Estimation of ion adsorption using iterative analytical model in capacitive deionization process

Abstract

Capacitive deionization (CDI) is an upcoming technique that can replace existing processes for removing and recuperating metal ions from dilute industrial waste waters. CDI removes ions via electrosorption on to its electrode surfaces, the efficiency of which is a function of CDI electrode properties that progressively change during continued operation. As such a need exists to develop a model to predict CDI performance over elongated periods which is independent of electrode properties and has negligible error values. By applying a first order non-linear dynamic model (FONDM) with inputs independent of the electrode characteristics, we propose a universal model that can predict CDI ion adsorption capacity with changes in applied potential, flow rate and electrolyte temperature to within 5% of the experimentally obtained results. The model was verified using activated carbon cloth (ACC) as a test electrode and aqueous sodium chloride solution as electrolyte, with a good prediction for ion electrosorption efficiency and time dependent electrosorption dynamics. The simplicity of the model makes it easy to adapt for various applications and in the development of intelligent control systems for CDI units in practical settings.