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Title:Use of genetic algorithm for fitting Sovova's mass transfer model
Authors:ID Hrnčič, Dejan (Author)
ID Mernik, Marjan (Author)
ID Knez Marevci, Maša (Author)
Files:.pdf Acta_Chimica_Slovenica_2010_Hrncic,_Mernik,_Hrncic_Knez_Use_of_Genetic_Algorithm_for_Fitting_Sovova_’s_Mass_Transfer_Model.pdf (718,52 KB)
MD5: 69D6E32B857444B8105DC483F40E70A9
PID: 20.500.12556/dkum/cb789ec0-6fba-4c85-ab51-2afb81335e2d
 
URL http://acta-arhiv.chem-soc.si/57/57-4-788.pdf
 
Language:English
Work type:Scientific work
Typology:1.01 - Original Scientific Article
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:A genetic algorithm with resizable population has been applied to the estimation of parameters for Sovovaćs mass transfer model. The comparison of results between a genetic algorithm and a global optimizer from the literatureshows that a genetic algorithm performs as good as or better than a global optimizer on a given set of problems. Other benefits of the genetic algorithm, for mass transfer modeling, are simplicity, robustness and efficiency.
Keywords:Sovova's mass transfer model, genetic algorithm, parameter estimation
Publication status:Published
Publication version:Version of Record
Year of publishing:2010
Number of pages:str. 788-797
Numbering:Letn. 57, št. 4
PID:20.500.12556/DKUM-26701 New window
ISSN:1318-0207
UDC:66
ISSN on article:1318-0207
COBISS.SI-ID:14648598 New window
NUK URN:URN:SI:UM:DK:429SCJAR
Publication date in DKUM:31.05.2012
Views:2034
Downloads:108
Metadata:XML DC-XML DC-RDF
Categories:Misc.
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Record is a part of a journal

Title:Acta Chimica Slovenica
Shortened title:Acta Chim. Slov.
Publisher:Slovensko kemijsko društvo
ISSN:1318-0207
COBISS.SI-ID:14086149 New window

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:31.05.2012

Secondary language

Language:Slovenian
Abstract:V članku predstavimo genetski algoritem s spremenljivo populacijo za določitev parametrov modela prenosa snovi Sovova. Primerjava rezultatov med genetskim algoritmom in globalnim optimizacijskim algoritmom povzetim iz literature pokaže, da so rezultati genetskega algoritma prav tako dobri ali boljši od rezultatov globalnega optimizacijskega algoritma na dani množici problemov. Ostale prednosti genetskega algoritma za modeliranje krivulje prenosa snovi so preprostost, robustnost in učinkovitost.
Keywords:genetski algoritmi, parameterska estimacija, Sovovov model


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