Mixed-Integer Nonlinear PDE-Constrained Optimization for Multi-Modal Chromatography

Dominik H. Cebulla (), Christian Kirches () and Andreas Potschka ()
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Dominik H. Cebulla: TU Braunschweig
Christian Kirches: TU Braunschweig
Andreas Potschka: Heidelberg University

A chapter in Operations Research Proceedings 2019, 2020, pp 81-87 from Springer

Abstract: Abstract Multi-modal chromatography emerged as a powerful tool for the separation of proteins in the production of biopharmaceuticals. In order to maximally benefit from this technology it is necessary to set up an optimal process control strategy. To this end, we present a mechanistic model with a recent kinetic adsorption isotherm that takes process controls such as pH and buffer salt concentration into account. Maximizing the yield of a target component subject to purity requirements leads to a mixed-integer nonlinear optimal control problem constrained by a partial differential equation. Computational experiments indicate that a good separation in a two-component system can be achieved.

Keywords: Optimal control; PDE-constrained optimization; Mixed-integer programming; Chromatography; 34H05; 35Q93; 90C11 (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:oprchp:978-3-030-48439-2_10

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DOI: 10.1007/978-3-030-48439-2_10

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