June 22, 2024
Abbas Rezaei

Abbas Rezaei

Academic rank: Assistant professor
Address:
Education: Ph.D in Electrical engineering
Phone: 083-38305001
Faculty: Faculty ofٍٍ Electrical Engineering

Research

Title
Artificial neural network multi-objective optimization of a novel integrated plant to produce power, cooling and potable water
Type Article
Keywords
Geothermal energy Multi-generation Neural network Optimization
Researchers Tao Hai، Salah I. Yahya، Jincheng Zhou، Ibrahim B. Mansir، Abbas Rezaei، Kabir Al Mamun

Abstract

This article investigates a geothermal energy-based cycle to produce power, cooling, and fresh water, using humidification-dehumidification technology. Energy, exergy, and exergo-economic analysis have been performed for a geothermal cycle and a proposed cycle that is an improvement of the basic cycle. Comparative analysis for the new cycle has been extracted and exergy-economic parameters have also been calculated.Moreover, by using artificial neural network and multi-objective optimization, optimal parameters of the system have been extracted. The primary novel part of this study is that different subsystems combined in a way generate different products and the optimum performance parameters are introduced based on the multi-objective optimization. The obtained results show that the highest exergy destruction is related to heat exchanger 1 (HX1) with a value of 670.5 kW. The proposed systemcan produce 1.104 kg/s freshwater and its net power production capacity is 2251kW. Also, the exergy destruction in the proposed systemis 964.4kWhigher than the basic cycle. Based on the multi-objective optimization, the optimal point is selected based on the ideal result of 32.35 % efficiency and 2322.32 kW exergy destruction, and the parameter unit cost of product is 8.81 $/kW