Determination of Model Parameters*

Seidel‐Morgenstern, Andreas; Jupke, Andreas; Schmidt–Traub, H.

doi:10.1002/9783527816347.ch7

Cited by 2 publications

(3 citation statements)

References 38 publications

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“…The equilibrium-dispersive model derived from the conservation of mass adequately describes the solute transport in chromatographic columns. , The model assumes negligible mass transfer resistance and there exists an instantaneous equilibrium between the fluid and the solid phases. In addition, the velocity of the fluid phase remains constant along the column owing to dilute solute concentrations considered.…”

Section: Methodsmentioning

confidence: 99%

“…Preparative chromatography is an indispensable separation tool in pharmaceutical, food, and agrochemical industries. − Mathematical models have increasingly become essential for understanding and simulating chromatographic processes in the context of process development, optimization, monitoring, and control. − These models provide the basis to quantitatively understand and predict complex dynamics of solute movement in chromatographic columns. To this end, mechanistic models described by partial differential equations (PDEs) are used to model chromatographic processes by considering underlying physical phenomena such as convection, dispersion, mass transfer, and equilibria between the fluid and the solid phases.…”

Section: Introductionmentioning

confidence: 99%

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Can a Computer “Learn” Nonlinear Chromatography?: Experimental Validation of Physics-Based Deep Neural Networks for the Simulation of Chromatographic Processes

Subraveti

Prasad

et al. 2023

Ind. Eng. Chem. Res.

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This article presents the capabilities of machine learning in addressing the challenges related to the accurate description of adsorption equilibria in the design of chromatographic processes. Our previously developed physics-based artificial neural network framework for adsorption and chromatography emulation (PANACHE) approach is extended to simulate the dynamics of chromatographic columns without using adsorption isotherms. The incorporation of underlying conservation laws in the form of a physics-constrained loss function during training enables PANACHE models to infer the complex dynamics of chromatographic columns, even without the knowledge of adsorption isotherms. The isothermagnostic abilities of PANACHE models are tested by considering two binary systems with complex adsorption equilibria: 1) mixed Langmuir M 1 -type binary system and 2) Troger's base enantiomers on Chiralpak AD that show inflection in the isotherm. For each case study, unique feed-forward deep neural networks with an input layer, five hidden layers, and an output layer are trained for two solute components using the elution data either from simulations or experiments. The results show that PANACHE models are successful in predicting the dynamics of binary solute mixtures in chromatographic columns even in the absence of adsorption isotherms, with prediction errors in the order of 10 −2 in the measure of relative 2 -norm, for most of the cases. The ability of PANACHE models to infer the adsorption isotherms is also demonstrated.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Can a Computer “Learn” Nonlinear Chromatography?: Experimental Validation of Physics-Based Deep Neural Networks for the Simulation of Chromatographic Processes

Subraveti

Prasad

et al. 2023

Ind. Eng. Chem. Res.

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“…Therefore, its separation ability is very high and can isolate some difficult-to-separate components. In the cyclic course, however, the target band will spread ceaselessly due to the effect of various non-ideal factors such as axial dispersion and mass-transfer resistance, , which limited the sample loading and the number of switches. Furthermore, the dilution of the target impurity has not been effectively solved yet.…”

Section: Introductionmentioning

confidence: 99%

Purification and Concentration of Minor Impurity in the Bulk Drug by Step-Gradient Twin-Column Recycling Chromatography

Feng

Zhao

et al. 2023

Ind. Eng. Chem. Res.

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A weaker solvent was constantly added at the middle of the two columns to set up a two-step solvent gradient in twin-column recycling chromatography so that the eluotropic strength of the downstream liquid was reduced. Therefore, the back edge of the band moved faster than the front edge during the circulation of the solute band, initiating special gradient compression to effectively counteract the band spreading. Herein, modified recycling chromatography was applied to the simultaneous purification and concentration of minor impurities in the bulk drug. Taking orlistat as an example, the target impurity with only 0.7% content in orlistat bulk drug was isolated, giving a purity of 96%. Its concentration was enhanced by 9.4 times. Investigations on operating conditions verified that decreasing the eluent eluotropic strength, increasing the solvent gradient, extending the column switching interval, and enhancing the number of switches within proper ranges were beneficial to the improvement of separation.

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Determination of Model Parameters*

Cited by 2 publications

References 38 publications

Can a Computer “Learn” Nonlinear Chromatography?: Experimental Validation of Physics-Based Deep Neural Networks for the Simulation of Chromatographic Processes

Can a Computer “Learn” Nonlinear Chromatography?: Experimental Validation of Physics-Based Deep Neural Networks for the Simulation of Chromatographic Processes

Purification and Concentration of Minor Impurity in the Bulk Drug by Step-Gradient Twin-Column Recycling Chromatography

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