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An adjoint method in inverse problems of chromatography
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Engineering and Chemical Sciences (from 2013). Orebro Univ, Sch Sci & Technol, Dept Math, Orebro, Sweden.
Zhejiang Univ, Dept Math, Hangzhou, Zhejiang, Peoples R China..
Orebro Univ, Sch Sci & Technol, Dept Math, Orebro, Sweden..
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Engineering and Chemical Sciences (from 2013).
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2017 (English)In: Inverse Problems in Science and Engineering, ISSN 1741-5977, E-ISSN 1741-5985, Vol. 25, no 8, p. 1112-1137Article in journal (Refereed) Published
Abstract [en]

How to determine adsorption isotherms is an issue of significant importance in chromatography. A modern technique of obtaining adsorption isotherms is to solve an inverse problem so that the simulated batch separation coincides with actual experimental results. In this work, as well as the natural least-square approach, we consider a Kohn-Vogelius type formulation for the reconstruction of adsorption isotherms in chromatography, which converts the original boundary fitting problem into a domain fitting problem. Moreover, using the first momentum regularizing strategy, a new regularization algorithm for both the Equilibrium-Dispersive model and the Transport-Dispersive model is developed. The mass transfer resistance coefficients in the Transport-Dispersive model are also estimated by the proposed inverse method. The computation of the gradients of objective functions for both of the two models is derived by the adjoint method. Finally, numerical simulations for both a synthetic problem and a real-world problem are given to show the robustness of the proposed algorithm.

Place, publisher, year, edition, pages
Taylor & Francis, 2017. Vol. 25, no 8, p. 1112-1137
Keywords [en]
Chromatography, adsorption isotherm, inverse problem, regularization, convection-diffusion equation, adjoint method
National Category
Chemical Sciences
Research subject
Chemistry
Identifiers
URN: urn:nbn:se:kau:diva-65526DOI: 10.1080/17415977.2016.1222528ISI: 000401246500002OAI: oai:DiVA.org:kau-65526DiVA, id: diva2:1170814
Available from: 2018-01-04 Created: 2018-01-04 Last updated: 2020-05-25Bibliographically approved

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Zhang, YeForssén, PatrikFornstedt, Torgny

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