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Prediction of raw material batches for the production of clinker by means of artificial neural networks-Analysis of behaviour

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dc.title Prediction of raw material batches for the production of clinker by means of artificial neural networks-Analysis of behaviour en
dc.contributor.author Komínková Oplatková, Zuzana
dc.contributor.author Šenkeřík, Roman
dc.relation.ispartof Proceedings - 29th European Conference on Modelling and Simulation, ECMS 2015
dc.identifier.isbn 9780993244001
dc.date.issued 2015
dc.citation.spage 570
dc.citation.epage 575
dc.event.title 29th European Conference on Modelling and Simulation, ECMS 2015
dc.event.location Varna
utb.event.state-en Bulgaria
utb.event.state-cs Bulharsko
dc.event.sdate 2015-05-26
dc.event.edate 2015-05-29
dc.type conferenceObject
dc.language.iso en
dc.publisher European Council for Modelling and Simulation (ECMS)
dc.identifier.doi 10.7148/2015-0570
dc.relation.uri http://www.scs-europe.net/dlib/2015/2015-0570.htm
dc.relation.uri http://www.scs-europe.net/dlib/2015/ecms2015acceptedpapers/0570-is_ECMS2015_0123.pdf
dc.subject Artificial neural networks en
dc.subject Cement en
dc.subject Chemical composition en
dc.description.abstract This research deals with the analysis of the behaviour of artificial neural nets for prediction of raw material batches for the production of clinker. During the production several oxides that are present in raw materials in quarries have to be extracted for homogenization of the mixture suitable for clinker production. There is some delay between the measurement of the mixture and the material which is send from quarry. It is necessary to "send" precise chemical composition to ensure a good quality of clinker and resulting product-cement. Artificial neural networks (ANN) are suitable for such kind of time-independent prediction. The results show that not all oxides are necessary to use for the prediction of one oxide. The ANN were designed into several nets with one input similarly as pseudo neural networks are able to work. The results will be used for the purpose of further research of pseudo neural nets which currently serve only as classifiers. © ECMS Valen M. Mladenov, Petia Georgieva, Grisha Spasov, Galidiya Petrova (Editors). en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1005305
utb.identifier.obdid 43874511
utb.identifier.scopus 2-s2.0-84938676889
utb.source d-scopus
dc.date.accessioned 2015-09-09T15:16:35Z
dc.date.available 2015-09-09T15:16:35Z
utb.contributor.internalauthor Komínková Oplatková, Zuzana
utb.contributor.internalauthor Šenkeřík, Roman
utb.fulltext.affiliation Zuzana Kominkova Oplatkova, Roman Senkerik Tomas Bata University in Zlin, Faculty of Applied Informatics Nam T.G. Masaryka 5555, 760 01 Zlin, Czech Republic {oplatkova, senkerik}@fai.utb.cz
utb.fulltext.dates -
utb.fulltext.faculty Faculty of Applied Informatics
utb.fulltext.faculty Faculty of Applied Informatics
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