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A guided analytics tool for feature selection in steel manufacturing with an application to blast furnace top gas efficiency
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Stein, Stefan, Leng, Chenlei, Thornton, Steven and Randrianandrasana, Michel (2021) A guided analytics tool for feature selection in steel manufacturing with an application to blast furnace top gas efficiency. Computational Materials Science, 186 . 110053. doi:10.1016/j.commatsci.2020.110053
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Official URL: https://doi.org/10.1016/j.commatsci.2020.110053
Abstract
In knowledge intensive industries such as steel manufacturing, application of data analytics to optimise process performance, requires effective knowledge transfer between domain experts and data scientists. This is often an inefficient path to follow, requiring much iteration whilst being suboptimal with regard to organisational knowledge capture for the long term. With the ‘initial Guided Analytics for parameter Testing and controlband Extraction (iGATE)’ tool we created a feature selection framework that finds influential process parameters and their optimal control bands and which can easily be made available to process operators in the form of guided analytics tool, while allowing them to modify the analysis according to their expertise. The method is embedded in a work flow whereby the extracted parameters and control bands are verified by the domain expert and a report of the analysis is automatically generated. The approach allows us to combine the power of suitable statistical analysis with process-expertise, whilst dramatically reducing the time needed for conducting the feature selection. We regard this application as a stepping stone to gain user confidence in advance of introduction of more autonomous analytics approaches. We present the statistical foundations of iGATE and illustrate its effectiveness in the form of a case study of Tata Steel blast furnace data. We have made the iGATE core functionality freely available in the igate package for the R programming language.
| Item Type: | Journal Article | |||||||||
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| Subjects: | Q Science > QA Mathematics T Technology > TS Manufactures |
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| Divisions: | Faculty of Science > Statistics | |||||||||
| Library of Congress Subject Headings (LCSH): | Steel-works , Steel-works -- Simulation methods, Process control -- Statistical methods, Mathematical optimization | |||||||||
| Journal or Publication Title: | Computational Materials Science | |||||||||
| Publisher: | Elsevier Science BV | |||||||||
| ISSN: | 0927-0256 | |||||||||
| Official Date: | January 2021 | |||||||||
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| Date of first compliant deposit: | 16 September 2020 | |||||||||
| Volume: | 186 | |||||||||
| Article Number: | 110053 | |||||||||
| DOI: | 10.1016/j.commatsci.2020.110053 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Access rights to Published version: | Restricted or Subscription Access | |||||||||
| RIOXX Funder/Project Grant: |
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