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A regularizing Kohn–Vogelius formulation forthe model-free adsorption isotherm estimationproblem in chromatography
Department of Mathematics, Zhejiang University, Hangzhou, China.
Örebro University, School of Science and Technology. Department of Engineering and Chemical Sciences, Karlstad University, Karlstad, Sweden. (Mathematics)ORCID iD: 0000-0003-4023-6352
Department of Mathematics, Zhejiang University, Hangzhou, China.
Örebro University, School of Science and Technology. (Mathematics)ORCID iD: 0000-0003-0332-2315
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2017 (English)In: Applicable Analysis, ISSN 0003-6811, E-ISSN 1563-504XArticle in journal (Refereed) Epub ahead of print
Abstract [en]

Competitive adsorption isotherms must be estimated in order to simulate and optimize modern continuous modes of chromatography in situations where experimental trial-and-error approaches are too complex and expensive. The inverse method is a numeric approach for the fast estimation of adsorption isotherms directly from overloaded elution profiles. However, this identification process is usually ill-posed. Moreover, traditional model-based inverse methods are restricted by the need to choose an appropriate adsorption isotherm model prior to estimate, which might be very hard for complicated adsorption behavior. In this study, we develop a Kohn–Vogelius formulation for the model-free adsorption isotherm estimation problem. The solvability and convergence for the proposed inverse method are studied. In particular, using a problem-adapted adjoint, we obtain a convergence rate under substantially weaker and more realistic conditions than are required by the general theory. Based on the adjoint technique, a numerical algorithm for solving the proposed optimization problem is developed. Numerical tests for both synthetic and real-world problems are given to show the efficiency of the proposed regularization method.

Place, publisher, year, edition, pages
Taylor & Francis Group, 2017.
Keyword [en]
Chromatography; adsorption isotherm; inverse problem; Kohn–Vogelius method; convergence rate
National Category
Computational Mathematics
Research subject
Mathematics
Identifiers
URN: urn:nbn:se:oru:diva-55172DOI: 10.1080/00036811.2017.1284311OAI: oai:DiVA.org:oru-55172DiVA: diva2:1070512
Available from: 2017-02-01 Created: 2017-02-01 Last updated: 2017-10-18Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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