ChromaTech: A discontinuous Galerkin spectral element simulator for preparative liquid chromatography

Meyer, Kristian; Leweke, Samuel; Lieres, Eric von; Huusom, Jakob Kjøbsted; Abildskov, Jens

doi:10.1016/j.compchemeng.2020.107012

Cited by 20 publications

(8 citation statements)

References 62 publications

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“…For the case studies using the SMA isotherm, the separation of lysozyme from ribonuclease A and cytochrome c using NaCl as modifier was studied for an ion‐exchange chromatography separation. The parameters 33 are shown in Table 3.…”

Section: Resultsmentioning

confidence: 99%

“…T A B L E 3 Parameters for the SMA isotherm case studies in Equation ( 13) where the first component for K eq and σ refers to the modifier. 33 Parameter Value was set to 282 mol/m 3 which resulted in a relatively strong adsorption with initial slopes ranging from 32 to 1.6. For the SMA isotherm, there is no analytical solution available for the TMB ideal model in Equation (1).…”

Section: Case Study 4: Multicomponent Isocratic Smbmentioning

confidence: 99%

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Shortcut design method for multicomponent gradient simulated moving beds

Frandsen,

Huusom,

Gernaey

et al. 2023

AIChE Journal

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This work describes a method for design (and verification) of isocratic and gradient simulated moving bed (SMB) chromatography, based on the triangle theory. The method is tested using both constant selectivity and nonconstant selectivity isotherms as well as implicit and explicit isotherms through the Linear, Langmuir, and SMA isotherms. The results show that the method is applicable to a wide range of isotherms and to multicomponent systems. The study also investigates the influence of modifier concentrations on gradient SMB performance, by determining the optimal flow rate ratios for calculating volume‐specific productivity and eluent consumption. These findings demonstrate the potential of the method to improve the performance of a gradient SMB and serve as a useful initial step in the design and optimization of operational flow rates and modifier concentrations.

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Section: Resultsmentioning

confidence: 99%

Section: Case Study 4: Multicomponent Isocratic Smbmentioning

confidence: 99%

Shortcut design method for multicomponent gradient simulated moving beds

Frandsen,

Huusom,

Gernaey

et al. 2023

AIChE Journal

Self Cite

View full text Add to dashboard Cite

show abstract

“…Depending on the complexity, the mechanistic models vary from simple equilibrium-dispersive models with negligible mass transfer resistances to more complex generalized rate models . Many commercial softwares of chromatographic mechanistic models, such Chromatographic Analysis and Design Toolkit (CADET), Aspen Chromatography, gPROMS, GoSilico, ChromaTech, etc., exist and offer simulation capabilities for a range of different applications. Moreover, the adsorption equilibria of solute components in chromatographic columns are characterized using their isotherms, which quantify the equilibrium relationship between the liquid- and the solid-phase concentrations.…”

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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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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“…Depending on the complexity, the mechanistic models vary from simple equilibrium-dispersive models with negligible mass transfer resistances to more complex generalized rate models. 10 Many commercial softwares of chromatographic mechanistic models, such Chromatographic Analysis and Design Toolkit (CADET), 11 Aspen Chromatography, gPROMS, GoSilico, 12 ChromaTech, 13 etc., exist and offer simulation capabilities for a range of different applications. Moreover, the adsorption equilibria of solute components in chromatographic columns are characterized using their isotherms, which quantify the equilibrium relationship between the liquid-and the solid-phase concentrations.…”

Section: Introductionmentioning

confidence: 99%

Can a computer “learn” non-linear chromatography?: Experimental validation of physics-based deep neural networks for the simulation of chromatographic processes

Subraveti

Prasad

et al. 2023

Preprint

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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 isotherm-agnostic abilities of PANACHE models are tested by considering two binary systems with complex adsorption equilibria: 1) mixed Langmuir M1-type binary system, and 2) Tröger'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 L2-norm, for most of the cases. The ability of PANACHE models to infer the adsorption isotherms is also demonstrated.

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ChromaTech: A discontinuous Galerkin spectral element simulator for preparative liquid chromatography

Cited by 20 publications

References 62 publications

Shortcut design method for multicomponent gradient simulated moving beds

Shortcut design method for multicomponent gradient simulated moving beds

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

Can a computer “learn” non-linear chromatography?: Experimental validation of physics-based deep neural networks for the simulation of chromatographic processes

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