5453. https://doi.org/10.3390/su15065453
Chen, L., He, P., Zou, J., Zhang, H., Peng, W., & Lü, F. (2025). Scalable and interpretable automated machine learning framework for biogas prediction, optimization, and stability monitoring in industrial-scale dry anaerobic digestion. Chemical Engineering Journal, 519, 165482. https://doi.org/10.1016/j.cej.2025.165482
Clifford, J. L., Chan, Y. J., Yusof, M. A. B. M., Tiong, T. J., Lim, S. S., Lee, C. S., & Tong, W.-Y. (2025). Predictive Modelling of H2S Removal from Biogas Generated from Palm Oil Mill Effluent (POME) Using a Biological Scrubber in an Industrial Biogas Plant: Integration of Artificial Neural Network (ANN) and Process Simulation §. Food Technology and Biotechnology, 63(2), 124-133. https://doi.org/10.17113/ftb.63.02.25.8792
Colak, M. B., & Özhan, E. (2025). Renewable Energy Forecasting in Turkey: Analytical Approaches. Journal of Intelligent Systems: Theory and Applications, 8(1), 25-34. https://doi.org/10.38016/jista.1447980
De Clercq, D., Jalota, D., Shang, R., Ni, K., Zhang, Z., Khan, A., Wen, Z., Caicedo, L., & Yuan, K. (2019). Machine learning powered software for accurate prediction of biogas production: A case study on industrial-scale Chinese production data. Journal of cleaner production, 218, 390-399. https://doi.org/10.1016/j.jclepro.2019.01.031
De Clercq, D., Wen, Z., Fei, F., Caicedo, L., Yuan, K., & Shang, R. (2020). Interpretable machine learning for predicting biomethane production in industrial-scale anaerobic co-digestion. Science of the Total Environment, 712, 134574. https://doi.org/10.1016/j.scitotenv.2019.134574
de Lima Pacheco, M., Ramos, R. C., & Fernando, J. S. (2025). Artificial Intelligence in Developing Countries: Challenges and Opportunities_An African view and its Application in Angola. Revista Gênero e Interdisciplinaridade, 6(2), 60-82. https://doi.org/10.51249/gei.v6i02.2455
Dewi, E. P., Sumarsono, J., Amuddin, A., & Kompyang, I. G. M. (2024). Development of data acquisition biogas monitoring system based on IoT. Jurnal Agrotek Ummat, 11(1), 1-15. https://doi.org/10.31764/jau.v11i1.20574
Dittmer, C., Krümpel, J., & Lemmer, A. (2021). Power demand forecasting for demand-driven energy production with biogas plants. Renewable Energy, 163, 1871-1877. https://doi.org/10.1016/j.renene.2020.10.099
Drudi, R., Drudi, K. C. R., dos Anjos Silva, Í., Antonio, G. C., & de Campos Leite, J. T. (2024). Mathematical Modeling of Biogas Production in Sanitary Landfills. Revista de Gestão Social e Ambiental, 18(11), 1-17. https://doi.org/10.24857/rgsa.v18n11-010
Ellacuriaga, M., García-Cascallana, J., & Gómez, X. (2021). Biogas production from organic wastes: Integrating concepts of circular economy. Fuels, 2(2), 144-167. https://doi.org/10.3390/fuels2020009
Gaikwad, P., Chavan, A., Ghodekar, S., Marale, D., & Karne, H. (2025). Predictive Modeling of Biogas Production Using Machine Learning. International Journal of Latest Technology in Engineering, Management & Applied Science, 14(5), 54-61. https://doi.org/10.51583/IJLTEMAS.2025.140500009
Gouiza, N., Jebari, H., & Reklaoui, K. (2024). Integration of iot-enabled technologies and artificial intelligence in diverse domains: Recent advancements and future trends. Journal of Theoretical and Applied Information Technology, 102(5), 1975-2029.
Isenkul, M. E., Güneş-Durak, S., Kocak, Y. P., Pir, İ., Tüfekci, M., Demirkol, G. T., Sevgen, S., Çığgın, A. S., & Tüfekci, N. (2025). Predicting biogas production in real scale anaerobic digester under dynamic conditions with machine learning approach. Environmental Research Communications, 7(6), 065016. https://doi.org/10.1088/2515-7620/ade03b
Janke, L., Leite, A., Nikolausz, M., Schmidt, T., Liebetrau, J., Nelles, M., & Stinner, W. (2015). Biogas production from sugarcane waste: assessment on kinetic challenges for process designing. International journal of molecular sciences, 16(9), 20685-20703. https://doi.org/10.3390/ijms160920685
Kasulla, S., Malik, S., Yadav, A., Kathpal, G., & Zafar, S. (2025). Harnessing Convolutional Neural Networks for The Optimization of Anaerobic Digestion of Sugarcane Bagasse: A Novel Approach to Pretreatment Strategies and Microbial Activity Prediction. African Journal of Biomedical Research, 28, 601-618. https://doi.org/10.53555/8fp4dq69
Labatut, R. A., Angenent, L. T., & Scott, N. R. (2011). Biochemical methane potential and biodegradability of complex organic substrates. Bioresource technology, 102(3), 2255-2264. https://doi.org/10.1016/j.biortech.2010.10.035
Mathur, R., Sharma, M. K., Loganathan, K., Abbas, M., Hussain, S., Kataria, G., Alqahtani, M. S., & Srinivas Rao, K. (2024). Modeling of two-stage anaerobic onsite wastewater sanitation system to predict effluent soluble chemical oxygen demand through machine learning. Scientific Reports, 14(1), 1835. https://doi.org/10.1038/s41598-023-50805-x
