Enhancing Anaerobic Codigestion Technology with Machine Learning Approach for Climate Change Mitigation in Palm Oil Agro-Industries of Cameroon

Document Type : Original Research

Authors

1 College of Technology, University of Buea, PoBox 63, Buea-Cameroon.

2 Laboratory of water management, Rural Engineering Department, Faculty of Agronomy and Agricultural Sciences, University of Dschang, PoBox 222, Dschang-Cameroon.

3 Laboratory of Renewable energy, Rural Engineering Department, Faculty of Agronomy and Agricultural Sciences, University of Dschang, PoBox 222, Dschang-Cameroon.

10.22103/bbr.2025.25506.1123

Abstract

Applying machine learning to anaerobic co-digestion offers potential benefits for the palm oil industry and climate change mitigation. Conventional prediction models are often complex and lack generalization, while studies on palm oil mill effluent (POME) and cow dung have not fully addressed optimal substrate ratios and operating conditions. In this study, response surface methodology (RSM) and a decision tree (DT) were applied to model and optimize POME–cow dung co-digestion. RSM examined the relationship between mixing ratios, temperature, pressure, and pH, while the DT classified biogas volume as low, high, or very high. Results indicated that biogas yield significantly depended on mixing ratios, with optimal performance at 1:1 and 0.5:1 ratios, corresponding to temperatures of 19°C and 39°C. The correlation coefficient for prediction reached 31%, and sensitivity analysis revealed temperature as the most influential factor, followed by pH and pressure. Overall, integrating machine learning into co-digestion modeling can reduce operating costs and enhance the sustainability of palm oil agro-industries.

Keywords

References

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Biomechanism and Bioenergy Research
Volume 4, Issue 3 - Serial Number 9
September 2025
Pages 11-28

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History

  • Receive Date: 07 July 2025
  • Revise Date: 29 August 2025
  • Accept Date: 01 September 2025

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