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Enhancing chromatographic resolution via smoothing-based global optimization and neural surrogate PDE modeling Full article

Journal Journal of Inverse and Ill-Posed Problems
ISSN: 0928-0219 , E-ISSN: 1569-3945
Output data Year: 2026, Pages: 1-30 Pages count : 30 DOI: 10.1515/jiip-2025-0083
Tags Preparative chromatography; chromatographic resolution; parameter estimation; neural network surrogate; gaussian smoothing; global optimization
Authors Sun You 1 , Kabanikhin Sergey I. 2,5 , Xu Chen 3 , Zhang Ye 4,5
Affiliations
1 School of Mathematics and Statistics , Beijing Institute of Technology , Beijing 100081 , P. R. China
2 Sobolev Institute of Mathematics, Siberian Branch , Russian Academy of Sciences , Novosibirsk , Russian Federation ;
3 Department of Engineering , Shenzhen MSU-BIT University , Shenzhen 518172 , P. R. China
4 School of Mathematics and Statistics , Beijing Institute of Technology , Beijing 100081;
5 MSU-BIT-SMBU Joint Research Center of Applied Mathematics, Shenzhen MSU-BIT University, Shenzhen 518172 , P. R. China

Abstract: Chromatographic resolution is an issue of significant importance in preparative chromatography. However, due to the high-dimensional parameter space and nonlinear behavior, baseline separation in multi-component preparative chromatography remains challenging. To overcome these difficulties, we propose a surrogate-assisted optimizer focused on chromatographic resolution that uses a feedforward neural network to emulate PDE-based outlet responses. Based on this surrogate model, we employ the global optimization method of GS-PowerOpt to reshape the nonconvex landscape and robustly identify high-resolution designs. We prove the continuity of the resolution function and establish guarantees for convergence in the smoothed search. Through several case studies, the method consistently identifies designs with well-separated peaks and high resolution. The results offer a practical and theoretically grounded route to effective method development.
Cite: Sun Y. , Kabanikhin S.I. , Xu C. , Zhang Y.
Enhancing chromatographic resolution via smoothing-based global optimization and neural surrogate PDE modeling
Journal of Inverse and Ill-Posed Problems. 2026. P.1-30. DOI: 10.1515/jiip-2025-0083 WOS Scopus OpenAlex
Dates:
Submitted: Oct 20, 2025
Accepted: Mar 29, 2026
Published online: Jul 30, 2026
Identifiers:
≡ Web of science: WOS:001834695300001
≡ Scopus: 2-s2.0-105046176182
≡ OpenAlex: W7171725052
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