Effects of uncertainties in experimental conditions on the estimation of adsorption model parameters in preparative chromatography

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Abstract

Model-based process design is increasingly popular when designing pharmaceutical purification processes. The effect of uncertainties in concentration measurements on the estimation of model parameters is analyzed for two cases of non-isocratic adsorption chromatography. A model, calibrated to experiments, is used to generate data by adding a Monte Carlo sampled error in the inlet concentrations. New model parameters are estimated by minimizing the deviation between the synthetic data and the model. The first case is a separation of rare earth elements by ion-exchange chromatography and the second case is a purification of insulin from a product-related impurity by reversed-phase chromatography. It is shown that normally distributed errors in the concentrations result in deviations in the UV-signal that are not normally distributed. With the applied method, known concentration distributions can be translated into probability distributions of the model parameters, which can be taken into account in the model-based process design. (C) 2013 Elsevier Ltd. All rights reserved.

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Subject classification (UKÄ) – MANDATORY

  • Chemical Engineering

Keywords

  • Modeling Ion-exchange chromatography, Reversed-phase chromatography, Model calibration, Parameter estimation, Parameter uncertainty
Original languageEnglish
Pages (from-to)148-157
JournalComputers & Chemical Engineering
Volume55
Publication statusPublished - 2013
Publication categoryResearch
Peer-reviewedYes