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: | , , , , , , , , , |
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| Format: | Article |
| Published: |
Institution of Chemical Engineers
2024
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| Subjects: | |
| Online Access: | http://eprints.um.edu.my/44880/ |
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| Summary: | 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 |
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