Good modeling practice for the calibration of ion exchange breakthrough prediction

Abstract

Ion exchange (IX) is a key technology in resource recovery for demineralization and fit-for-purpose water production thanks to its ion-selective recovery features. 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. The models used to predict ion breakthrough times are often unreliable due to poor calibration methods and high uncertainty in parameter estimates. A well-calibrated model for ion breakthrough prediction can provide important insights into the process and enable optimization and model-based control with the goal of improving the overall e_iciency and sustainability of the process. Therefore, we performed local and global sensitivity analyses to identify the design and operational parameters that contribute most to the prediction of breakthrough curves. The global sensitivity analysis allowed us to select a small 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. Validation of the model was carried out using experimental data. Hence, we propose a sound calibration procedure, based on good modeling practice, that encompasses both sensitivity and uncertainty analyses and provides a basis for the optimization of the IX process with the aim of improving the accuracy of breakthrough prediction.