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Continuous cast width prediction using a data mining approach

Dissertation (MEng (Mechanical Engineering))--University of Pretoria, 2007.

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Other Authors: Craig, K.J. (Kenneth)
Format: Thesis
Published: University of Pretoria 2013
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access_status_str Open Access
author2 Craig, K.J. (Kenneth)
author_browse Craig, K.J. (Kenneth)
author_facet Craig, K.J. (Kenneth)
collection Thesis
dc_rights_str_mv © University of Pretor
description Dissertation (MEng (Mechanical Engineering))--University of Pretoria, 2007.
format Thesis
id oai:repository.up.ac.za:2263/29189
institution University of Pretoria (South Africa)
last_indexed 2026-06-10T12:40:26.265Z
license_str Other — see source repository
provenance_str_mv Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository
publishDate 2013
publishDateRange 2013
publishDateSort 2013
publisher University of Pretoria
publisherStr University of Pretoria
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spelling oai:repository.up.ac.za:2263/29189 Continuous cast width prediction using a data mining approach Craig, K.J. (Kenneth) debeer.gerhard@columbus.co.za De Beer, Petrus Gerhardus Stainless steel Continuous casting Statistical regression Decision trees Fuzzy logic Rule based model Width change Strand width control UCTD Dissertation (MEng (Mechanical Engineering))--University of Pretoria, 2007. 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. Mechanical and Aeronautical Engineering MEng unrestricted 2013-09-07T15:06:32Z 2007-12-19 2013-09-07T15:06:32Z 2007-04-20 2007-12-19 2007-11-02 Dissertation 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> Pretoria http://hdl.handle.net/2263/29189 http://upetd.up.ac.za/thesis/available/etd-11022007-132916/ © University of Pretor application/pdf University of Pretoria
spellingShingle Stainless steel
Continuous casting
Statistical regression
Decision trees
Fuzzy logic
Rule based model
Width change
Strand width control
UCTD
Continuous cast width prediction using a data mining approach
title Continuous cast width prediction using a data mining approach
title_full Continuous cast width prediction using a data mining approach
title_fullStr Continuous cast width prediction using a data mining approach
title_full_unstemmed Continuous cast width prediction using a data mining approach
title_short Continuous cast width prediction using a data mining approach
title_sort continuous cast width prediction using a data mining approach
topic Stainless steel
Continuous casting
Statistical regression
Decision trees
Fuzzy logic
Rule based model
Width change
Strand width control
UCTD
url http://hdl.handle.net/2263/29189
http://upetd.up.ac.za/thesis/available/etd-11022007-132916/