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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

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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
Subjects: Q Science > QA Mathematics
T Technology > TS Manufactures
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
Dates:
DateEvent
January 2021Published
18 September 2020Available
4 September 2020Accepted
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:
Project/Grant IDRIOXX Funder NameFunder ID
EP/R51214X/1[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
UNSPECIFIEDTata Steelhttp://dx.doi.org/10.13039/501100007220
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