Enhancing chromatographic resolution via smoothing-based global optimization and neural surrogate PDE modeling | Sciact - Система учета научной деятельности ИМ СО РАН

Enhancing chromatographic resolution via smoothing-based global optimization and neural surrogate PDE modeling Научная публикация

Журнал

Journal of Inverse and Ill-Posed Problems
ISSN: 0928-0219 , E-ISSN: 1569-3945

Вых. Данные

Год: 2026, Страницы: 1-30 Страниц : 30 DOI: 10.1515/jiip-2025-0083

Ключевые слова

Preparative chromatography; chromatographic resolution; parameter estimation; neural network surrogate; gaussian smoothing; global optimization

Авторы

Sun You ¹ , Kabanikhin Sergey I. ^2,5 , Xu Chen ³ , Zhang Ye ^4,5

Организации

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

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.

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

Поступила в редакцию:	20 окт. 2025 г.
Принята к публикации:	29 мар. 2026 г.
Опубликована online:	30 июл. 2026 г.

≡ Web of science:	WOS:001834695300001
≡ Scopus:	2-s2.0-105046176182
≡ OpenAlex:	W7171725052