My research focuses on process modeling, optimization, and transport phenomena in chemical and electrochemical systems, with a particular emphasis on capacitive deionization (CDI). I combine experimental studies with thermodynamic, transport, and fluid dynamic modeling to gain deeper insights into the separation processes. My background also includes data-driven methods such as machine learning and image processing, which could support future work in analysis and predictive modeling.
Related Oral Presentations
Title: A Two-Dimensional Model for Predicting the Electrochemical Separation of Carboxylates with Capacitive Deionization
Abstract number: 713559
Abstract
Capacitive deionization (CDI) is an emerging electrochemical technique for ion removal and recovery, based on the application and removal of a potential difference between two electrodes. Upon applying a voltage, ions migrate toward oppositely charged porous electrodes and are immobilized within the electric double layer (EDL). When the voltage is removed, the ions are released, enabling their recovery. In this study, capacitive deionization (CDI) was employed to separate and recover carboxylates, specifically acetate and butyrate, using a 10 mM influent solution at an initial pH of 6.2.
Experiments were conducted at three applied voltages: 0.8, 1.0, and 1.2 V and three flow rates: 4, 6, and 8 mL/min. For both carboxylates, the adsorption capacity increases with higher applied voltage and lower flow rate. The highest adsorption capacities, approximately 0.43 mmol/g for acetate and 0.40 mmol/g for butyrate, are achieved at an applied voltage of 1.2 V and a flow rate of 4 mL/min. Additionally, this study introduces a fully coupled two-dimensional model to simulate ion adsorption and desorption during the application and removal of voltage under varying conditions. The model describes ion transport in a porous conductive medium by incorporating diffusion, convection, electromigration, and electric double layer (EDL) capacitance, using a modified Donnan approach combined with the Nernst-Planck equation. Its predictions are validated against experimental data, offering valuable insights for optimizing CDI performance and supporting the scale-up of the technology for industrial applications.