Model-based design of experiments for efficient and accurate isotherm model identification in High Performance Liquid Chromatography

Chromatography is a key purification process in the pharmaceutical industry. The process design is based on knowledge of the adsorption isotherm that describes the separation within the chromatographic column. Although obtaining the values of isotherm model parameters has traditionally been the work of experimentalists, recently design methods based on mathematical models have emerged, and for these, accurate isotherm models and model parameter values are crucial. Different methods exist for parameter estimation, all depending on experiment execution. Model-Based Design of Experiments (MBDoE) can be used to optimally design experiments that maximise the information obtained from each experiment. In this work, we propose an MBDoE-based methodology that aims to identify the most suitable isotherm model, to estimate its parameters, and to evaluate its predictive capability. The methodology is tested on an in-silico case study where the performance is compared to that of traditional factorial design of experiments.