This study presents a multi-objective optimization approach to enhance the performance of Spray Flash Vacuum Distillation Systems. The paper builds upon a previously published integrated system model and extends it by incorporating a comprehensive parametric analysis. The system comprises four interconnected subsystems: (1) a spray flash evaporation chamber, (2) a recirculation loop for feed-brine mixture, (3) a hybrid dual-condenser for internal heat recovery and external cooling, and (4) a vacuum source that maintains vacuum conditions across the system.
The study employs parametric study to scrutinize the impact of varying input parameters i.e., inlet temperature and mass flow rate of the spray nozzles, and evaporation chamber vacuum pressure on key performance indicators such as yield rate. Special attention is given to major interlinked operation parameters and their cascading effects on transport characteristics, including flash vapor generation and condensing vapor flow.

In addition a multi-objective optimization (MOO) framework, employing a genetic algorithm (GA), is rigorously applied to enhance the operational efficiency of Spray Flash Vacuum Distillation Systems. This approach evaluates the interplay between various operational parameters, such as inlet temperature and vacuum pressure, to simultaneously minimize energy consumption, particularly in the vacuum pump, and maximize yield rates. The utilization of GA in MOO facilitates an efficient exploration of the solution space, leading to an optimized balance between energy efficiency and yield maximization. This methodical approach underscores the potential of MOO and GA in advancing sustainable and efficient distillation system designs.