Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/4007
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dc.contributor.advisorDoymaz, Fuaten
dc.contributor.authorAvşar, Hakan-
dc.date.accessioned2014-07-22T13:52:57Z-
dc.date.available2014-07-22T13:52:57Z-
dc.date.issued2006en
dc.identifier.urihttp://hdl.handle.net/11147/4007-
dc.descriptionThesis (Master)--Izmir Institute of Technology, Chemical Engineering, Izmir, 2006en
dc.descriptionIncludes bibliographical references (leaves: 77-78)en
dc.descriptionText in English; Abstract: Turkish and Englishen
dc.descriptionxi, 89 leavesen
dc.description.abstractIn this study, artificial neural networks (ANN) and fuzzy logic models were developed to model relationship among cement mill operational parameters. The response variable was weight percentage of product residue on 32-micrometer sieve (or fineness), while the input parameters were revolution percent, falofon percentage, and the elevator amperage (amps), which exhibits elevator charge to the separator. The process data collected from a local plant, Cimenta Cement Factory, in 2004, were used in model construction and testing. First, ANN (Artificial Neural Network) model was constructed. A feed forward network type with one input layer including 3 input parameters, two hidden layer, and one output layer including residue percentage on 32 micrometer sieve as an output parameter was constructed. After testing the model, it was detected that the model.s ability to predict the residue on 32-micrometer sieve (fineness) was successful (Correlation coefficient is 0.92). By detailed analysis of values of parameters of ANN model.s contour plots, Mamdani type fuzzy rule set in the fuzzy model on MatLAB was created. There were three parameters and three levels, and then there were third power of three (27) rules. In this study, we constructed mix of Z type, S type and gaussian type membership functions of the input parameters and response. By help of fuzzy toolbox of MatLAB, the residue percentage on 32-micrometer sieve (fineness) was predicted. Finally, It was found that the model had a correlation coefficient of 0.76. The utility of the ANN and fuzzy models created in this study was in the potential ability of the process engineers to control processing parameters to accomplish the desired cement fineness levels. In the second part of the study, a quantitative procedure for monitoring and evaluating cement milling process performance was described. Some control charts such as CUSUM (Cumulative Sum) and EWMA (Exponentially Weighted Moving Average) charts were used to monitor the cement fineness by using historical data. As a result, it is found that CUSUM and EWMA control charts can be easily used in the cement milling process monitoring in order to detect small shifts in 32-micrometer fineness, percentage by weight, in shorter sampling time interval.en
dc.language.isoenen_US
dc.publisherIzmir Institute of Technologyen
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subject.lccTP883 .A94 2006en
dc.subject.lcshPortland cementen
dc.titleControl, optimization and monitoring of Portland cement (Pc 42.5) quality at the ball millen_US
dc.typeMaster Thesisen_US
dc.institutionauthorAvşar, Hakan-
dc.departmentThesis (Master)--İzmir Institute of Technology, Chemical Engineeringen_US
dc.relation.publicationcategoryTezen_US
item.openairetypeMaster Thesis-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextopen-
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.cerifentitytypePublications-
Appears in Collections:Master Degree / Yüksek Lisans Tezleri
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