Multivariate Time Series for Data-Driven Endpoint Prediction in the Basic Oxygen Furnace

Davi Alberto Sala, Azarakhsh Jalalvand, Andy Van Yperen-De Deyne, Erik Mannens

Research output: Chapter in Book/Report/Conference proceedingConference contribution

21 Scopus citations

Abstract

Industrial processes are heavily instrumented by employing a large number of sensors, generating huge amounts of data. One goal of the Industry 4.0 era is to apply data-driven approaches to optimize such processes. At the basic oxygen furnace (BOF), molten iron is transformed into steel by lowering its carbon content and achieving a certain chemical endpoint. In this work, we propose a data-driven approach to predict the endpoint temperature and chemical concentration of phosphorus, manganese, sulfur and carbon at the basic oxygen furnace. The prediction is based on two distinct datasets. First, a collection of static features is used which represent a more classic data-driven solution. The second approach includes time-series data that provide a better estimate of the final endpoint and enable further tuning of the process parameters, if necessary. For both approaches, model-based feature selection is used to filter the most relevant information. Results obtained by both models are compared in order to estimate the added value of including the time series data analysis on the performance of the BOF process. Results show that a simple feature extraction approach can enhance the prediction for phosphorus, manganese and temperature.

Original languageEnglish (US)
Title of host publicationProceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018
EditorsM. Arif Wani, Mehmed Kantardzic, Moamar Sayed-Mouchaweh, Joao Gama, Edwin Lughofer
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1419-1426
Number of pages8
ISBN (Electronic)9781538668047
DOIs
StatePublished - Jul 2 2018
Externally publishedYes
Event17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018 - Orlando, United States
Duration: Dec 17 2018Dec 20 2018

Publication series

NameProceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018

Conference

Conference17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018
Country/TerritoryUnited States
CityOrlando
Period12/17/1812/20/18

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Safety, Risk, Reliability and Quality
  • Signal Processing
  • Decision Sciences (miscellaneous)

Keywords

  • Basic oxygen furnace
  • Prediction
  • Sensors
  • Steel industry
  • Time-series data analysis

Fingerprint

Dive into the research topics of 'Multivariate Time Series for Data-Driven Endpoint Prediction in the Basic Oxygen Furnace'. Together they form a unique fingerprint.

Cite this