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
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Publish Date: | 2020-05-06 |
Description: |
AbstractSulfur 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. see all
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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. |