天然盐湖中盐度梯度太阳能池的热优化以实现可再生热能储存与卤虫养殖的协同增效

Abstract Natural salt lakes such as the Great Salt Lake, Lake Eyre, and Lake Urmia offer strong potential for salinity gradient solar ponds (SGSPs) as renewable energy storage systems. Notably, Lake Urmia hosts Artemia, which thrive in hypersaline conditions, feed on algae, and improve water clarity, thereby enhancing thermal efficiency and offering ecological and economic benefits. This study evaluates the feasibility and thermal optimization of SGSPs in such naturally suitable environments. A numerical model based on the Crank–Nicolson finite difference method was developed to simulate SGSP thermal behavior using real month to month meteorological data from a natural Salt Lake. The model investigates the effects of varying layer thicknesses (upper convective zone UCZ, non convective zone NCZ, and lower convective zone LCZ) as well as environmental variables such as solar radiation, wind speed, and relative humidity. The study also explores environmental co benefits including salt storm mitigation, reduced lake salinity, and sustainable Artemia aquaculture. Thermally, with baseline layer thickness (UCZ 0.2 m, NCZ 0.8 m, LCZ 0.6 m), the LCZ reached a peak temperature of 115 °C in August. Increasing the UCZ thickness to 0.6 m reduced LCZ temperatures by about 4 °C per 0.2 m increment, while an NCZ thickness of 0.8 m maximized heat retention. Humidity significantly enhanced thermal performance (5.89 °C gain from 10 % to 81 % RH), whereas wind speed had a smaller cooling effect (about 2 °C reduction).