University of Oulu

Vuolio, T., Visuri, V., Sorsa, A., Paananen, T. and Fabritius, T. (2019), Genetic Algorithm‐Based Variable Selection in Prediction of Hot Metal Desulfurization Kinetics. Steel Research Int., 90: 1900090. doi:10.1002/srin.201900090

Genetic algorithm‐based variable selection in prediction of hot metal desulfurization kinetics

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Author: Vuolio, Tero1; Visuri, Ville-Valtteri1; Sorsa, Aki2;
Organizations: 1Process Metallurgy Research Unit, University of Oulu, P.O. Box 4300, FI–90014, University of Oulu, Finland
2Control Engineering, University of Oulu, P.O. Box 4300, FI–90014, University of Oulu, Finland
3SSAB Europe Oy, Rautaruukintie 155, P.O. Box 93, FI–92101, Raahe, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.8 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2019112644291
Language: English
Published: John Wiley & Sons, 2019
Publish Date: 2020-05-06
Description:

Abstract

Sulfur is considered as one of the main impurities in hot metal and hot metal desulfurization is often carried out using injection of fine‐grade desulfurization reagent. The selection of variables used for predicting the course of hot metal desulphurization requires expert knowledge. However, it is difficult to model the complex interactions in the process and to evaluate a high number of possible variable subsets with manual variable selection techniques. As the amount of data gathered from the process increases, manual variable selection becomes too time‐consuming and might lead to a suboptimal prediction model. The objective of this work is to execute an automatic variable selection procedure for prediction of hot metal desulfurization based on an industrial scale data set. The variable selection problem is formulated as a constrained optimization problem, in which the objective function is formulated based on repeated leave‐multiple‐out cross‐validation. The implemented solution strategy is a binary‐coded genetic algorithm (GA). By making use of the developed model, the effect of the main production variables on the rate and efficiency of primary hot metal desulfurization is quantified. The variables related to properties of the reagent and the injection parameters were found to be of great importance.

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Series: Steel research international
ISSN: 1611-3683
ISSN-E: 1869-344X
ISSN-L: 1611-3683
Volume: 90
Issue: 8
Article number: 1900090
DOI: 10.1002/srin.201900090
OADOI: https://oadoi.org/10.1002/srin.201900090
Type of Publication: A1 Journal article – refereed
Field of Science: 215 Chemical engineering
216 Materials engineering
Subjects:
Copyright information: © 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. This is the peer reviewed version of the following article: Vuolio, T. , Visuri, V. , Sorsa, A. , Paananen, T. and Fabritius, T. (2019), Genetic Algorithm‐Based Variable Selection in Prediction of Hot Metal Desulfurization Kinetics. steel research int., 90: 1900090, which has been published in final form at https://doi.org/10.1002/srin.201900090. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.