Optimal Experimental Design for the Determination of Isotherm Parameters

Optimal experimental design (OED) – think first, then act!

Figure: (a) Lateral view and (b) axial view of the confidence ellipsoid. HIC isotherm parameters n, k_ads and k_des were estimated with the optimum being located skew to the parameter axes which show the remaining size of the confidence interval in percent compared to the estimation of the residual

Ion-exchange, hydrophobic interaction or mixed mode chromatography – model predictions can be improved by creating particularly reliable models of common chromatographic purification processes. ChromX allows the identification of optimal experimental set-ups for a fast and robust model calibration.

Chromatography models contain many parameters which are difficult to measure experimentally. These model parameters are, for instance, activity coefficients or interaction coefficients. It is hard to predict the optimal experimental set-up to determine these parameters for model calibration, especially in multi-component settings. The concept of optimal experimental design takes one step back:

Parameter sensitivity analysis to predict optimal estimation experiments. In an OED approach, first the experimental set-up with the highest information content is identified. Uncertainties of parameters are accessed by taking into account their parameter confidence intervals. Driven by a minimization of these, new experiments are then suggested. Resulting ideal experiments for model building may then look completely different to the typical Design of Experiment runs. With this method, model parameters with maximal statistical significance were found using the lowest number of experiments. The approach needs less time and material compared to manual experimental design.

Improved reliability of isotherm parameters. This approach can be successfully used for automated process development. The reliability of isotherm parameters as well as the robustness of downstream processing can be enhanced. Nevertheless, expert knowledge is still necessary to set an appropriate parameter space.

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