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Gradient Boosting Regression Tree Optimized with Slime Mould Algorithm to Predict the Higher Heating Value of Municipal Solid Waste

Author

Listed:
  • Esraa Q. Shehab

    (Department of Civil Engineering, College of Engineering, University of Diyala, Baqubah 32001, Iraq)

  • Farah Faaq Taha

    (Department of Civil Engineering, College of Engineering, University of Diyala, Baqubah 32001, Iraq)

  • Sabih Hashim Muhodir

    (Department of Architectural Engineering, Cihan University Erbil, Erbil 44001, Iraq)

  • Hamza Imran

    (Department of Construction and Project, Al-Karkh University of Science, Baghdad 10081, Iraq)

  • Krzysztof Adam Ostrowski

    (Faculty of Civil Engineering, Cracow University of Technology, Warszawska 24, 31-155 Cracow, Poland)

  • Marcin Piechaczek

    (Faculty of Civil Engineering, Cracow University of Technology, Warszawska 24, 31-155 Cracow, Poland)

Abstract

The production of municipal solid waste (MSW) has led to an unprecedented level of environmental pollution, worsening the global challenges posed by climate change. Researchers and policymakers have recently made significant strides in the field of sustainable and renewable energy sources, which are viable from technological, environmental, and economic perspectives. Consequently, the waste-to-energy programs enhance nations’ socioeconomic status while positively impacting the environment. To predict the higher heating value (HHV) of MSW fuel based on carbon, hydrogen, oxygen, nitrogen, and sulfur content, the current study introduces a Gradient Boosting Regression Tree (GBRT) model optimized with the Slime Mold Algorithm (SMA). This model was evaluated using an additional 50 data points after being trained with 202 MSW biomass data points. The performance of the model was assessed using three metrics: root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R 2 ). The results indicated that our model outperformed previously developed models in terms of accuracy and reliability. Additionally, a graphical user interface (GUI) was developed to facilitate the practical application of the model, allowing users to easily input data and receive predictions on the enthalpy of the combustion of MSW fuel.

Suggested Citation

  • Esraa Q. Shehab & Farah Faaq Taha & Sabih Hashim Muhodir & Hamza Imran & Krzysztof Adam Ostrowski & Marcin Piechaczek, 2024. "Gradient Boosting Regression Tree Optimized with Slime Mould Algorithm to Predict the Higher Heating Value of Municipal Solid Waste," Energies, MDPI, vol. 17(17), pages 1-19, August.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:17:p:4213-:d:1462515
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    References listed on IDEAS

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    2. Nishu, & Tang, Songbiao & Mei, Wenjie & Yang, Juntao & Wang, Zhongming & Yang, Gaixiu, 2024. "Effect of anaerobic digestion pretreatment on pyrolysis of distillers’ grain: Product distribution, kinetics and thermodynamics analysis," Renewable Energy, Elsevier, vol. 221(C).
    3. Xing, Jiangkuan & Luo, Kun & Wang, Haiou & Gao, Zhengwei & Fan, Jianren, 2019. "A comprehensive study on estimating higher heating value of biomass from proximate and ultimate analysis with machine learning approaches," Energy, Elsevier, vol. 188(C).
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