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Application of an explainable glass-box machine learning approach for prognostic analysis of a biogas-powered small agriculture engine

Author

Listed:
  • Jamei, Mehdi
  • Sharma, Prabhakar
  • Ali, Mumtaz
  • Bora, Bhaskor J.
  • Malik, Anurag
  • Paramasivam, Prabhu
  • Farooque, Aitazaz A.
  • Abdulla, Shahab

Abstract

Biogas has developed as a potential substitute fuel source due to its renewable and sustainable nature, which can help reduce greenhouse gas emissions. In this paper, along with the experimental investigation, a novel interpretable expert system was constructed to address these issues and provide meaningful explanations for model predictions using extreme gradient boosting (XGB). For this purpose, the SHapley Additive exPlanations (SHAP) tool was coupled with XGB open-source algorithm to simulate five efficiency parameters of the biogas-powered engines, including brake thermal efficiency (BTE), peak pressure (PP), hydrocarbon (HC), oxides of nitrogen (NOx), and carbon monoxide (CO). Here, four experimental-based variables comprised of fuel injection timing, fuel injection pressure, compression ratio, and engine load were employed as predictors. Apart from the main framework, two advanced ensemble machine learning (ML), namely the light gradient-boosting machine (LightGBM) and Extra Tree algorithms, were adopted to validate the primary model. In addition, we use the SHAP framework to understand the impact of input features on engine performance and emission outputs. XGB-SHAP owing to its best predictive performance (BTE|R = 0.994 and RMSE = 0.567, PP|R = 0.984 and RMSE = 0.846, HC|R = 0.994 and RMSE = 6.215, NOx|R = 0.998 and RMSE = 1.407, and CO|R = 0.985 and RMSE = 3.464) outperformed the Extra Tree and LightGBM, respectively.

Suggested Citation

  • Jamei, Mehdi & Sharma, Prabhakar & Ali, Mumtaz & Bora, Bhaskor J. & Malik, Anurag & Paramasivam, Prabhu & Farooque, Aitazaz A. & Abdulla, Shahab, 2024. "Application of an explainable glass-box machine learning approach for prognostic analysis of a biogas-powered small agriculture engine," Energy, Elsevier, vol. 288(C).
    • Handle: RePEc:eee:energy:v:288:y:2024:i:c:s0360544223032565
    DOI: 10.1016/j.energy.2023.129862
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    References listed on IDEAS

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    1. Alessandro Falai & Daniela Anna Misul, 2023. "Data-Driven Model for Real-Time Estimation of NOx in a Heavy-Duty Diesel Engine," Energies, MDPI, vol. 16(5), pages 1-17, February.
    2. Ana Gonçalves & Jaime Filipe Puna & Luís Guerra & José Campos Rodrigues & João Fernando Gomes & Maria Teresa Santos & Diogo Alves, 2019. "Towards the Development of Syngas/Biomethane Electrolytic Production, Using Liquefied Biomass and Heterogeneous Catalyst," Energies, MDPI, vol. 12(19), pages 1-21, October.
    3. Brédy, Jhemson & Gallichand, Jacques & Celicourt, Paul & Gumiere, Silvio José, 2020. "Water table depth forecasting in cranberry fields using two decision-tree-modeling approaches," Agricultural Water Management, Elsevier, vol. 233(C).
    4. Ferreira, Lucas Borges & da Cunha, Fernando França, 2020. "New approach to estimate daily reference evapotranspiration based on hourly temperature and relative humidity using machine learning and deep learning," Agricultural Water Management, Elsevier, vol. 234(C).
    5. Mehdi Jamei & Mumtaz Ali & Anurag Malik & Ramendra Prasad & Shahab Abdulla & Zaher Mundher Yaseen, 2022. "Forecasting Daily Flood Water Level Using Hybrid Advanced Machine Learning Based Time-Varying Filtered Empirical Mode Decomposition Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(12), pages 4637-4676, September.
    6. Manish Pandey & Masoud Karbasi & Mehdi Jamei & Anurag Malik & Jaan H. Pu, 2023. "A Comprehensive Experimental and Computational Investigation on Estimation of Scour Depth at Bridge Abutment: Emerging Ensemble Intelligent Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(9), pages 3745-3767, July.
    7. Gao, Feng & Shao, Xueyan, 2022. "A novel interval decomposition ensemble model for interval carbon price forecasting," Energy, Elsevier, vol. 243(C).
    8. Sloughter, J. McLean & Gneiting, Tilmann & Raftery, Adrian E., 2010. "Probabilistic Wind Speed Forecasting Using Ensembles and Bayesian Model Averaging," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 25-35.
    9. Jamei, Mehdi & Karbasi, Masoud & Malik, Anurag & Jamei, Mozhdeh & Kisi, Ozgur & Yaseen, Zaher Mundher, 2022. "Long-term multi-step ahead forecasting of root zone soil moisture in different climates: Novel ensemble-based complementary data-intelligent paradigms," Agricultural Water Management, Elsevier, vol. 269(C).
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