IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i2p364-d1317070.html
   My bibliography  Save this article

Maximizing Biogas Yield Using an Optimized Stacking Ensemble Machine Learning Approach

Author

Listed:
  • 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)

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.

Suggested Citation

  • Angelique Mukasine & Louis Sibomana & Kayalvizhi Jayavel & Kizito Nkurikiyeyezu & Eric Hitimana, 2024. "Maximizing Biogas Yield Using an Optimized Stacking Ensemble Machine Learning Approach," Energies, MDPI, vol. 17(2), pages 1-13, January.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:2:p:364-:d:1317070
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/2/364/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/2/364/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Johanna Karina Solano Meza & David Orjuela Yepes & Javier Rodrigo-Ilarri & María-Elena Rodrigo-Clavero, 2023. "Comparative Analysis of the Implementation of Support Vector Machines and Long Short-Term Memory Artificial Neural Networks in Municipal Solid Waste Management Models in Megacities," IJERPH, MDPI, vol. 20(5), pages 1-20, February.
    2. Lyu, Wenjing & Liu, Jin, 2021. "Artificial Intelligence and emerging digital technologies in the energy sector," Applied Energy, Elsevier, vol. 303(C).
    3. Angelique Mukasine & Louis Sibomana & Kayalvizhi Jayavel & Kizito Nkurikiyeyezu & Eric Hitimana, 2023. "Correlation Analysis Model of Environment Parameters Using IoT Framework in a Biogas Energy Generation Context," Future Internet, MDPI, vol. 15(8), pages 1-14, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chwiłkowska-Kubala, Anna & Cyfert, Szymon & Malewska, Kamila & Mierzejewska, Katarzyna & Szumowski, Witold, 2023. "The impact of resources on digital transformation in energy sector companies. The role of readiness for digital transformation," Technology in Society, Elsevier, vol. 74(C).
    2. Zhao, Qian & Wang, Lu & Stan, Sebastian-Emanuel & Mirza, Nawazish, 2024. "Can artificial intelligence help accelerate the transition to renewable energy?," Energy Economics, Elsevier, vol. 134(C).
    3. Fareri, Silvia & Apreda, Riccardo & Mulas, Valentina & Alonso, Ruben, 2023. "The worker profiler: Assessing the digital skill gaps for enhancing energy efficiency in manufacturing," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    4. Nguyen Thanh Viet & Alla G. Kravets, 2022. "The New Method for Analyzing Technology Trends of Smart Energy Asset Performance Management," Energies, MDPI, vol. 15(18), pages 1-26, September.
    5. Zhao, Guanjia & Cui, Zhipeng & Xu, Jing & Liu, Wenhao & Ma, Suxia, 2022. "Hybrid modeling-based digital twin for performance optimization with flexible operation in the direct air-cooling power unit," Energy, Elsevier, vol. 254(PC).
    6. Chițu Florentina & Mecu Andra-Nicoleta & Marin Georgiana-Ionela, 2024. "Exploring the Climate Change-AI Nexus: A Bibliometric and Scientometric Study," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 18(1), pages 1658-1670.
    7. Bhagwan, N. & Evans, M., 2023. "A review of industry 4.0 technologies used in the production of energy in China, Germany, and South Africa," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    8. Zihao Lin, 2024. "Can digital transformation curtail carbon emissions? Evidence from a quasi-natural experiment," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-12, December.
    9. Anna Kwiotkowska, 2024. "Creating Organizational Resilience through Digital Transformation and Dynamic Capabilities: Findings from fs/QCA Analysis on the Example of Polish CHP Plants," Sustainability, MDPI, vol. 16(14), pages 1-18, July.
    10. Henryk Dzwigol & Aleksy Kwilinski & Oleksii Lyulyov & Tetyana Pimonenko, 2024. "Digitalization and Energy in Attaining Sustainable Development: Impact on Energy Consumption, Energy Structure, and Energy Intensity," Energies, MDPI, vol. 17(5), pages 1-17, March.
    11. Chen, Yan & Zhang, Ruiqian & Lyu, Jiayi & Hou, Yuqi, 2024. "AI and Nuclear: A perfect intersection of danger and potential?," Energy Economics, Elsevier, vol. 133(C).
    12. Jing Wang & Yubing Xu, 2022. "How Does Digitalization Affect Haze Pollution? The Mediating Role of Energy Consumption," IJERPH, MDPI, vol. 19(18), pages 1-15, September.
    13. Yang, Siying & Liu, Fengshuo, 2024. "Impact of industrial intelligence on green total factor productivity: The indispensability of the environmental system," Ecological Economics, Elsevier, vol. 216(C).
    14. Huang, Chenchen & Lin, Boqiang, 2023. "Promoting decarbonization in the power sector: How important is digital transformation?," Energy Policy, Elsevier, vol. 182(C).
    15. Lin Wang & Yugang He & Renhong Wu, 2024. "Digitization Meets Energy Transition: Shaping the Future of Environmental Sustainability," Energies, MDPI, vol. 17(4), pages 1-25, February.
    16. Fargalla, Mandella Ali M. & Yan, Wei & Deng, Jingen & Wu, Tao & Kiyingi, Wyclif & Li, Guangcong & Zhang, Wei, 2024. "TimeNet: Time2Vec attention-based CNN-BiGRU neural network for predicting production in shale and sandstone gas reservoirs," Energy, Elsevier, vol. 290(C).
    17. Latifa A. Yousef & Hibba Yousef & Lisandra Rocha-Meneses, 2023. "Artificial Intelligence for Management of Variable Renewable Energy Systems: A Review of Current Status and Future Directions," Energies, MDPI, vol. 16(24), pages 1-27, December.
    18. Chankook Park, 2022. "Expansion of servitization in the energy sector and its implications," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 11(4), July.
    19. Jun Liu & Yu Qian & Yuanjun Yang & Zhidan Yang, 2022. "Can Artificial Intelligence Improve the Energy Efficiency of Manufacturing Companies? Evidence from China," IJERPH, MDPI, vol. 19(4), pages 1-18, February.
    20. Enock Siankwilimba & Chisoni Mumba & Bernard Mudenda Hang’ombe & Joshua Munkombwe & Jacqueline Hiddlestone-Mumford & Munyaradzi A. Dzvimbo & Md Enamul Hoque, 2024. "Bioecosystems towards sustainable agricultural extension delivery: effects of various factors," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(9), pages 21801-21843, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:2:p:364-:d:1317070. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.