Optimizing Anaerobic Digestion for Enhanced Biogas Production Using Convolutional Neural Networks: Addressing Nutrient Imbalance, Hydrolysis Limitations, and Feedstock Variability through Intelligent Process Control

Abstract

Anaerobic digestion (AD) is a promising renewable method to convert organic waste into biogas, a valuable source of bioenergy. Despite its potential, the efficiency of AD is often constrained by nutrient imbalances, hydrolysis bottlenecks, and variability in feedstock composition, which adversely affect biogas yields and process stability. This study develops a process optimization strategy using Convolutional Neural Networks (CNNs) trained on a comprehensive set of operational and physicochemical parameters such as solids content, carbon-to-nitrogen ratio, pH, temperature, retention time, nutrient supplementation, and pretreatment methods. To address challenges posed by limited and imbalanced data, techniques including synthetic minority oversampling and stratified k-fold cross-validation were applied, alongside rigorous regularization to improve model robustness and predictive accuracy. The resulting model enables enhanced prediction of biogas production performance, facilitating adaptive process adjustments to maximize methane output. Outcomes illustrate that this data-driven approach effectively mitigates nutrient-related issues, hydrolysis constraints, and feedstock variability, thereby improving overall digestion efficiency. These findings underscore the potential of integrating advanced modeling tools in AD operations to support sustainable and optimized bioenergy production. The proposed CNN model outperforms existing traditional models by delivering higher accuracy and precision in biogas production prediction, while maintaining lower time complexity, thus enabling more efficient and reliable real-time optimization of anaerobic digestion processes.

Introduction

Anaerobic digestion (AD) is a sustainable biological process that converts organic waste into biogas and nutrient-rich digestate, supporting renewable energy production and circular economy goals. However, AD performance is strongly affected by nutrient imbalance, hydrolysis limitations—particularly with lignocellulosic feedstocks—and variability in feedstock composition, which often lead to unstable methane production and reduced efficiency at industrial scales.

To address these challenges, this study proposes a Convolutional Neural Network (CNN)–based predictive and control framework for optimizing anaerobic digestion. CNNs are well suited to modeling the complex, nonlinear relationships among physicochemical, microbial, and operational parameters that govern biogas production. The model is trained on a comprehensive dataset including solids content, carbon-to-nitrogen ratio, pH, temperature, retention time, nutrient supplementation, and pretreatment methods, with techniques such as synthetic minority oversampling and stratified cross-validation used to improve robustness and generalizability.

The CNN architecture outperformed conventional models such as linear regression, traditional neural networks, and support vector machines, achieving high predictive accuracy (R² > 0.9). When integrated into a real-time process control system, the model enabled adaptive adjustments to operating conditions, improving process stability, mitigating nutrient and hydrolysis constraints, and enhancing methane yield under variable feedstock conditions.

Overall, the study demonstrates that CNN-driven intelligent control provides an effective, data-driven solution for optimizing anaerobic digestion, bridging existing research gaps and supporting sustainable biogas production and organic waste valorization.

Conclusion

This study confirmed that Convolutional Neural Networks (CNNs) are highly effective at forecasting and optimizing anaerobic digestion for biogas production. By utilizing detailed datasets—including solids concentration, carbon-to-nitrogen ratio, pH, temperature, retention time, nutrient additions, and pretreatment types—the CNN demonstrated a notable capacity to generalize across various feedstock types and process conditions, delivering both accurate biogas yield predictions and improved methane output. The use of strategies such as synthetic minority oversampling and stratified cross-validation provided a sturdy model architecture that translated into more stable process operation and elevated energy recovery. Analysis revealed that the most significant influencers on AD process efficiency are nutrient status, hydrolysis rates, and underlying feedstock diversity. The model’s interpretability enabled precise process interventions, such as real-time nutrient dosing adjustment or tailored pretreatment, leading to observable gains in yield and operational reliability. These outcomes show that data-driven, adaptive process control provides a dynamic foundation for managing industrial AD systems, representing a new standard for biogas optimization.

