Brackish water

Membrane distillation

Spring (device)

Scale (ratio)

Engineering

Ecology

Biology

Cartography

Petroleum engineering

Geothermal gradient

Biochemistry

Multiple-effect distillation

Salinity

Mathematical optimization

Geology

Paleontology

Geothermal desalination

Mechanical engineering

Environmental engineering

Greenhouse gas

Membrane

Renewable energy

Desalination

Mathematics

Genetic algorithm

Geography

Process engineering

Environmental science

Hot spring

Chemistry

Geophysics

Carbon footprint

Exergy

Electrical engineering

Energy Conversion and Management

• A solar-hot-spring-geothermal brackish water MD system is proposed. • A whole life cycle parameter framework and carbon footprint model is established. • A methodology for multi-software co-simulation is developed. • An improved NSGAII genetic algorithm is formulated for multi-objective optimization. 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.

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