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

Simone Rusconi, Christina Schenk, Arghir Zarnescu and Elena Akhmatskaya

Applied Mathematics and Computation, 2023, vol. 443, issue C

Abstract: 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.

Keywords: Polymerization; Latex particles morphology formation; Population balance equation model; Nondimensionalization; Reduction of model complexity; Optimal scaling with constraints (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:443:y:2023:i:c:s0096300322008244

DOI: 10.1016/j.amc.2022.127756

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