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master's thesis
Razvoj modela softverskih senzora primjenom "bootstrapping" metode
Zagreb: University of Zagreb, Faculty of Chemical Engineering and Technology, 2015. urn:nbn:hr:149:102823
Cvetnić, Matija
University of Zagreb
Faculty of Chemical Engineering and Technology

Institutional repository: Repository of Faculty of Chemical Engineering and Technology University of Zagreb

Cite this document

Cvetnić, M. (2015). Razvoj modela softverskih senzora primjenom "bootstrapping" metode (Master's thesis). Zagreb: University of Zagreb, Faculty of Chemical Engineering and Technology. Retrieved from https://urn.nsk.hr/urn:nbn:hr:149:102823

Cvetnić, Matija. "Razvoj modela softverskih senzora primjenom "bootstrapping" metode." Master's thesis, University of Zagreb, Faculty of Chemical Engineering and Technology, 2015. https://urn.nsk.hr/urn:nbn:hr:149:102823

Cvetnić, Matija. "Razvoj modela softverskih senzora primjenom "bootstrapping" metode." Master's thesis, University of Zagreb, Faculty of Chemical Engineering and Technology, 2015. https://urn.nsk.hr/urn:nbn:hr:149:102823

Cvetnić, M. (2015). 'Razvoj modela softverskih senzora primjenom "bootstrapping" metode', Master's thesis, University of Zagreb, Faculty of Chemical Engineering and Technology, accessed 27 December 2024, https://urn.nsk.hr/urn:nbn:hr:149:102823

Cvetnić M. Razvoj modela softverskih senzora primjenom "bootstrapping" metode [Master's thesis]. Zagreb: University of Zagreb, Faculty of Chemical Engineering and Technology; 2015 [cited 2024 December 27] Available at: https://urn.nsk.hr/urn:nbn:hr:149:102823

M. Cvetnić, "Razvoj modela softverskih senzora primjenom "bootstrapping" metode", Master's thesis, University of Zagreb, Faculty of Chemical Engineering and Technology, Zagreb, 2015. Available at: https://urn.nsk.hr/urn:nbn:hr:149:102823

Metadata
TitleRazvoj modela softverskih senzora primjenom "bootstrapping" metode
Title (english)Soft sensor model development based on bootstrapping method
AuthorMatija Cvetnić
MentorNenad Bolf (mentor)
Committee memberNenad Bolf (predsjednik povjerenstva)
Committee memberIgor Dejanović (član povjerenstva)
Committee memberŽeljka Ujević Andrijić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Chemical Engineering and Technology
Zagreb
Defense date and country2015-07-20, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Chemical Engineering
Analysis, Synthesis and Management of Chemical Processes
Abstract
U rafinerijskim postrojenjima ključne procesne veličine kao što su sastavi procesnih struja i svojstva proizvoda mjere se pomoću procesnih analizatora. Budući da su procesni analizatori često nedostupni, ključna svojstva moraju se određivati rijetkim i dugotrajnim laboratorijskim analizama. U radu je prikazan razvoj modela softverskih senzora za procjenu sadržaja benzena u lakom reformatu. Prikazana je usporedba više modela softverskih senzora razvijenih na temelju malog skupa eksperimentalnih podataka. Softverski senzori razvijeni su primjenom viševeličinske linearne regresije, viševeličinske adaptivne regresije (MARSpline) te primjenom neuronskih mreža. Svi razvijeni modeli pokazali su dobro vladanje na skupu podataka za vrednovanje modela uz visoke korelacijske faktore i male pogreške modela. Kao najbolji modeli među razvijenima pokazale su se neuronske mreže. Nešto lošije, ali i dalje zadovoljavajuće vladanje dali su modeli viševeličinske adaptivne regresije te linearni model. Linearni viševeličinski model razvijen na generiranim podacima daje donekle lošije rezultate uz zadovoljavajuće vladanje pri određenim procesnim uvjetima.
Abstract (english)
In refinery plants, key process variables like contents of process stream and fuel properties needs to be continuously monitored using adequate measuring devices. Measuring devices are often unavailable or malfunction, and laboratory assays, which are irregular and therefore not suitable for process control, are inevitable alternative. In this study, soft sensors for the estimation of benzene content of light reformate are developed. This research shows a comparison of soft sensor models developed on the basis of a small data sets. Soft sensors have been developed using multivariable linear regression, multivariable adaptive regression splines (MARSpline) and neural networks. Within the validation data set, all models have shown good results with high correlation factors and small errors. Best results are achieved by neural network-based models. Models developed on small data set by multivariable adaptive regression splines and by multivariable linear regression are satisfactory and still applicable for benzene content estimation while multivariable linear regression developed on resampled data set showed slightly worse behavior on particular data range.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:149:102823
Study programmeTitle: Chemical Engineering - Graduate study
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka kemijskog inženjerstva (magistar/magistra inženjer/inženjerka kemijskog inženjerstva)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2017-03-06 10:59:21