h1

%0 Thesis
%A Heymann, William
%T Advanced parameter estimation, Bayesian uncertainty quantification, and surrogate modeling for chromatography processes
%I Rheinisch-Westfälische Technische Hochschule Aachen
%V Dissertation
%C Aachen
%M RWTH-2024-10846
%P 1 Online-Ressource : Illustrationen
%D 2023
%Z Veröffentlicht auf dem Publikationsserver der RWTH Aachen University 2024
%Z Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2023
%X Manufacturing medicine is hard and expensive. Manufacturing protein-based medicines can be especially difficult due to the size and complexity of the molecules. Packed bed liquid chromatography is used during the manufacturing process to remove impurities. The problem with liquid phase chromatography is that it is an expensive and difficult process to develop. The goal of chromatography modeling is to make it faster and easier to develop a chromatography process that purifies that target protein. Chromatography modeling is difficult and the process of designing a model, designing experiments, calibrating the model, and determining the uncertainty of the model parameters is a complex and computationally intensive process. This thesis covers model calibration, parameter uncertainty, and surrogate modeling. Model calibration is done with parameter estimation where the parameters of a model are estimated based on chromatogram data. While least squares estimation of unknown parameters is a well-established standard it can also suffer from critical disadvantages. The description of real-world systems is generally prone to unaccounted mechanisms in the models that are customarily applied, such as dispersion in external holdup volumes, and systematic measurement errors, such as caused by pump delays. In this scenario, matching the shape between simulated and measured chromatograms has been found to be more important than the exact peak positions. A new score system is demonstrated that separately accounts for the shape, position, and height of individual peaks. A genetic algorithm is used for optimizing these multiple objectives. Even for non-conflicting objectives, this approach shows superior convergence in comparison to single-objective gradient search, while conflicting objectives indicate incomplete models or inconsistent data. In the latter case, Pareto optima provide important information for understanding the system and improving experiments. Once a model is calibrated the next step is to determine how well defined the model is. With increased dependence on modeling, it is not enough to calibrate a model to experimental data. No model is perfect, and no model can perfectly explain experimental data. What is increasingly needed is the uncertainty in the calibrated model’s parameters along with the uncertainty in the resulting chromatogram. A method is presented to determine the probability distribution of parameters for a calibrated model using Bayesian uncertainty quantification. This method incorporates experimental errors such as pump delays, pump flow rates, loading concentration, and UV measurement error. The method presented here is based on Bayes’ theorem and uses Markov Chain Monte Carlo with an ensemble sampler and covers how to build and evaluate the error model. While the model calibration and parameter uncertainty analysis presented here work well for synthetic and industrial data, they also require a lot of computing resources. A surrogate model approximates the original model and can stand in for the real model under restricted conditions. The advantage of surrogate models is they can be tens of thousands of times faster than the original model. Construction of a surrogate model using an artificial neural network is demonstrated along with the entire network design process. All software created for this project is freely available as open-source code on GitHub (https://github.com/modsim/CADET-Match).
%F PUB:(DE-HGF)11
%9 Dissertation / PhD Thesis
%R 10.18154/RWTH-2024-10846
%U https://publications.rwth-aachen.de/record/996812