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BOF Process Control and Slopping Prediction Based on Multivariate Data Analysis
Department of Process Integration, Swerea MEFOS AB.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Minerals and Metallurgical Engineering.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Minerals and Metallurgical Engineering.ORCID iD: 0000-0003-1511-8020
2016 (English)In: Steel Research International, ISSN 1611-3683, E-ISSN 1869-344X, Vol. 87, no 3, p. 301-310Article in journal (Refereed) Published
Abstract [en]

A complex industrial batch processes such as the top-blown BOF steelmaking process, it is a complicated task to monitor and act on the progress of several important control parameters in order to avoid an undesired process event such as “slopping” and to secure a successful batch completion such as a sufficiently low steel phosphorous content. It would, therefore, be of much help to have an automated tool, which simultaneously can interpret a large number of process variables, with the function to warn of any imminent deviation from the normal batch evolution and to predict the batch end result. One way to compute, interpret, and visualize this “batch evolution” is to apply multivariate data analysis (MVDA). At SSAB Europe's steel plant in Luleå, new BOF process control devices are installed with the purpose to investigate the possibility for developing a dynamic system for slopping prediction. A main feature of this system is steelmaking vessel vibration measurements and audiometry to estimate foam height. This paper describes and discusses the usefulness of the MVDA approach for static and dynamic slopping prediction, as well as for end-of-blow phosphorous content prediction.

Place, publisher, year, edition, pages
2016. Vol. 87, no 3, p. 301-310
National Category
Metallurgy and Metallic Materials
Research subject
Process Metallurgy
Identifiers
URN: urn:nbn:se:ltu:diva-13778DOI: 10.1002/srin.201500040ISI: 000371504300004Scopus ID: 2-s2.0-84959561270Local ID: d11cbc90-daaf-419f-80a8-fa42274ae625OAI: oai:DiVA.org:ltu-13778DiVA, id: diva2:986731
Note
Validerad; 2016; Nivå 2; 20150629 (andbra)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved

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Björkman, BoSamuelsson, Caisa

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