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

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 t...

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Main Authors: Zhang, Yulan, Aldosky, Abdulrahman Jaffar, Goyal, Vishal, Meqdad, Maytham N., Nutakki, Tirumala Uday Kumar, Alsenani, Theyab R., Nguyen, Van Nhanh, Dahari, Mahidzal, Nguyen, Phuoc Quy Phong, Ali, H. Elhosiny
Format: Article
Published: Institution of Chemical Engineers 2024
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.um.edu.my/44880/
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spelling my.um.eprints.448802024-06-14T02:59:20Z http://eprints.um.edu.my/44880/ 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 Aldosky, Abdulrahman Jaffar Goyal, Vishal Meqdad, Maytham N. Nutakki, Tirumala Uday Kumar Alsenani, Theyab R. Nguyen, Van Nhanh Dahari, Mahidzal Nguyen, Phuoc Quy Phong Ali, H. Elhosiny TK Electrical engineering. Electronics Nuclear engineering 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 Institution of Chemical Engineers 2024 Article PeerReviewed 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 <https://doi.org/10.1016/j.psep.2023.12.054>. 10.1016/j.psep.2023.12.054
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Zhang, Yulan
Aldosky, Abdulrahman Jaffar
Goyal, Vishal
Meqdad, Maytham N.
Nutakki, Tirumala Uday Kumar
Alsenani, Theyab R.
Nguyen, Van Nhanh
Dahari, Mahidzal
Nguyen, Phuoc Quy Phong
Ali, H. Elhosiny
A machine learning study on a municipal solid waste-to-energy system for environmental sustainability in a multi-generation energy system for hydrogen production
description 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
format Article
author Zhang, Yulan
Aldosky, Abdulrahman Jaffar
Goyal, Vishal
Meqdad, Maytham N.
Nutakki, Tirumala Uday Kumar
Alsenani, Theyab R.
Nguyen, Van Nhanh
Dahari, Mahidzal
Nguyen, Phuoc Quy Phong
Ali, H. Elhosiny
author_facet Zhang, Yulan
Aldosky, Abdulrahman Jaffar
Goyal, Vishal
Meqdad, Maytham N.
Nutakki, Tirumala Uday Kumar
Alsenani, Theyab R.
Nguyen, Van Nhanh
Dahari, Mahidzal
Nguyen, Phuoc Quy Phong
Ali, H. Elhosiny
author_sort Zhang, Yulan
title A machine learning study on a municipal solid waste-to-energy system for environmental sustainability in a multi-generation energy system for hydrogen production
title_short A machine learning study on a municipal solid waste-to-energy system for environmental sustainability in a multi-generation energy system for hydrogen production
title_full A machine learning study on a municipal solid waste-to-energy system for environmental sustainability in a multi-generation energy system for hydrogen production
title_fullStr A machine learning study on a municipal solid waste-to-energy system for environmental sustainability in a multi-generation energy system for hydrogen production
title_full_unstemmed A machine learning study on a municipal solid waste-to-energy system for environmental sustainability in a multi-generation energy system for hydrogen production
title_sort machine learning study on a municipal solid waste-to-energy system for environmental sustainability in a multi-generation energy system for hydrogen production
publisher Institution of Chemical Engineers
publishDate 2024
url http://eprints.um.edu.my/44880/
_version_ 1805881180177825792
score 13.211869

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