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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.

Otras publicaciones de la misma revista o congreso con autores/as de la Universidad de Cantabria

 Autoría: Tristán C., Fallanza M., Ibáñez R., Ortiz I., Grossmann I.E.,

 Fuente: Computers and Chemical Engineering, 2023, 174, 108196

Editorial: Elsevier

 Fecha de publicación: 01/06/2023

Nº de páginas: 18

Tipo de publicación: Artículo de Revista

 DOI: 10.1016/j.compchemeng.2023.108196

ISSN: 0098-1354,1873-4375

 Proyecto español: PDC2021-120786-I00

Url de la publicación: https://doi.org/10.1016/j.compchemeng.2023.108196

Autoría

CAROLINA TRISTAN TEJA

IGNACIO GROSSMANN EPPER