Table_1_Object-Oriented Modeling of a Capacitive Deionization Process.DOCX

The process of capacitive deionization (CDI) is a cost effective and energy efficient method that offers many opportunities in terms of desalination of brackish water and the removal of ionic contaminants. Current research focusses on evaluating different influence parameters to make the CDI process more competitive to other commercially available methods like reverse osmosis, direct distillation or ion exchange. CDI is based on the adsorption of ions to highly porous electrodes by applying an external voltage difference. Although, remarkable progress in CDI modeling has been achieved during the past decade, so far, only few models exist which fully describe the CDI process and which predict the cell behavior in all its aspects, including e.g., performance under constant voltage and/or constant current control or pH effects including water dissociation. However, in this paper a new approach to CDI modeling is presented, which opens a path to fast and easy implementation of a digital depiction of complex CDI setups having e.g., multiple cells. The model is based on the object-oriented modeling language Modelica that enables the simulation and prediction of the behavior of complete CDI cells by combining chemical, electrochemical and electrical components. Furthermore, there is the possibility to predict complex setups with e.g., complex electrolytes, concentration or voltage fluctuations as they appear due to environmental influences outside laboratory experiments. Besides detailed time courses of species concentrations in the bulk and the electrodes or local electrical potentials, the model enables the prediction of important sum parameters such as the salt adsorption capacity, current efficiency and power consumption. The results of the developed CDI model are validated by using parameter settings from literature and comparing the resulting predictions of equilibrium and kinetics. In addition, the agreement between our own experimental results and the respective model predictions is discussed.