Mittal, S., Ahlgren, E. O., & Shukla, P. (2018). Barriers to biogas dissemination in India: A review. Energy Policy, 112, 361-370.
Mukasine, A., Sibomana, L., Jayavel, K., Nkurikiyeyezu, K., & Hitimana, E. (2023). Correlation Analysis Model of Environment Parameters Using IoT Framework in a Biogas Energy Generation Context. Future Internet, 15(8), 265. https://doi.org/10.3390/fi15080265
Mukasine, A., Sibomana, L., Jayavel, K., Nkurikiyeyezu, K., & Hitimana, E. (2024). Maximizing biogas yield using an optimized stacking ensemble machine learning approach. Energies, 17(2), 364. https://doi.org/10.3390/en17020364
Nazmi, H., Siau, N. Z., Bramantoro, A., & Suhaili, W. S. (2023). Predictive modeling of marine fish production in brunei darussalam’s aquaculture sector: A comparative analysis of machine learning and statistical techniques. International Journal of Advanced and Applied Sciences, 10(7), 109-126. https://doi.org/10.21833/ijaas.2023.07.013
Nguyen, G., Dlugolinsky, S., Bobák, M., Tran, V., Lopez Garcia, A., Heredia, I., Malík, P., & Hluchý, L. (2019). Machine learning and deep learning frameworks and libraries for large-scale data mining: a survey. Artificial Intelligence Review, 52(1), 77-124. https://doi.org/10.1007/s10462-018-09679-z
Olatunji, O. O., Adedeji, P. A., Madushele, N., Rasmeni, Z. Z., & van Rensburg, N. J. (2024). Evolutionary optimization of biogas production from food, fruit, and vegetable (FFV) waste. Biomass Conversion and Biorefinery, 14(11), 12113-12125. https://doi.org/10.1007/s13399-023-04506-0
Paleyes, A., Urma, R.-G., & Lawrence, N. D. (2022). Challenges in deploying machine learning: a survey of case studies. ACM computing surveys, 55(6), 1-29. https://doi.org/10.1145/3533378
Pilarski, K., Pilarska, A. A., & Dach, J. (2025). Biogas as renewable energy source: A brief overview. Journal of Ecological Engineering, 26(7), 408–416. https://doi.org/10.12911/22998993/203376
Raven, R. P., & Gregersen, K. H. (2007). Biogas plants in Denmark: successes and setbacks. Renewable and sustainable energy reviews, 11(1), 116-132. https://doi.org/10.1016/j.rser.2004.12.002
Rezaeifar, J., Rohani, A., & Ebrahimi-Nik, M. (2024). Unleashing Dairy Manure's Biogas Potential: A Michaelis-Menten Modeling Approach. Biomechanism and Bioenergy Research, 3(1), 46-55. https://doi.org/10.22103/bbr.2024.22854.1076
Rutland, H., You, J., Liu, H., Bull, L., & Reynolds, D. (2023). A systematic review of machine-learning solutions in anaerobic digestion. Bioengineering, 10(12), 1410. https://doi.org/10.3390/bioengineering10121410
Sun, J., Xu, Y., Nairat, S., Zhou, J., & He, Z. (2023). Prediction of biogas production in anaerobic digestion of a full‐scale wastewater treatment plant using ensembled machine learning models. Water Environment Research, 95(6), e10893. https://doi.org/10.1002/wer.10893
Theuerl, S., Klang, J., & Prochnow, A. (2019). Process disturbances in agricultural biogas production—Causes, mechanisms and effects on the biogas microbiome: A review. Energies, 12(3), 365. https://doi.org/10.3390/en12030365
Tisocco, S., Weinrich, S., Lyons, G., Wills, M., Zhan, X., & Crosson, P. (2024). Application of a simplified ADM1 for full-scale anaerobic co-digestion of cattle slurry and grass silage: assessment of input variability. Frontiers of Environmental Science & Engineering, 18(4), 50. https://doi.org/10.1007/s11783-024-1810-9
Tryhuba, I., Tryhuba, A., Hutsol, T., Cieszewska, A., Andrushkiv, O., Glowacki, S., Bryś, A., Slobodian, S., Tulej, W., & Sojak, M. (2024). Prediction of Biogas Production Volumes from Household Organic Waste Based on Machine Learning. Energies, 17(7), 1786. https://doi.org/10.3390/en17071786
Wang, T., Wang, X., Ma, R., Li, X., Hu, X., Chan, F. T., & Ruan, J. (2020). Random forest-bayesian optimization for product quality prediction with large-scale dimensions in process industrial cyber–physical systems. IEEE Internet of Things Journal, 7(9), 8641-8653.
Wang, Y., Huntington, T., & Scown, C. D. (2021). Tree-based automated machine learning to predict biogas production for anaerobic co-digestion of organic waste. ACS Sustainable Chemistry & Engineering, 9(38), 12990-13000. https://doi.org/10.1101/2021.07.12.452124
Yildirim, O., & Ozkaya, B. (2023). Prediction of biogas production of industrial scale anaerobic digestion plant by machine learning algorithms. Chemosphere, 335, 138976. https://doi.org/10.1016/j.chemosphere.2023.138976
Zhu, J.-J., Yang, M., & Ren, Z. J. (2023). Machine learning in environmental research: common pitfalls and best practices. Environmental Science & Technology, 57(46), 17671-17689. https://doi.org/10.1021/acs.est.3c00026
Biomechanism and Bioenergy Research
Volume 4, Issue 3 - Serial Number 9
September 2025
Pages 29-41

Files

History

  • Receive Date: 29 July 2025
  • Revise Date: 05 September 2025
  • Accept Date: 09 September 2025

Share

How to cite

Statistics