Good modeling practice for the calibration of ion exchange breakthrough prediction

Abstract

Ion exchange (IX) is a key technology in resource recovery processes for demineralization and fit-for-purpose water production due to its inherent ion-selective recovery properties. A major bottleneck in the optimization of the IX process is the accurate prediction of ion breakthrough times, which has the potential to save on regeneration chemicals by maximizing resin utilization. However, the models used to predict ion breakthrough times are often unreliable due to poor calibration methods and significant uncertainty in parameter estimates. A well-calibrated ion breakthrough prediction model could provide valuable insights into the process, enabling optimization and model-based control with the aim of improving the overall efficiency and sustainability of the process. Consequently, we conducted local and global sensitivity analyses to identify the design and equilibrium parameters that contribute most to the prediction of breakthrough curves. The global sensitivity analysis allowed us to select a limited subset of parameters for calibration, which showed that only two parameters, namely the maximum adsorption capacity isotherm parameter and the resin particle size, need to be thoroughly calibrated to obtain an accurate prediction of the breakthrough curve. We also showed that uncertainty quantification for model calibration is important to establish the reliability of the predictions. The validation of the model was carried out using experimental data. Hence, we propose a robust calibration procedure, based on good modeling practice, that encompasses both sensitivity and uncertainty analyses, and therefore provides a basis for the optimization of the IX process with the aim of improving the accuracy of breakthrough prediction.