2010
DOI: 10.1016/j.chroma.2010.08.063
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Integration of scale-down experimentation and general rate modelling to predict manufacturing scale chromatographic separations

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Cited by 36 publications
(7 citation statements)
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References 27 publications
(35 reference statements)
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“…20–25 Once best estimates of the binding parameters (maximum capacity q max,n , adsorption coefficient k ads,n , and desorption coefficient k d,n ) of the protein, which gave the closest prediction, based on the least square method, of the experimental breakthrough curve for a given flow rate were determined, these were then fixed and the completed model was used to simulate breakthrough curves at various other operating flow rates. The predictive capability of this method at various scales has been demonstrated by Gerontas et al26…”
Section: Methodsmentioning
confidence: 92%
“…20–25 Once best estimates of the binding parameters (maximum capacity q max,n , adsorption coefficient k ads,n , and desorption coefficient k d,n ) of the protein, which gave the closest prediction, based on the least square method, of the experimental breakthrough curve for a given flow rate were determined, these were then fixed and the completed model was used to simulate breakthrough curves at various other operating flow rates. The predictive capability of this method at various scales has been demonstrated by Gerontas et al26…”
Section: Methodsmentioning
confidence: 92%
“…The separation of proteins by packed bed chromatography depends on the differences in the mass and binding affinity or charge [29]. The limitations of packed bed chromatography include a high pressure drop across the packed bed, slow film and pore diffusions, a difficulty in packing the column, and a complicated scale-up [30,31]. In contrast to the packed bed column, the characteristics of the monolith-based separation process are a laminar convective mass transfer, a higher dynamic binding capacity with a solute, an easy scale-up, and a high rate of solute recovery.…”
Section: Introductionmentioning
confidence: 99%
“…In this context, mathematical models can immensely reduce the number of experiments to be performed and can strategically guide process development and optimization [13] [6], resulting in accelerated development time scales and robust processes. Furthermore, mathematical models are promising tools to support scale-up [14] as well as operation in the manufacturing phase. In combination with UV or Raman sensors, they can be used to monitor breakthrough [15], resin aging [16], and in advanced control schemes [17][18][19].…”
Section: Introductionmentioning
confidence: 99%