A generalized disjunctive programming model for the optimal design of reverse electrodialysis process for salinity gradient-based power generation

Abstract

Reverse electrodialysis (RED) is an emerging electro-membrane technology that generates electricity out of salinity differences between two solutions, a renewable source known as salinity gradient energy. Realizing full-scale RED would require more techno-economic and environmental assessments that consider full process design and operational decision space from the RED stack to the entire system. This work presents an optimization model formulated as a Generalized Disjunctive Programming (GDP) problem that incorporates a finite difference RED stack model from our research group to define the cost-optimal process design. The solution to the GDP problem provides the plant topology and the RED units´ working conditions that maximize the net present value of the RED process for given RED stack parameters and site-specific conditions. Our results show that, compared with simulation-based approaches, mathematical programming techniques are efficient and systematic to assist early-stage research and to extract optimal design and operation guidelines for large-scale RED implementation.