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An adjoint method in inverse problems of chromatography
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.ORCID iD: 0000-0003-0332-2315
Department of Engineering and Chemical Sciences, Karlstad University, Karlstad, Sweden.
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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 Group, 2017. Vol. 25, no 8, p. 1112-1137
Keywords [en]
Chromatography; adsorption isotherm; inverse problem; regularization; convection–diffusion equation; adjoint method
National Category
Mathematics
Research subject
Mathematics
Identifiers
URN: urn:nbn:se:oru:diva-51924DOI: 10.1080/17415977.2016.1222528ISI: 000401246500002Scopus ID: 2-s2.0-84983247185OAI: oai:DiVA.org:oru-51924DiVA, id: diva2:957062
Funder
Knowledge Foundation, 20140179Swedish Research Council, 2015-04627
Note

Funding Agencies:

AForsk Foundation  15/497

STINT  IB2015-5989 

NSCF  11571311 

Available from: 2016-08-31 Created: 2016-08-31 Last updated: 2017-10-18Bibliographically approved

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Zhang, YeGulliksson, Mårten

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