Artificial neural network multi-objective optimization of a novel integrated plant to produce power, cooling and potable water

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 system can produce 1.104 kg/s fresh water and its net power production capacity is 2251 kW. Also, the exergy destruction in the proposed system is 964.4 kW higher 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.