Parameter estimation for the simulation of liquid chromatography

Altenhöner, Ulf; Meurer, Markus; Strube, Jochen; Schmidt–Traub, H.

doi:10.1016/s0021-9673(97)00173-8

Cited by 79 publications

(48 citation statements)

References 13 publications

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“…From our experimental results, we can recommend the correlation of Giddings [18] in connection with empirical parameters determined by Tallarek [22] with NMR measurements for both dispersion coefficients. The correlations of Koch and Brady, Knox and Saleem [27], Huber [28] and of Chung and Wen [29] also give good results for axial and the correlations of Koch and Brady and of Saffman [32] give good results for lateral dispersion.…”

Section: Discussionmentioning

confidence: 72%

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A Rigorous Model for Annular Chromatography

Brozio

Bart

2004

Chem Eng & Technol

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Modeling of annular chromatography is refined to incorporate the influence of axial and annular dispersion. Annular and axial dispersion coefficients are measured simultaneously in an annular chromatograph using an experimental technique based on the introduction of a non-adsorbable tracer. The annular dispersion coefficient is found to be one order of magnitude lower than the axial one. Nevertheless, annular dispersion has a more pronounced effect on total dispersion when steep concentration profiles are present. A criterion to judge whether annular dispersion is negligible is developed. If the circumferential velocity is at least 0.88 times higher than the interstitial velocity, lateral dispersion can be omitted. Based on this criterion it can be concluded that the impact of annular dispersion decreases with the radius of the annulus. Therefore, it is preferable to build annular chromatographs with a large radius and a thin annulus rather than using a small radius and a thick annulus.

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Section: Discussionmentioning

confidence: 72%

“…This macromolecule is too large to diffuse into the pores of the resin. It is a widely used tracer for the determination of dispersion effects [18]. The dextran concentration is determined with an RI detector.…”

Section: Apparatus and Chemicalsmentioning

confidence: 99%

A Rigorous Model for Annular Chromatography

Brozio

Bart

2004

Chem Eng & Technol

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“…An alternative method is applying correlations using the Péclet (Pe) and the Reynolds (Re) number. Both ways are good engineering practice e.g., in chromatography [32][33][34].…”

Section: Process Modelingmentioning

confidence: 99%

Systematic and Model-Assisted Evaluation of Solvent Based- or Pressurized Hot Water Extraction for the Extraction of Artemisinin from Artemisia annua L.

Sixt

Strube

2017

Processes

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Abstract:In this study, the solvent based extraction of artemisinin from Artemisia annua L. using acetone in percolation mode is compared to the method of pressurized hot water extraction. Both techniques are simulated by a physico-chemical process model. The model as well as the model parameter determination, including the thermal degradation of artemisinin are shown and discussed. For the conventional extraction, a solvent screening is performed considering various organic solvents. A temperature screening is presented for the systematic design of the pressurized hot water extraction. The best temperature with regards to thermal decomposition and high productivity was found to be 80 • C. Both, conventional percolation and Pressurized Hot Water Extraction (PHWE) are suitable for the extraction of artemisinin. The extraction curves show a high conformity with the simulation results.

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“…In such situations one should never try to estimate too many model parameters at the same time, since different mechanisms might have a similar impact on the resulting chromatogram. Altenhöner et al [15] proposed a sequence of experiments to guarantee a consistent determination of the applied model parameters: The experiments are designed and performed in a consecutive sequence with an increasing number of effects that influence the experimental results. Hence, only a few parameters values are estimated at one time and the resulting values are then fixed for the determination of all remaining parameters.…”

Section: Determination Of Model Parametersmentioning

confidence: 99%

High Throughput Screening for the Design and Optimization of Chromatographic Processes: Assessment of Model Parameter Determination from High Throughput Compatible Data

et al. 2008

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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.

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Parameter estimation for the simulation of liquid chromatography

Cited by 79 publications

References 13 publications

A Rigorous Model for Annular Chromatography

A Rigorous Model for Annular Chromatography

Systematic and Model-Assisted Evaluation of Solvent Based- or Pressurized Hot Water Extraction for the Extraction of Artemisinin from Artemisia annua L.

High Throughput Screening for the Design and Optimization of Chromatographic Processes: Assessment of Model Parameter Determination from High Throughput Compatible Data

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