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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Published in:Renewable & sustainable energy reviews Vol. 200; p. 114616
Main Authors: Forootan, Mohammad Mahdi, Ahmadi, Abolfazl
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.08.2024
Subjects:
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 Access:Get full text
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Abstract 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 oxide fuel cell as motive cycles, the steam and organic Rankine cycle, a parabolic trough solar collector (PTC), a proton exchange membrane electrolyzer, multi-effect distillation, and reverse osmosis. Using Engineering Equation Solver software, analyses in energy, exergy, exergoeconomic, and exergoenvironment (4E) are conducted to identify critical system evaluation parameters in thermodynamics, economics, and the environment. Additionally, the proposed system is optimized using an artificial neural network and the multi-objective grey wolf optimizer algorithm, utilizing decision variables identified through sensitivity analysis to enhance system performance. The results of six-objective optimization show energy and exergy efficiencies at 49.48 % and 47.21 %, with a net power production of 133 MW and a total cost rate of $7903/h. Among all the components, the combustion chamber and PTC have the maximum exergy destruction, with values of 58.87 MW and 17.29 MW, respectively. Also, hydrogen, FW, and DHW production rates are 201.6 kg/h, 43.88 kg/s, and 40.98 kg/s, respectively. Moreover, the costs of hydrogen, FW, and DHW are $953.64/h, $57.35/h, and $82.48/h, respectively. Finally, Bloemfontein, Las Vegas, and Tunis are the most favorable regions economically, with a low levelized cost of electricity of $0.05931, $0.06006, and $0.05932 per kWh, along with payback periods of 0.6144, 0.5524, and 1.069 years, respectively. [Display omitted] •A novel polygeneration system fueled by methane and solar energy has been introduced.•The integration of solid oxide fuel cell and Brayton cycle as motive cycle for other subsystems for electricity, freshwater, hydrogen, oxygen and domestic hot water production.•Machine learning base multi-objective grey wolf optimization algorithm with more than 99 % accuracy.•Multiple scenarios are considered for optimization process.•The economic feasibility of several cities has been assessed for the establishment of the system.
AbstractList 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 oxide fuel cell as motive cycles, the steam and organic Rankine cycle, a parabolic trough solar collector (PTC), a proton exchange membrane electrolyzer, multi-effect distillation, and reverse osmosis. Using Engineering Equation Solver software, analyses in energy, exergy, exergoeconomic, and exergoenvironment (4E) are conducted to identify critical system evaluation parameters in thermodynamics, economics, and the environment. Additionally, the proposed system is optimized using an artificial neural network and the multi-objective grey wolf optimizer algorithm, utilizing decision variables identified through sensitivity analysis to enhance system performance. The results of six-objective optimization show energy and exergy efficiencies at 49.48 % and 47.21 %, with a net power production of 133 MW and a total cost rate of $7903/h. Among all the components, the combustion chamber and PTC have the maximum exergy destruction, with values of 58.87 MW and 17.29 MW, respectively. Also, hydrogen, FW, and DHW production rates are 201.6 kg/h, 43.88 kg/s, and 40.98 kg/s, respectively. Moreover, the costs of hydrogen, FW, and DHW are $953.64/h, $57.35/h, and $82.48/h, respectively. Finally, Bloemfontein, Las Vegas, and Tunis are the most favorable regions economically, with a low levelized cost of electricity of $0.05931, $0.06006, and $0.05932 per kWh, along with payback periods of 0.6144, 0.5524, and 1.069 years, respectively. [Display omitted] •A novel polygeneration system fueled by methane and solar energy has been introduced.•The integration of solid oxide fuel cell and Brayton cycle as motive cycle for other subsystems for electricity, freshwater, hydrogen, oxygen and domestic hot water production.•Machine learning base multi-objective grey wolf optimization algorithm with more than 99 % accuracy.•Multiple scenarios are considered for optimization process.•The economic feasibility of several cities has been assessed for the establishment of the system.
ArticleNumber 114616
Author Ahmadi, Abolfazl
Forootan, Mohammad Mahdi
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  givenname: Abolfazl
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  surname: Ahmadi
  fullname: Ahmadi, Abolfazl
  email: a_ahmadi@iust.ac.ir
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Keywords Multi-objective grey wolf optimizer
Multi-effect distillation
Solid oxide fuel cell
Parabolic trough solar collector
Hydrogen production
Machine learning
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Snippet 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...
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SubjectTerms Hydrogen production
Machine learning
Multi-effect distillation
Multi-objective grey wolf optimizer
Parabolic trough solar collector
Solid oxide fuel cell
Title 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
URI https://dx.doi.org/10.1016/j.rser.2024.114616
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