TY - JOUR
T1 - Modeling and multi-objective optimization of a solar-boosted gas turbine cycle for green hydrogen production and potable water production
AU - Hai, Tao
AU - Alenizi, Farhan A.
AU - Rajab, Husam
AU - Kumar Singh, Pradeep
AU - Sayed Mohammed Metwally, Ahmed
AU - Sh. Majdi, Hasan
N1 - Publisher Copyright:
© 2023 Elsevier Ltd
PY - 2023/12/15
Y1 - 2023/12/15
N2 - Global warming has caused lots of problems in recent years. Renewable energy can be a solution to this problem. In this research, a multigeneration system is modeled including a concentrated solar plan, Brayton cycle, Rankine cycle, organic Rankine cycle, absorption chiller, single-stage flash desalination, humidification, and dehumidification desalination, and PEM electrolyzer to produce power, cooling load, desalinated water, and hydrogen. The system is modeled in EES software. The results show that the system's overall energy and exergy efficiency is 46.6 % and 43.11 %, respectively. The total power production is 7533 kW after modeling the system in EES, by using a neural network, the data was transferred to MATLAB software. In MATLAB, multi-objective optimization was performed using the genetic algorithm to maximize exergy efficiency and minimize the cost rate. The optimized results show that the overall energy and exergy efficiencies are 46.1 % and 42.1 %, respectively. The total power production is 7628 kW.
AB - Global warming has caused lots of problems in recent years. Renewable energy can be a solution to this problem. In this research, a multigeneration system is modeled including a concentrated solar plan, Brayton cycle, Rankine cycle, organic Rankine cycle, absorption chiller, single-stage flash desalination, humidification, and dehumidification desalination, and PEM electrolyzer to produce power, cooling load, desalinated water, and hydrogen. The system is modeled in EES software. The results show that the system's overall energy and exergy efficiency is 46.6 % and 43.11 %, respectively. The total power production is 7533 kW after modeling the system in EES, by using a neural network, the data was transferred to MATLAB software. In MATLAB, multi-objective optimization was performed using the genetic algorithm to maximize exergy efficiency and minimize the cost rate. The optimized results show that the overall energy and exergy efficiencies are 46.1 % and 42.1 %, respectively. The total power production is 7628 kW.
KW - Desalination
KW - Electrolysis
KW - Exergy analysis
KW - Green hydrogen
KW - Optimization
KW - Solar energy
UR - http://www.scopus.com/inward/record.url?scp=85166668263&partnerID=8YFLogxK
U2 - 10.1016/j.fuel.2023.129245
DO - 10.1016/j.fuel.2023.129245
M3 - Article
AN - SCOPUS:85166668263
SN - 0016-2361
VL - 354
JO - Fuel
JF - Fuel
M1 - 129245
ER -
