@article{10902/28716,
year = {2023},
month = {6},
url = {https://hdl.handle.net/10902/28716},
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.},
organization = {This work was supported by the LIFE Programme of the European Union (LIFE19 ENV/ES/000143); the MCIN/AEI/10.13039/501100011033 and “European Union NextGenerationEU/PRTR” (PDC2021–120786-I00); and by the MCIN/AEI/10.13039/501100011033 and “ESF Investing in your future” (PRE2018–086454).},
publisher = {Elsevier},
publisher = {Computers and Chemical Engineering, 2023, 174, 108196},
title = {A generalized disjunctive programming model for the optimal design of reverse electrodialysis process for salinity gradient-based power generation},
author = {Tristán Teja, Carolina and Fallanza Torices, Marcos and Ibáñez Mendizábal, Raquel and Ortiz Uribe, Inmaculada and Grossmann Epper, Ignacio},
}