A generalized disjunctive programming model for the optimal design of reverse electrodialysis process for salinity gradient-based power generation - Tristán, C.; Fallanza, M.; Ibáñez, R.; Ortiz, I.; Grossmann, I.E. (Computers & chemical engineering, 2023)
•Optimal net present value reverse electrodialysis (RED) process design and operation.•Recycle and reuse alternatives improves the cost-competitiveness of RED.•The optimal solution outperforms the conventional series staging of RED units.•Limited low-salinity feed restricts the electricity profits making RED unprofitable.•Efficient and systematic decision-making tool over simulation-based approaches. 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.