Generalized Disjunctive Programming Model for Optimization of Reverse Electrodialysis Process

Abstract

Reverse electrodialysis (RED), an emerging electrochemical technology that uses ion-selective membranes to directly draw electricity out from salinity differences between two solutions, i.e., salinity gradient energy (SGE), has the potential to be a clean and steady renewable source to reach a sustainable water and energy supply portfolio. Although RED has made notable advances, full-scale RED progress demands more techno-economic and environmental assessments that consider full process design and operational decision space from module- to system-level. This work presents an optimization model formulated as a Generalized Disjunctive Programming (GDP) problem to define the cost-optimal RED process design for different deployment scenarios. We use a predictive model of the RED stack developed and validated in our research group to fully capture the behavior of the system. The problem addressed is to determine the RED plant's topology and the working conditions for a given design of each RED stack which renders the cost-optimal design for the defined problem and scenario. Our results show that, compared with simulation-based approaches, mathematical programming techniques are an efficient and systematic approach to provide decision-making support in early-stage applied research and to obtain design and operation guidelines for full-scale RED implementation in real scenarios.