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Using statistical modeling to predict the electrical energy consumption of an electric arc furnace producing stainless steel
KTH, School of Industrial Engineering and Management (ITM), Materials Science and Engineering.
KTH, School of Industrial Engineering and Management (ITM), Materials Science and Engineering.ORCID iD: 0000-0002-8802-4036
KTH, School of Industrial Engineering and Management (ITM), Materials Science and Engineering.ORCID iD: 0000-0001-9775-0382
2020 (English)In: Metals, ISSN 2075-4701, Vol. 10, no 1, article id 36Article in journal (Refereed) Published
Abstract [en]

The non-linearity of the Electric Arc Furnace (EAF) process and the correlative behavior between the process variables impose challenges that have to be considered if one aims to create a statistical model that is relevant and useful in practice. In this regard, both the statistical modeling framework and the statistical tools used in the modeling pipeline must be selected with the aim of handling these challenges. To achieve this, a non-linear statistical modeling framework known as Artificial Neural Networks (ANN) has been used to predict the Electrical Energy (EE) consumption of an EAF producing stainless steel. The statistical tools Feature Importance (FI), Distance Correlation (dCor) and Kolmogorov–Smirnov (KS) tests are applied to investigate the most influencing input variables as well as reasons behind model performance differences when predicting the EE consumption on future heats. The performance, measured as kWh per heat, of the best model was comparable to the performance of the best model reported in the literature while requiring substantially fewer input variables.

Place, publisher, year, edition, pages
MDPI AG , 2020. Vol. 10, no 1, article id 36
Keywords [en]
Electric Arc Furnace, Electrical energy consumption, Machine learning, Predictive modeling, Statistical modeling
National Category
Metallurgy and Metallic Materials
Identifiers
URN: urn:nbn:se:kth:diva-267855DOI: 10.3390/met10010036ISI: 000516827800036Scopus ID: 2-s2.0-85077311305OAI: oai:DiVA.org:kth-267855DiVA, id: diva2:1394571
Note

QC 20200219

Available from: 2020-02-19 Created: 2020-02-19 Last updated: 2020-05-02Bibliographically approved

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Carlsson, LeoSamuelsson, PeterJönsson, Pär G.

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