Publisher DOI: 10.1016/j.compchemeng.2017.03.018
Title: Estimation of aggregation kernels based on Laurent polynomial approximation
Language: English
Authors: Eisenschmidt, Holger 
Soumaya, M. 
Bajcinca, N. 
Le Borne, Sabine  
Sundmacher, Kai 
Keywords: Aggregation;Aggregation kernel;Inverse methods;Polynomial approximation
Issue Date: 2017
Source: Computers and Chemical Engineering (103): 210-217 (2017)
Abstract (english): 
The dynamics of particulate processes can be described by population balance equations which are governed by the phenomena of growth, nucleation, aggregation and breakage. Estimating the kinetics of the latter phenomena is a major challenge particularly for particle aggregation because first principle models are rarely available and the kernel estimation from measured population density data constitutes an ill-conditioned problem. In this work we demonstrate the estimation of aggregation kernels from experimental data using an inverse problem approach. This approach is based on the approximation of the aggregation kernel by use of Laurent polynomials. We show that the aggregation kernel can be well estimated from in silico data and that the estimation results are robust against substantial measurement noise. The method is demonstrated for three different aggregation kernels. Good agreement between true and estimated kernels was found in all investigated cases.
ISSN: 0098-1354
Institute: Mathematik E-10 
Document Type: Article
Project: SPP 1679: Teilprojekt "Numerische Lösungsverfahren für gekoppelte Populationsbilanzsysteme zur dynamischen Simulation multivariater Feststoffprozesse am Beispiel der formselektiven Kristallisation" 
Journal: Computers & chemical engineering 
Appears in Collections:Publications without fulltext

Show full item record

Page view(s)

Last Week
Last month
checked on Jun 27, 2022


Last Week
Last month
checked on Jun 22, 2022

Google ScholarTM


Add Files to Item

Note about this record

Cite this record


Items in TORE are protected by copyright, with all rights reserved, unless otherwise indicated.