Exergoeconomic and optimization study of a solar and wind-driven plant employing machine learning approaches; a case study of Las Vegas city. (20th January 2023)

• Record Type: Journal Article
• Authors: Shakibi, Hamid; Assareh, Ehsanolah; Chitsaz, Ata; Keykhah, Sajjad; Behrang, Mohammadali; Golshanzadeh, Masoud; Ghodrat, Maryam; Lee, Moonyong
• Is Part Of: Journal of cleaner production. Volume 385(2023)
• Publication Date: 2023-01-20
• Subjects: Wind-solar driven energy unit -- Multi-aspect study -- Multi-objective optimization -- ANN-based model -- Grey wolf optimizer; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2022.135529
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: High reliance on conventional fuels and existing freshwater resources is unviable in the long run. A multi-generation system primed with a parabolic trough solar collector and a wind turbine is introduced in parallel to the efforts to shift this dependency on alternative fuels. A thermoelectric generator was integrated with the steam Rankine cycle as a condenser for higher electricity generation. The steam Rankine cycle, a wind turbine, a reverse osmosis desalination unit, and a single-effect absorption chiller, and proton exchange membrane electrolyzer were employed for freshwater, cooling load, and hydrogen production. The performance of several thermal oils was analyzed based on the first and second laws of thermodynamics, and Syltherm 800 was chosen as the best option. The artificial neural network-based model was considered to remodel the thermodynamic issues in the second section. The multi-aspect grey wolf optimization approach was carried out to find the optimum conditions of the target functions. According to the results, solar part (901.4 kW) has the highest exergy destruction value. Consequently, in a case study that considered the conditions of Las Vegas city, 22.02 MWh power and 4.50 tons CO2 per year were achieved, which leads to an environmental cost of 107.8 $/year, equivalent to the cost of expansion of 218.2 m 2 of farm or green zone. Graphical abstract: Image 1 Highlights: A combination of the Machine learning model and GWO is implemented to perform multi-aspect optimization procedures. A novel generation unit is introduced based on RO unit, PEM electrolyzer, and SEAC to produce fresh water, oxygen, hydrogen, and cooling load. 54.8% of the total cost is assigned to the wind Turbine cost, while the equipment cost for Turbine, solar PTC, and PEM electrolyzer units are 12.6%, 20.8%, and 9.6% of the total cost respectively. An increase in Δ T P P, E v a causes to boosting the payback period as well as the total cost rate while the exergy efficiency of the solar-wind-based unit enhances. 517.2 kWh, 105.5 tons CO2 per year can be achieved by employing the third scenario of optimization.