Maximizing Biogas Yield Using an Optimized Stacking Ensemble Machine Learning Approach
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 improvin...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/2/364 |
| _version_ | 1797344136718712832 |
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| author | Angelique Mukasine Louis Sibomana Kayalvizhi Jayavel Kizito Nkurikiyeyezu Eric Hitimana |
| author_facet | Angelique Mukasine Louis Sibomana Kayalvizhi Jayavel Kizito Nkurikiyeyezu Eric Hitimana |
| author_sort | Angelique Mukasine |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T10:57:58Z |
| format | Article |
| id | doaj.art-3434f8ad239c41759b8b0528ec905cba |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-08T10:57:58Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-3434f8ad239c41759b8b0528ec905cba2024-01-26T16:17:19ZengMDPI AGEnergies1996-10732024-01-0117236410.3390/en17020364Maximizing Biogas Yield Using an Optimized Stacking Ensemble Machine Learning ApproachAngelique Mukasine0Louis Sibomana1Kayalvizhi Jayavel2Kizito Nkurikiyeyezu3Eric Hitimana4African Center of Excellence in the Internet of Things, University of Rwanda, Kigali P.O. Box 3900, RwandaNational Council for Science and Technology, Kigali P.O. Box 2285, RwandaCreative Computing Institute, University of the Arts London, London WC1V 7EY, UKDepartment of Electrical and Electronics Engineering, University of Rwanda, Kigali P.O. Box 3900, RwandaAfrican Center of Excellence in the Internet of Things, University of Rwanda, Kigali P.O. Box 3900, RwandaBiogas 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.https://www.mdpi.com/1996-1073/17/2/364energy managementbiogas yield predictionoptimized stacking ensemble model |
| spellingShingle | Angelique Mukasine Louis Sibomana Kayalvizhi Jayavel Kizito Nkurikiyeyezu Eric Hitimana Maximizing Biogas Yield Using an Optimized Stacking Ensemble Machine Learning Approach Energies energy management biogas yield prediction optimized stacking ensemble model |
| title | Maximizing Biogas Yield Using an Optimized Stacking Ensemble Machine Learning Approach |
| title_full | Maximizing Biogas Yield Using an Optimized Stacking Ensemble Machine Learning Approach |
| title_fullStr | Maximizing Biogas Yield Using an Optimized Stacking Ensemble Machine Learning Approach |
| title_full_unstemmed | Maximizing Biogas Yield Using an Optimized Stacking Ensemble Machine Learning Approach |
| title_short | Maximizing Biogas Yield Using an Optimized Stacking Ensemble Machine Learning Approach |
| title_sort | maximizing biogas yield using an optimized stacking ensemble machine learning approach |
| topic | energy management biogas yield prediction optimized stacking ensemble model |
| url | https://www.mdpi.com/1996-1073/17/2/364 |
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