Reducing model complexity by means of the optimal scaling: Population balance model for latex particles morphology formation

作     者：Rusconi, Simone Schenk, Christina Zarnescu, Arghir Akhmatskaya, Elena 

作者机构：BCAM Basque Ctr Appl Math Alameda Mazarredo 14 Bilbao 48009 Spain CUNEF Univ C Pirineos 55 Madrid 28040 Spain IMDEA Mat Inst C Eric Kandel 2Tecnogetafe Getafe 28906 Madrid Spain Basque Fdn Sci IKERBASQUE Plaza Euskadi 5 Bilbao 48009 Spain Romanian Acad Simion Stoilow Inst 21 Calea Grivitei Bucharest 010702 Romania 

出 版 物：《APPLIED MATHEMATICS AND COMPUTATION》 (应用数学和计算)

年 卷 期：2023年第443卷

核心收录：

学科分类：07[理学] 070104[理学-应用数学] 0701[理学-数学] 

基　　金：Ministerio de Economia y Competitividad (MINECO) of the Spanish Government through BCAM Severo Ochoa accreditation - AEI/FEDER, UE [SEV-2017-0718, PID2019-104927GB-C22, PID2020-114189RB-I00] BERC 2022-2025 Program ELKARTEK Programme - Basque Government [KK-2021/00022, KK-2021/00064, KK-2022/00006K] ERDF ESF Vinnova [2021-00022, 2021-00064] Funding Source: Vinnova 

主　　题：Polymerization Latex particles morphology formation Population balance equation model Nondimensionalization Reduction of model complexity Optimal scaling with constraints 

摘      要：Rational computer-aided design of multiphase polymer materials is vital for rapid progress in many important applications, such as: diagnostic tests, drug delivery, coatings, additives for constructing materials, cosmetics, etc. Several property predictive models, including the prospective Population Balance Model for Latex Particles Morphology Formation (LPMF PBM), have already been developed for such materials. However, they lack computational efficiency, and the accurate prediction of materials properties still remains a great challenge. To enhance performance of the LPMF PBM, we explore the feasibility of reducing its complexity through disregard of the aggregation terms of the model. The introduced nondimensionalization approach, which we call Optimal Scaling with Constraints, suggests a quantitative criterion for locating regions of slow and fast aggregation and helps to derive a family of dimensionless LPMF PBM of reduced complexity. The mathematical analysis of this new family is also provided. When compared with the original LPMF PBM, the resulting models demonstrate several orders of magnitude better computational efficiency. (c) 2022 The Authors. Published by Elsevier *** is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/)