The continuous research to find answers for more energy efficient desalination technolo-gies has introduced the concept of MCDI (Membrane Capacitive Deionization). In the re-cent decades this technology has gathered so much attention and numerous research for elaboration of different phenomena inside an MCDI cell is done. However, the need for combining electrochemical and flow dynamics simulation is not answered yet. In this study the MCDI test unit of Innovation, located in Dresden University of Technology is used, the aim is to produce solid and accurate data which can be trusted for calibration and verification of further MCDI simulation.
The Innovation test unit reports many parameters during the experiment but the im-portant data missing for calibration of simulation is the instant concentration of salt in the outflow solution. The key parameter that can lead to instant solution concentration is the real time solution electrical conductivity. The solution electrical conductivity can re-late directly to the salt concentration of the solution, when only one salt is present in the solution of the experiments. Therefore, multiple single salt solutions specifically for this purpose are prepared and many experiments are done. Two salts of NaCl and Na2SO4 are chosen for the experiments (only one of them present at each experiment). Multiple pa-rameters including usually reported values of Desalination Capacity (DC), Charge Efficien-cy (CE), Salt Adsorption Capacity (SAC), Average Salt Adsorption Rate (ASAR), Specific Ener-gy Consumption (SEC) and also a few new parameters such as Energy Consumed Per Ion (ECI) and mean pH induced conductivity error are also calculated and presented to help further study and simulations achieve more robust and accurate data.
The raw data out of the Innovation MCDI test unit can not directly used. Since multiple parameters such as temperature, pH induced conductivity and also unique characteris-tics of Innovation test unit such as locations of the electroconductivity and pH sensors are affecting the results. All this effects alongside other remarks including statistical analysis to find representative equilibrium cycle for further calculations are discussed and calcu-lated in this study. Statistical analyses are also done to provide better and more stable and reproducible experimental procedure methods. The main tool for all these analyses is Python programming language. In this study the previous python code written for Inno-vation test unit data analysis is further developed and all needed modification mentioned are also added.
It is found that cell desalination performance is much lower in comparison to literature and the energy consumption is much higher. It is also shown that pH changes are very important for electrical conductivity values and in some cases, can affect the results even in the range of 50%. At last, in the end of the study, multiple experimental Data set is chosen that can be trusted in any form for further calibration, validation and verification of models.