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BOF Process Control and Slopping Prediction Based on Multivariate Data Analysis
RISE, Swerea, MEFOS.ORCID iD: 0000-0003-0844-2201
Luleå University of Technology, Sweden.
Luleå University of Technology, Sweden.
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]

In 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. Multivariate data analysis (MVDA) methods have been applied on the top-blown BOF steelmaking process, with the main aim to create industrially applicable static (i.e., prior to blow), as well as dynamic in-blow batch models for predicting the slopping probability. The MVDA approach has also been investigated in regard to in-blow prediction of end-of-blow phosphorous content.

Place, publisher, year, edition, pages
Wiley-VCH Verlag , 2016. Vol. 87, no 3, p. 301-310
Keywords [en]
BOF steelmaking, multivariate data analysis, phosphorous prediction, slopping, static and dynamic control, Batch data processing, Data handling, Forecasting, Information analysis, Multivariant analysis, Phosphorus, Steel metallurgy, Steelmaking, Control parameters, Dynamic controls, Prediction-based, Process control devices, Process Variables, Process control
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Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-41095DOI: 10.1002/srin.201500040Scopus ID: 2-s2.0-84959561270OAI: oai:DiVA.org:ri-41095DiVA, id: diva2:1377202
Available from: 2019-12-11 Created: 2019-12-11 Last updated: 2019-12-11Bibliographically approved

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