Title
Inference of Kinetics in Population Balance Models using Gaussian Process Regression
Author
Michiel Busschaert
Department of Chemical Engineering, KU Leuven
Abstract
Population balance models are used to describe systems composed of individual entities dispersed in a continuous phase. Identification of system dynamics is an essential yet difficult step in the modeling of population systems. In this paper, Gaussian processes are utilized to infer kinetics of a population model, including interaction with a continuous phase, from measurements via non-parametric regression. Under a few conditions, it is shown that the population kinetics in the process model can be estimated from the moment dynamics, rather than the entire population distribution. The method is illustrated with a numerical case study regarding crystallization, in order to infer growth and nucleation rates from varying noise-induced simulation data.
Keywords
Population balance modelingGaussian process regressionCrystallizationSystems identificationMoment dynamics
Object type
Language
English [eng]
Persistent identifier
phaidra.univie.ac.at/o:1681655
Appeared in
Title
IFAC-PapersOnLine
Volume
55
Issue
7
ISSN
2405-8963
Issued
2022
From page
384
To page
391
Publication
Elsevier BV
Version type
Date issued
2022
Access rights
Rights statement
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