References

[1] Mansouri, A., Bendjaballah-Lalaoui, N., Berouaken, A., Salmani, R., Boutarka, Y. (2025). Modeling and optimization of chemical pretreatment for enhanced co-digestion of paper sludge. Laboratory of Materials Chemistry, Catalysis, and Environment, University of Science and Technology Houari Boumediene, U.S.T.H.B, Algiers; Centre de Développement des Energies Renouvelables, CDER, Algiers. Received: 31 October 2024; Accepted: 24 January 2025; Available online: 27 January 2025. https://doi.org/10.30955/gnj.06946 [2] Kasulla, S., Malik, S. J., Yadav, A., Kayusi, F., et al. (2024). Smart Nutrient Management in Anaerobic Digestion: A CNN-Powered Strategy for Optimizing Trace Elements in Biogas Production. International Journal of Science Engineering and Technology, 12(6), 14. https://doi.org/10.61463/ijset.vol.12.issue6.385 [3] Paladino, O. (2022). Data Driven Modelling and Control Strategies to Improve Biogas Quality and Production from High Solids Anaerobic Digestion: A Mini Review. Sustainability, 14, 16467. https://doi.org/10.3390/su142416467 [4] Phillip, A., Bhatt, S. M., Sharma, N., & Poudel, B. (2024). Enhanced Biogas Production Using Anaerobic Co-Digestion of Animal Waste and Food Waste: A Review. Journal of Scientific Research and Reports, 30(8), 761–781. https://doi.org/10.9734/jsrr/2024/v30i82297 [5] Kasulla, S., Malik, S. J., Kayusi, F., Zafar, S., & Bhatta, A. D. (2024). Enhancing Biogas Production from Press Mud Using Convolutional Neural Networks for Process Optimization and Yield Improvement. Partners Universal Multidisciplinary Research Journal (PUMRJ), 01(04), October-November 2024. https://doi.org/10.5281/zenodo.14202811 [6] Kasulla, S., Malik, S. J., 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(2S), 601–618. https://doi.org/10.53555/AJBR.v28i2S.6890 [7] Malik, S. J., & Kasulla, S. (2020). Biogas Production from Sugar Cane Press Mud and Sugar Cane Bagasse by Anaerobic Co-Digestion. International Journal of Engineering Applied Sciences and Technology, 5(7). https://doi.org/10.33564/IJEAST.2020.v05i07.030 [8] Kasulla, S., Malik, S. J., Zafar, S., Kayusi, F., et al. (2024). Role of Microbial Hydrolysis in Anaerobic Digestion: Enhancing Biogas Production Efficiency. Department of Botany, R.D. & S. H. National College. https://doi.org/10.5281/zenodo.13955806 [9] Kasulla, S., Malik, S. J. (2021). Enhancement in Biogas Production from Press Mud. International Journal of Waste Resources, 11, 404. [10] Bamba, J. N., Almendrala, M. C., Caparanga, A. R., & Doma, B. T. (2021). Effect of Biochemical Pretreatment and Nutrient Supplementation on Anaerobic Co-Digestion of Sugarcane Press Mud and Distillery Effluent. IOP Conference Series: Earth and Environmental Science, 801(1), 012001. https://doi.org/10.1088/1755-1315/801/1/012001 [11] Bamba, J. N., Almendrala, M. C., Caparanga, A. R., & Doma, B. T. (2021). Effect of Biochemical Pretreatment and Nutrient Supplementation on Anaerobic Co-Digestion of Sugarcane Press Mud and Distillery Effluent. IOP Conference Series: Earth and Environmental Science, 801, 012001. https://doi.org/10.1088/1755-1315/801/1/012001 [12] Malik, S. J., Gunjal, B. B., Kasulla, S., & Gunjal, A. (2019). Spentwash and Press mud to BIOCNG - An Overview. In: Bioenergy from Sugar Industry- A way Forward for Sustainability, Nijalingappa Sugar Institute, Belagavi, Karnataka, India, April 2019. https://doi.org/10.6084/m9.figshare.27304170

Copyright

Copyright © 2026 Dr. Srinivas Kasulla, Dr. S J Malik, Sumedh Bapat, Anjani Yadav, Gaurav Kathpal. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.