An innovative biomass-driven multi-generation system equipped with PEM fuel cells/VCl cycle: Throughout assessment and optimal design via particle swarm algorithm

This work proposes a new, efficient, economically, and environmentally viable approach for developing cutting-edge energy systems and assisting the anticipated global green transition with maximal renewable integration. The cogeneration of hydrogen and power is driven by biomass, which in turn drive...

Full description

Bibliographic Details
Published in:International Journal of Hydrogen Energy
Main Author: Hai T.; El-Shafay A.S.; Al-Obaidi R.; Singh Chauhan B.; Fahad Almojil S.; Almohana A.I.; Alali A.F.
Format: Article
Language:English
Published: Elsevier Ltd 2024
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153092004&doi=10.1016%2fj.ijhydene.2023.03.356&partnerID=40&md5=ab64e749218732bdd39d454c457ec6d9
id 2-s2.0-85153092004
spelling 2-s2.0-85153092004
Hai T.; El-Shafay A.S.; Al-Obaidi R.; Singh Chauhan B.; Fahad Almojil S.; Almohana A.I.; Alali A.F.
An innovative biomass-driven multi-generation system equipped with PEM fuel cells/VCl cycle: Throughout assessment and optimal design via particle swarm algorithm
2024
International Journal of Hydrogen Energy
51

10.1016/j.ijhydene.2023.03.356
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153092004&doi=10.1016%2fj.ijhydene.2023.03.356&partnerID=40&md5=ab64e749218732bdd39d454c457ec6d9
This work proposes a new, efficient, economically, and environmentally viable approach for developing cutting-edge energy systems and assisting the anticipated global green transition with maximal renewable integration. The cogeneration of hydrogen and power is driven by biomass, which in turn drives the vanadium chloride cycle and the proton exchange membrane fuel cells. A cooling absorption unit is powered by waste heat recovered using a passive energy improvement technique to improve performance and cut costs. Energy, exergy, exergo-economic, exergo-environmental impacts, and CO2 emission rate of the suggested renewable-based model are analyzed using an engineering equation solver tool. Parametric analysis is also used to assess the impact of key operational factors on main performance indicators. With machine learning, a particle swarm method is implemented in MATLAB to find the optimal operating state with high precision and low computing cost. The results show the importance of multi-objective optimization by pointing out a conflicting change in the performance metrics from different angles by picking up the biomass moisture content and fuel cell current density. According to the optimization results, an acceptable total cost, environmental damage effectiveness, and exergy efficiency of 5 $/h, 0.86, and 55% are achieved through the integration of particle swarm optimizer and artificial neural network method. The results further reveal that the gasification temperature is not sensitive; however, changing the fuel cell utilization factor significantly impacts the system's performance from all sides. Finally, the chord diagram of the irreversibility rate indicates that the fuel cell and gasifier have the highest destruction of 6.4 kW and 2.6 kW under the optimum condition, owing to mixing and chemical reactions. As for the environmental aspect, by optimizing the system, the system's CO2 emission are greatly reduced. © 2023 Hydrogen Energy Publications LLC
Elsevier Ltd
3603199
English
Article

author Hai T.; El-Shafay A.S.; Al-Obaidi R.; Singh Chauhan B.; Fahad Almojil S.; Almohana A.I.; Alali A.F.
spellingShingle Hai T.; El-Shafay A.S.; Al-Obaidi R.; Singh Chauhan B.; Fahad Almojil S.; Almohana A.I.; Alali A.F.
An innovative biomass-driven multi-generation system equipped with PEM fuel cells/VCl cycle: Throughout assessment and optimal design via particle swarm algorithm
author_facet Hai T.; El-Shafay A.S.; Al-Obaidi R.; Singh Chauhan B.; Fahad Almojil S.; Almohana A.I.; Alali A.F.
author_sort Hai T.; El-Shafay A.S.; Al-Obaidi R.; Singh Chauhan B.; Fahad Almojil S.; Almohana A.I.; Alali A.F.
title An innovative biomass-driven multi-generation system equipped with PEM fuel cells/VCl cycle: Throughout assessment and optimal design via particle swarm algorithm
title_short An innovative biomass-driven multi-generation system equipped with PEM fuel cells/VCl cycle: Throughout assessment and optimal design via particle swarm algorithm
title_full An innovative biomass-driven multi-generation system equipped with PEM fuel cells/VCl cycle: Throughout assessment and optimal design via particle swarm algorithm
title_fullStr An innovative biomass-driven multi-generation system equipped with PEM fuel cells/VCl cycle: Throughout assessment and optimal design via particle swarm algorithm
title_full_unstemmed An innovative biomass-driven multi-generation system equipped with PEM fuel cells/VCl cycle: Throughout assessment and optimal design via particle swarm algorithm
title_sort An innovative biomass-driven multi-generation system equipped with PEM fuel cells/VCl cycle: Throughout assessment and optimal design via particle swarm algorithm
publishDate 2024
container_title International Journal of Hydrogen Energy
container_volume 51
container_issue
doi_str_mv 10.1016/j.ijhydene.2023.03.356
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153092004&doi=10.1016%2fj.ijhydene.2023.03.356&partnerID=40&md5=ab64e749218732bdd39d454c457ec6d9
description This work proposes a new, efficient, economically, and environmentally viable approach for developing cutting-edge energy systems and assisting the anticipated global green transition with maximal renewable integration. The cogeneration of hydrogen and power is driven by biomass, which in turn drives the vanadium chloride cycle and the proton exchange membrane fuel cells. A cooling absorption unit is powered by waste heat recovered using a passive energy improvement technique to improve performance and cut costs. Energy, exergy, exergo-economic, exergo-environmental impacts, and CO2 emission rate of the suggested renewable-based model are analyzed using an engineering equation solver tool. Parametric analysis is also used to assess the impact of key operational factors on main performance indicators. With machine learning, a particle swarm method is implemented in MATLAB to find the optimal operating state with high precision and low computing cost. The results show the importance of multi-objective optimization by pointing out a conflicting change in the performance metrics from different angles by picking up the biomass moisture content and fuel cell current density. According to the optimization results, an acceptable total cost, environmental damage effectiveness, and exergy efficiency of 5 $/h, 0.86, and 55% are achieved through the integration of particle swarm optimizer and artificial neural network method. The results further reveal that the gasification temperature is not sensitive; however, changing the fuel cell utilization factor significantly impacts the system's performance from all sides. Finally, the chord diagram of the irreversibility rate indicates that the fuel cell and gasifier have the highest destruction of 6.4 kW and 2.6 kW under the optimum condition, owing to mixing and chemical reactions. As for the environmental aspect, by optimizing the system, the system's CO2 emission are greatly reduced. © 2023 Hydrogen Energy Publications LLC
publisher Elsevier Ltd
issn 3603199
language English
format Article
accesstype
record_format scopus
collection Scopus
_version_ 1809678012296200192