Modeling of Chromatographic Processes*

Seidel‐Morgenstern, Andreas

doi:10.1002/9783527816347.ch6

Cited by 8 publications

(11 citation statements)

References 50 publications

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“…In line with other industrial case studies, 26,30 the transport dispersive model (TDM) 33 was selected to describe the temporal change of the solute bulk concentration c i of solute i in the interstitial column volume

\frac{\partial c_{i}}{\partial t} (x, t) = - u_{italicint} \frac{\partial c_{i}}{\partial x} (x, t) + D_{italicax} \frac{\partial^{2} c_{i}}{\partial x^{2}} - \frac{1 - ε}{ε} \frac{6}{d_{p}} k_{eff, i} (c_{i} (x, t) - c_{p, i} (x, t)),

where, x represents the axial position within the column, t is the time, D ax denotes the axial dispersion coefficient, k eff , i is the effective mass transfer coefficient, u int is the interstitial velocity, ε represents the void fraction, and c p , i is the concentration of the i th solute in the pore volume. As this model only considers an average concentration in the pore volume, k eff , i accounts for both internal and external mass transfer resistance.…”

Section: Theorymentioning

confidence: 75%

“…As this model only considers an average concentration in the pore volume, k eff , i accounts for both internal and external mass transfer resistance. A General Rate Model (GRM) 33 can be used to separate these two mass transfer effects and to achieve better predictions, especially under overloaded conditions. In order to determine the additional parameters of the GRM, however, dedicated experiments are necessary, ideally breakthrough curves, 29 which were considered out‐of‐scope for this study.…”

Section: Theorymentioning

confidence: 99%

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Mechanistic modeling, simulation, and optimization of mixed‐mode chromatography for an antibody polishing step

Hahn¹,

Geng²,

Petrushevska-Seebach

et al. 2022

Biotechnology Progress

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Mixed‐mode chromatography combines features of ion‐exchange chromatography and hydrophobic interaction chromatography and is increasingly used in antibody purification. As a replacement for flow‐through operations on traditional unmixed resins or as a pH‐controlled bind‐and‐elute step, the use of both interaction modes promises a better removal of product‐specific impurities. However, the combination of the functionalities makes industrial process development significantly more complex, in particular the identification of the often small elution window that delivers the desired selectivity. Mechanistic modeling has proven that even difficult separation problems can be solved in a computer‐optimized manner once the process dynamics have been modeled. The adsorption models described in the literature are also very complex, which makes model calibration difficult. In this work, we approach this problem with a newly constructed model that describes the adsorber saturation with the help of the surface coverage function of the colloidal particle adsorption model for ion‐exchange chromatography. In a case study, a model for a pH‐controlled antibody polishing step was created from six experiments. The behavior of fragments, aggregates, and host cell proteins was described with the help of offline analysis. After in silico optimization, a validation experiment confirmed an improved process performance in comparison to the historical process set point. In addition to these good results, the work also shows that the high dynamics of mixed‐mode chromatography can produce unexpected results if process parameters deviate too far from tried and tested conditions.

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\frac{\partial c_{i}}{\partial t} (x, t) = - u_{italicint} \frac{\partial c_{i}}{\partial x} (x, t) + D_{italicax} \frac{\partial^{2} c_{i}}{\partial x^{2}} - \frac{1 - ε}{ε} \frac{6}{d_{p}} k_{eff, i} (c_{i} (x, t) - c_{p, i} (x, t)),

Section: Theorymentioning

confidence: 75%

Section: Theorymentioning

confidence: 99%

Mechanistic modeling, simulation, and optimization of mixed‐mode chromatography for an antibody polishing step

Hahn¹,

Geng²,

Petrushevska-Seebach

et al. 2022

Biotechnology Progress

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show abstract

“…Assuming an instantaneous equilibrium between the phases, the interparticular concentration is equal to the average intraparticular concentration. Thus, the driving force for diffusion is neglected, and all effects caused by the axial dispersion and other diffusion effects are lumped into an apparent axial dispersion coefficient to describe peak broadening . This assumption yields the equilibrium dispersive model, which would be used in this work.…”

Section: Methodsmentioning

confidence: 99%

“…Thus, the driving force for diffusion is neglected, and all effects caused by the axial dispersion and other diffusion effects are lumped into an apparent axial dispersion coefficient to describe peak broadening. 18 This assumption yields the equilibrium dispersive model, which would be used in this work. Additionally, the adsorption equilibrium was described by the SMA model, with four parameters (characteristic charge ν, equilibrium coefficient k eq , shielding factor σ, and kinetic coefficient k kin ).…”

Section: Model Introductionmentioning

confidence: 99%

Practical Teaching of Modeling Tools for Ion-Exchange Chromatography: A Case Study

Chen,

Lin

et al. 2023

J. Chem. Educ.

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The utilization of modeling tools has gained significant attention recently. These models typically involve a series of partial differential equations, which can be challenging for novice modelers. This paper presented an approach in practical teaching of modeling tools for ion-exchange chromatography with three parts: model introduction, model calibration through group learning using different calibration strategies, and model applications. This approach was integrated into the traditional bioseparation engineering curriculum as an activity, using the separation of monomer–dimer mixtures of monoclonal antibodies as an example. Results of competitive group learning and the Wilcoxon test revealed that the parameter-by-parameter method was more user-friendly than the Yamamoto method for novice modelers to obtain reasonable model parameters quickly. Then, students used the well-fitted model for process optimization and explored the effects of process parameters and material input variation on the chromatography process, which helped students appreciate the critical role of time and material savings achieved through modeling tools. Finally, the student questionnaire results revealed that over two-thirds of the students gave positive feedback on the activity. Through this practical teaching, students became familiar with chromatography modeling tools, moving away from the tedious formulaic descriptions found in traditional modeling courses. This well-designed activity can be expanded from academia to industry, transforming the novice modelers into experienced modelers who can meet the high demands of the modern biopharmaceutical industry.

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“…Linear adsorption isotherms are prerequisite for symmetric peaks without tailing. 104 Noteworthily, moderate separation factors of d -glucose and d -fructose in the range of 2.5–3.5 present a disadvantage of Ca 2+ ion exchange resins. 105,106…”

Section: Performance Of Boronate-functionalized Materials Vs Ca2+ Ion...mentioning

confidence: 99%

Adsorptive separation of saccharides and polyols over materials functionalized with boronate groups

Delidovich,

Toussaint

2024

Green Chem.

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Modeling of Chromatographic Processes*

Cited by 8 publications

References 50 publications

Mechanistic modeling, simulation, and optimization of mixed‐mode chromatography for an antibody polishing step

Mechanistic modeling, simulation, and optimization of mixed‐mode chromatography for an antibody polishing step

Practical Teaching of Modeling Tools for Ion-Exchange Chromatography: A Case Study

Adsorptive separation of saccharides and polyols over materials functionalized with boronate groups

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