A machine learning study on a municipal solid waste-to-energy system for environmental sustainability in a multi-generation energy system for hydrogen production

Zhang, Yulan and Aldosky, Abdulrahman Jaffar and Goyal, Vishal and Meqdad, Maytham N. and Nutakki, Tirumala Uday Kumar and Alsenani, Theyab R. and Nguyen, Van Nhanh and Dahari, Mahidzal and Nguyen, Phuoc Quy Phong and Ali, H. Elhosiny (2024) A machine learning study on a municipal solid waste-to-energy system for environmental sustainability in a multi-generation energy system for hydrogen production. Process Safety and Environmental Protection, 182. 1171 – 1184. ISSN 0957-5820, DOI https://doi.org/10.1016/j.psep.2023.12.054.

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Municipal solid waste (MSW)-to-energy systems have gained significant attention in recent years for their potential to produce renewable energy from waste. These systems involve the conversion of MSW into electricity, heat or fuel. One of the most promising applications of MSW-to-energy systems is the production of hydrogen, which is considered a clean and sustainable fuel. Machine learning algorithms have the potential to revolutionize the way MSW-to-energy systems are managed. The integration of machine learning into MSW-to-energy systems has the potential to significantly improve the sustainability and profitability of this industry. In this study, a novel integrated MSW-to-energy system is modeled to produce hydrogen, power, and oxygen and with capacities of heating water and air. Hydrogen production, power production, oxygen storage, hot water, hot air, and system emission are predicted using machine learning algorithms based on regression models with high validity and R2 values more than 99.8 having errors smaller than 1. The reduced regression models are developed by eliminating the insignificant variables from the full algorithms using the analysis of variance. The findings reveal high accuracy for the reduced regression models while their errors slightly decrease to 2. This suggests that the machine learning algorithms can also be used as an effective tool to further improve MSW-to-energy systems. © 2024 The Institution of Chemical Engineers

Item Type: Article
Funders: Weifang University of Science and Technology [Grant no. 2021RWTS02, KJRC2021005], Deanship of Scientific Research, King Khalid University [Grant no. R.G.P.2/485/44]
Uncontrolled Keywords: Hydrogen fuels; Hydrogen production; Learning algorithms; Municipal solid waste; Oxygen; Regression analysis; Sustainable development; Waste treatment; Emission; Energy systems; Environmental sustainability; Learning studies; Machine learning algorithms; Machine-learning; Multi generations; Regression modelling; Renewable energies; Waste-to-energy systems; Machine learning
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering > Department of Electrical Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 14 Jun 2024 02:59
Last Modified: 14 Jun 2024 02:59
URI: http://eprints.um.edu.my/id/eprint/44880

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