Abstract
Capacitive Deionization (CDI) is an emerging electrochemical technology for energy-efficient brackish water desalination. However, the rigorous design and scale-up of CDI systems are frequently hindered by the complexity of validating predictive models. The coupling of electrochemical double-layer kinetics with macroscopic mass transport often leads to structural parameter correlations, where multiple combinations of kinetic rates yield indistinguishable effluent trajectories. This paper addresses these challenges by proposing a simulation-driven Model-Based Design of Experiments (MBDoE) framework. We develop and implement a reduced-order Dynamic Langmuir (DL) model within the gPROMS platform, designed to capture cyclic adsorption-desorption dynamics with high computational efficiency. Sensitivity analysis reveals that information content is highly transient, concentrated primarily in the short time windows following voltage switching, and that the effluent concentration is significantly more sensitive to desorption kinetics than adsorption. Fisher Information Matrix (FIM) analysis of baseline experimental data confirms a strong negative correlation between kinetic parameters, resulting in a poorly conditioned estimation problem. To resolve this, a large-scale in-silico screening of the experimental design space-spanning inlet concentration, flow rate, and cell volume-is conducted using a D-optimality criterion. The simulation results demonstrate that operating at low flow rates and large effective cell volumes maximizes parameter identifiability by enhancing the separation of dynamic signatures. This work illustrates the critical role of dynamic simulation in guiding experimental strategy, minimizing trial-and-error effort, and improving the robustness of process models.