Multi-objective optimization of a small-scale solar-hot-spring-geothermal brackish water direct contact membrane distillation system

Abstract

A small-scale solar-hot-spring-geothermal brackish water membrane distillation (MD) system is proposed by combining hot-spring and solar components to reduce fossil energy consumptions. A performance parameter framework and carbon footprint model is developed to perform an analysis on whole life cycle of the proposed system. The results show that the solar subsystem improved heat recovery with the water generation ratio increased by 22–42 %. As the feed-side inlet temperature increases, the total Specific Energy Consumption (SEC) contribution rate of renewable geothermal and solar energies increases from 12.34 % to 20.45 %. The cold and hot sources are responsible for the largest exergic losses at 1.79 kJ/S and 0.48 kJ/S. The unit exergy cost of the solar subsystem is the highest of 0.037 $/h in terms of economic benefits. One of effective ways to improve the carbon emission reduction rate is to develop a cogeneration process for the water purification. A multi-software co-simulation methodology is proposed to explore innovative MD systems of optimal configurations, improve desalination efficiency and reduce the energy consumptions. The Non-dominated Sorting Genetic Algorithm (NSGAII) and improved NSGAII genetic algorithms (NSGAII-A and NSGAII-B) are utilized for multi-objective optimization of the MD system. Finally, the Technique for Order Preference by Similarity to Ideal Situation (TOPSIS) method based on entropy weight is employed to obtain the Pareto frontier solution set. It is indicated that NSGAII-B based on the four objective-optimal scheme is the best for the Multi-objective optimization of the MD system.