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High Throughput Screening for the Design and Optimization of Chromatographic Processes: Assessment of Model Parameter Determination from High Throughput Compatible Data
Author(s) -
Susanto A.,
KniepsGrünhagen E.,
von Lieres E.,
Hubbuch J.
Publication year - 2008
Publication title -
chemical engineering and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.403
H-Index - 81
eISSN - 1521-4125
pISSN - 0930-7516
DOI - 10.1002/ceat.200800457
Subject(s) - throughput , bottleneck , experimental data , design of experiments , process (computing) , computer science , biological system , estimation theory , yield (engineering) , chromatography , process engineering , chemistry , algorithm , mathematics , engineering , materials science , statistics , telecommunications , metallurgy , wireless , biology , embedded system , operating system
Abstract Chromatographic processes can be optimized in various ways, and the two most prominent approaches are based either on statistical data analysis or on experimentally validated simulation models. Both strategies rely heavily on experimental data, the generation of which usually imposes a significant bottleneck on rational process design. The latter approach is followed in this work, and the utilizability of high throughput compatible experiments for the determination of model parameters which are required for in silico process optimization, is assessed. The unknown parameter values are estimated from batch uptake experiments on a robotic platform and from dynamic breakthrough experiments with miniaturized chromatographic columns. The identified model is then validated with respect to process optimization by comparison of model predictions with experimental data from a preparative scale column. In this study, a strong cation exchanger Toyopearl SP‐650M and lysozyme solved in phosphate buffer (pH 7), is used as the test system. The utilization of data from miniaturized and high throughput compatible experiments is shown to yield sufficiently accurate results, and minimizes efforts and costs for both parameter estimation and model validation.