Continuous cast width prediction using a data mining approach

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dc.contributor.advisor Craig, K.J. (Kenneth) en
dc.contributor.postgraduate De Beer, Petrus Gerhardus en
dc.date.accessioned 2013-09-07T15:06:32Z
dc.date.available 2007-12-19 en
dc.date.available 2013-09-07T15:06:32Z
dc.date.created 2007-04-20 en
dc.date.issued 2007-12-19 en
dc.date.submitted 2007-11-02 en
dc.description Dissertation (MEng (Mechanical Engineering))--University of Pretoria, 2007. en
dc.description.abstract In modern times continuous casting is the preferred way to convert molten steel into solid forms to enable further processing. At Columbus Stainless the continuous casting machine cast slabs of constant thickness with varying width. One important aspect of the continuously cast strand that must be controlled, is the strand width. The strand width exiting from the casting machine, has a direct influence on the product yield which in turn influences the profitability of the company. In general, the strand width control on the austentic and ferritic type steels achieved is excellent with the exception of the 12% chrome non stabilised ferritic steel. This steel type exhibited different strand width changes when a sequence of different heats was cast. The strand width changes corresponded to the different heats in the sequence. Each heat has a unique chemistry and a relationship between the austenite and ferrite fraction at high temperature and the resulting strand width change was explained by Siyasiya[27]. The relationship between the heat composition and width change has in the past resulted in the development of a model that enabled the prediction of the expected width change of a specific heat before it is cast to enable preventative action to be taken. This model has been implemented as an on-line prediction model in the production environment with very encouraging results. This study was initiated because it was uncertain if the implemented model was the most accurate for this application. This study is concerned with the development of more models based on different techniques in an attempt to implement a more accurate model. The data mining techniques used include statistical regression, decision trees and fuzzy logic. The results indicated that the existing model was the most accurate and it could not be improved upon. en
dc.description.availability unrestricted en
dc.description.degree MEng
dc.description.department Mechanical and Aeronautical Engineering en
dc.identifier.citation De Beer, PG 2007, Continuous cast width prediction using a data mining approach, MEng Dissertation, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/29189>
dc.identifier.other Pretoria en
dc.identifier.upetdurl http://upetd.up.ac.za/thesis/available/etd-11022007-132916/ en
dc.identifier.uri http://hdl.handle.net/2263/29189
dc.language.iso en
dc.publisher University of Pretoria en_ZA
dc.rights © University of Pretor en
dc.subject Stainless steel en
dc.subject Continuous casting en
dc.subject Statistical regression en
dc.subject Decision trees en
dc.subject Fuzzy logic en
dc.subject Rule based model en
dc.subject Width change en
dc.subject Strand width control en
dc.subject UCTD en_US
dc.title Continuous cast width prediction using a data mining approach en
dc.type Dissertation en


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