Maximizing Biogas Yield Using an Optimized Stacking Ensemble Machine Learning Approach

Angelique Mukasine (), Louis Sibomana, Kayalvizhi Jayavel, Kizito Nkurikiyeyezu and Eric Hitimana
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Angelique Mukasine: African Center of Excellence in the Internet of Things, University of Rwanda, Kigali P.O. Box 3900, Rwanda
Louis Sibomana: National Council for Science and Technology, Kigali P.O. Box 2285, Rwanda
Kayalvizhi Jayavel: Creative Computing Institute, University of the Arts London, London WC1V 7EY, UK
Kizito Nkurikiyeyezu: Department of Electrical and Electronics Engineering, University of Rwanda, Kigali P.O. Box 3900, Rwanda
Eric Hitimana: African Center of Excellence in the Internet of Things, University of Rwanda, Kigali P.O. Box 3900, Rwanda

Energies, 2024, vol. 17, issue 2, 1-13

Abstract: Biogas is a renewable energy source that comes from biological waste. In the biogas generation process, various factors such as feedstock composition, digester volume, and environmental conditions are vital in ensuring promising production. Accurate prediction of biogas yield is crucial for improving biogas operation and increasing energy yield. The purpose of this research was to propose a novel approach to improve the accuracy in predicting biogas yield using the stacking ensemble machine learning approach. This approach integrates three machine learning algorithms: light gradient-boosting machine (LightGBM), categorical boosting (CatBoost), and an evolutionary strategy to attain high performance and accuracy. The proposed model was tested on environmental data collected from biogas production facilities. It employs optimum parameter selection and stacking ensembles and showed better accuracy and variability. A comparative analysis of the proposed model with others such as k-nearest neighbor (KNN), random forest (RF), and decision tree (DT) was performed. The study’s findings demonstrated that the proposed model outperformed the existing models, with a root-mean-square error (RMSE) of 0.004 and a mean absolute error (MAE) of 0.0024 for the accuracy metrics. In conclusion, an accurate predictive model cooperating with a fermentation control system can significantly increase biogas yield. The proposed approach stands as a pivotal step toward meeting the escalating global energy demands.

Keywords: energy management; biogas yield prediction; optimized stacking ensemble model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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