Modeling and genetic algorithm-based multi-objective optimization of the MED-TVC desalination system
Abstract
This study proposes a systematic approach of analysis and optimization of the multi-effect distillation-thermal vapor compression (MED-TVC) desalination system. The effect of input variables, such as temperature difference, motive steam mass flow rate, and preheated feed water temperature was investigated using response surface methodology (RSM) and partial least squares (PLS) technique. Mathematical and economical models with exergy analysis were used for total annual cost (TAC), gain output ratio (GOR) and fresh water flow rate (Q). Multi-objective optimization (MOO) to minimize TAC and maximize GOR and Q was performed using a genetic algorithm (GA) based on an artificial neural network (ANN) model. Best Pareto optimal solution selected from the Pareto sets showed that the MED-TVC system with 6 effects is the best system among the systems with 3, 4, 5 and 6 effects, which has a minimum value of unit product cost (UPC) and maximum values of GOR and Q. The system with 6 effects under the optimum operation conditions can save 14%, 12.5%, 2% in cost and reduces the amount of steam used for the production of 1 m3 of fresh water by 50%, 34% and 18% as compared to systems with 3, 4 and 5 effects, respectively.