Break

Mathematical modeling and optimization; Process synthesis and optimization; Life Cycle Assessment (LCA) and Techno-Economic Analysis (TEA); Circular Economy; Desalination and treated wastewater reuse.

Research Experience

Optimizing Reverse Electrodialysis for Salinity-Driven Energy Recovery: A Sustainable Fix for the Water Energy Nexus, Advisors: Prof. Raquel Ibáñez, Dr. Marcos Fallanza. Department of Chemical and Biomolecular Engineering, University of Cantabria.

Treated wastewater reuse and desalination are now more trusted as alternative water sources for dealing with water scarcity. Desalination and wastewater reclamation are major energy consumers, disposing of different salinity effluents. These waste streams possess great potential as renewable energy sources, contributing to the sustainability and circularity of these processes. Capturing salinity gradient energy (SGE) via reverse electrodialysis (RED) offers a promising solution to produce baseload and clean electricity. However, to reach significant strides in RED, it is central to conduct robust techno-economic and environmental assessments that cover all aspects of process design and operational decisions, which can be technically demanding due to the intricate nature of the decision space. During my doctoral studies, I worked on providing tools to optimize RED process designs by evaluating their economic and environmental effects on producing renewable electricity from waste streams in the water sector. A validated RED device model [1], environmental characterization using LCA [2], and Generalized Disjunctive Programming (GDP) optimization model [3] are combined to create optimal large-scale RED process designs for desalination and wastewater treatment plants. Three European R&D projects in the water sector have successfully utilized the modeling framework to promote sustainability. Water sector giants like ACCIONA and SACYR have taken notice of the outcomes.

Generalized Disjunctive Programming for Nonlinear Discrete Optimization in Process Systems Engineering, Advisor: Dr. David E. Bernal Neira. Davidson School of Chemical Engineering, Purdue University.

Mathematical programming plays a crucial role in addressing process design, planning, scheduling, and operational challenges in industry. Many applications in Process Systems Engineering (PSE) involve optimization problems that can be formulated as Mixed-Integer Nonlinear Programming (MINLPs) and equivalently as Generalized Disjunctive Programs (GDPs). The GDP modeling framework offers a more intuitive way to represent optimization problems. Besides, tailored solution algorithms can leverage the resulting mathematical structure for efficient problem-solving. Striking a balance between the fidelity of a model and its tractability requires a deep understanding of both its application and mathematical structure. My research as a postdoctoral fellow at Purdue University centers on applying the GDP modeling framework in several systems relevant to Chemical Engineering, not limited to the water sector. As part of the Systems Engineering via Classical and Quantum Optimization for Industrial Applications (SECQUOIA) research group, I am collaborating with my coworkers to update an open-source repository of GDP problems. The GDPlib library (https://github.com/SECQUOIA/gdplib) is meant to guide optimization modelers in adopting this paradigm and to enable algorithm developers to use these examples as a benchmark for enhancing solution methods for these relevant and challenging problems. For instance, the repository now includes GDP models to optimize water networks and reverse electrodialysis processes. Bilinearities and concave functions in both models lead to multiple local optima, needing expensive optimization methods to reach global optimality. By incorporating quadratic and piecewise linear approximations for nonlinear terms, the GDP models are reformulated into quadratic GDP (QGDP) models, which can be solved more efficiently by suitable solvers [4].

Presentations at the current AIChE Annual Meeting

Improving Sustainability in the Water Sector with Reverse Electrodialysis Optimization for Renewable Electricity Generation from Salinity Gradients. Carolina Tristán, Marcos Fallanza, Raquel Ibáñez, Ignacio E. Grossmann, David E. Bernal Neira. Session: 10A: Process Synthesis & Design for Sustainability I.

GDPLib: An Open Library of Generalized Disjunctive Programming Problems and Solution Method Benchmarking. Carolina Tristán, Albert Lee, Zedong Peng, David E. Bernal Neira Session: 10C: Interactive Session: Systems and Process Operations.

Selected Recent Publications

[1] C. Tristán, M. Fallanza, R. Ibáñez, I. Ortiz, Recovery of salinity gradient energy in desalination plants by reverse electrodialysis, Desalination. 496 (2020) 114699. doi:10.1016/j.desal.2020.114699.

[2] C. Tristán, M. Rumayor, A. Dominguez-Ramos, M. Fallanza, R. Ibáñez, I. Ortiz, Life cycle assessment of salinity gradient energy recovery by reverse electrodialysis in a seawater reverse osmosis desalination plant, Sustain. Energy Fuels. 4 (2020) 4273–4284. doi:10.1039/d0se00372g.

[3] C. Tristán, M. Fallanza, R. Ibáñez, I. Ortiz, I.E. Grossmann, A generalized disjunctive programming model for the optimal design of reverse electrodialysis process for salinity gradient-based power generation, Comput. Chem. Eng. 174 (2023) 108196. doi:https://doi.org/10.1016/j.compchemeng.2023.108196.

[4] C. Tristán, M. Fallanza, R. Ibáñez, I.E. Grossmann, D.E. Bernal, Global Optimization via Quadratic Disjunctive Programming for Water Networks Design with Energy Recovery, Elsevier Masson SAS, 2024. doi:10.1016/B978-0-443-28824-1.50361-6.