To address the resource wastage associated with the discharge of substantial quantities of low-temperature waste heat in industrial processes, this study proposes a technical solution based on low-temperature multi-effect distillation for wastewater purification and desalinated water production. The approach integrates Aspen Plus simulation with experimental validation. Furthermore, a multi-objective genetic algorithm (MOGA) combined with a genetic programming-based response surface model is employed to conduct multi-parameter optimization. Sensitivity analysis indicates that the temperature difference in the second effect exerts the most significant influence on the gained output ratio, whereas the temperature differences in the first and third effects, along with the feed temperature, predominantly affect the specific heat transfer area. Considering MOGA optimization outcomes, thermal losses, and capital costs, a four-effect distillation was selected, with inter-stage temperature differences of 12 ∘ C , 14 ∘ C , and 14 ∘ C , respectively, and the final-effect vapor used to preheat the feed to 30 ∘ C . Compared to the baseline design, the optimized system achieves a 5 % improvement in the gained output ratio and a 52.7 % reduction in specific heat transfer area. This process demonstrates considerable environmental benefits and industrial applicability by enabling the full recovery and utilization of low-temperature waste heat, as well as the on-site purification and reuse of wastewater. • A multi-effect distillation process is proposed to produce desalinated water by utilizing industrial low-temperature waste heat. • Process operating parameters are optimized using a multi-objective genetic algorithm combined with a genetic aggregation response surface model. • This technology achieves both full recovery of low-temperature waste heat and purification and in-plant reuse of discharged wastewater, offering significant environmental benefits and industrial application value.
Wang et al. (Sun,) studied this question.