MINLP Model for Reverse Osmosis Network Design under Time-Variant Operation Constraints

Abstract

Reverse osmosis (RO) is a very well-established technology for water desalination. Commercial RO systems are composed of several numbers of RO passes with auxiliary equipment such as high-pressure pumps and pressure-exchanger units to facilitate the separation, and to deliver clean water. Available design models for the RO network represent mixed integer nonlinear programming (MINLP) formulations. In addition, the existing RO design MINLP models are static in nature. These models are not suitable for RO network design under time-variant constraints. In this study, a multiperiod MINLP model is detailed for the RO network design. High-level design decision variables are related to the RO network configuration and equipment sizing, which are independent of time. These decisions are modeled by discrete and continuous variables. Low-level time-variant decision variables are related to the RO network operation and modeled by continuous variables. The operation decision variables are constrained by the design variables through different time periods to satisfy water-demand constraints. In addition, several correlations are considered in this study to estimate water and RO transport properties during the separation process. Case studies are analyzed to show the application of the mathematical programming model.