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Multivariate process monitoring of EAFs
RISE, Swerea, Swerea MEFOS.
University of Manchester.
Perceptive Engineering Ltd..
Perceptive Engineering Ltd..
2005 (English)In: Ironmaking and Steelmaking, 2005, Vol. 32, no 3, p. 221-225Conference paper, Published paper (Refereed)
Abstract [en]

The application of a multivariate statistical process control (MSPC) to an EAF and the benefits that can be delivered was discussed. Several statistical methods for multivariate prediction were tested such as multiple linear regression (MLR), principal component regression (PCR) and partial least squares (PLS). The results show that PLS was the most suitable of the tested methods and the prediction accuracy for tramp elements and alloying elements were satisfactory for online predictions and condition monitoring of scrap properties. Monitoring of short and long term variations in scrap quality was possible by analysis of the prediction errors and regression coefficients.

Place, publisher, year, edition, pages
2005. Vol. 32, no 3, p. 221-225
Series
Ironmaking and Steelmaking, ISSN 0301-9233
National Category
Materials Engineering
Identifiers
URN: urn:nbn:se:ri:diva-13507DOI: 10.1179/174328105X45884Scopus ID: 2-s2.0-22944483448OAI: oai:DiVA.org:ri-13507DiVA, id: diva2:973716
Available from: 2016-09-22 Created: 2016-09-22Bibliographically approved

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