Machine learning-based optimization and 4E analysis of renewable-based polygeneration system by integration of GT-SRC-ORC-SOFC-PEME-MED-RO using multi-objective grey wolf optimization algorithm and neural networks

This study introduces a new solar-methane-driven setup that recovers waste heat from flue gases to produce electricity, hydrogen, oxygen, fresh water (FW), and domestic hot water (DHW) while reducing the environmental impact of gas turbines. This integration includes the Brayton cycle and solid oxid...

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Veröffentlicht in:	Renewable & sustainable energy reviews Jg. 200; S. 114616
Hauptverfasser:	Forootan, Mohammad Mahdi, Ahmadi, Abolfazl
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.08.2024
Schlagworte:	Hydrogen production Machine learning Multi-effect distillation Multi-objective grey wolf optimizer Parabolic trough solar collector Solid oxide fuel cell Multi-objective grey wolf optimizer Multi-effect distillation Solid oxide fuel cell Parabolic trough solar collector Hydrogen production Machine learning
ISSN:	1364-0321
Online-Zugang:	Volltext
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