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

Esraa Q. Shehab, Farah Faaq Taha, Sabih Hashim Muhodir, Hamza Imran (), Krzysztof Adam Ostrowski () and Marcin Piechaczek
Additional contact information
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

Energies, 2024, vol. 17, issue 17, 1-19

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.

Keywords: Gradient Boosting Regression Tree; machine learning; municipal solid waste; higher heating value (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
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/17/4213/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/17/4213/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2024:i:17:p:4213-:d:1462515

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().