Machine learning-based prediction of a BOS reactor performance from operating parameters

Alireza Rahnama, Zushu Li, Seetharaman Sridhar

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

A machine learning-based analysis was applied to process data obtained from a Basic Oxygen Steelmaking (BOS) pilot plant. The first purpose was to identify correlations between operating parameters and reactor performance, defined as rate of decarburization (dc/dt). Correlation analysis showed, as expected a strong positive correlation between the rate of decarburization (dc/dt) and total oxygen flow. On the other hand, the decarburization rate exhibited a negative correlation with lance height. Less obviously, the decarburization rate, also showed a positive correlation with temperature of the waste gas and CO2 content in the waste gas. The second purpose was to train the pilot-plant dataset and develop a neural network based regression to predict the decarburization rate. This was used to predict the decarburization rate in a BOS furnace in an actual manufacturing plant based on lance height and total oxygen flow. The performance was satisfactory with a coefficient of determination of 0.98, confirming that the trained model can adequately predict the variation in the decarburization rate (dc/dt) within BOS reactors.

Original languageEnglish (US)
Article number371
JournalProcesses
Volume8
Issue number3
DOIs
StatePublished - Mar 1 2020
Externally publishedYes

Keywords

  • Artificial intelligence
  • BOS reactor
  • Machine learning
  • Neural network
  • Steelmaking

ASJC Scopus subject areas

  • Bioengineering
  • Chemical Engineering (miscellaneous)
  • Process Chemistry and Technology

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