Sustainable ReverseOsmosis Desalination Systems:Comprehensive Superstructure Modeling and Multi-Objective Optimization

This study explores the development of sustainable reverse osmosis (RO) desalination systems through comprehensive superstructure modeling and multiobjective optimization. We present a robust superstructure model that covers various configurations and operational strategies for RO systems. Detailed mathematical and empirical models are developed for the feed water intake section, RO units, pumps, and pressure exchanger. By employing multiobjective optimization techniques, toward a sustainable design, we aim to demonstrate how key performance indicators, such as water recovery, specific water cost (SWC), specific energy consumption (SEC), specific CO₂ emissions (SCE), and specific intensity of brine pollution (SIBP), interact with each other and highlight the effects of key design parameters on these trade-offs. The optimization problem, characterized as mixed-integer nonlinear programming (MINLP), is solved using the NSGA-II method. Principal results indicate a close relationship between water recovery, SWC, and SIBP, with SEC showing an inverse trend. Validation against industry-standard software demonstrated the reliability of the proposed models, with an average relative error of 2.7% for permeate production and 8.0% for its concentration. The resulting Pareto fronts are comprehensively discussed, revealing the impacts of expert decisions and the high-level trade-offs between the various design criteria. Case studies and Pareto analyses showed that higher recovery and lower SWC result from higher pressure and fewer pressure vessels, which is the inverse for SEC. Additionally, as stages are added, higher recovery results; however, for similar recovery across various configurations, increasing the number of stages results in higher costs and energy consumption.