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...

Celý popis

Podrobná bibliografie

Vydáno v:	Renewable & sustainable energy reviews Ročník 200; s. 114616
Hlavní autoři:	Forootan, Mohammad Mahdi, Ahmadi, Abolfazl
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 01.08.2024
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!