Multivariate Analysis of the Main Operational Variables Involved in Steel Producing on BOF Using Time Series Tools


  • Luccas Klotz University of São Paulo, Brazil
  • Guilherme Lenz University of São Paulo, Brazil
  • Natalia Antoniassi University of São Paulo, Brazil
  • Ronaldo Borges University of São Paulo, Brazil
  • Thales Nunes University of São Paulo, Brazil



BOF, Time series analysis, AI, Modeling


There is significant interest in accurately modeling the operational variables of the steel-making process in LD converters. Despite this, the task is challenging due to the complex interactions between process variables, which are not entirely comprehended. Often, decisions in the industry are grounded in experience. This study aims to introduce a robust model that can effectively guide engineers and technicians by forecasting the future behavior of steelmaking variables in the BOF furnace. We employed multivariate time series analysis to reach this goal, utilizing tools like Vector Autoregression models, ElasticNet, K-Nearest-Neighbors, Multiple Linear Regression, and Long Short-Term Memory Neural networks. These models were tested on data from three distinct steel production campaigns. A successful model was identified, predicting 35 out of the 42 chosen variables, demonstrating the potential to correlate a majority of the selected parameters.


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How to Cite

Klotz, Luccas, Guilherme Lenz, Natalia Antoniassi, Ronaldo Borges, and Thales Nunes. 2023. “Multivariate Analysis of the Main Operational Variables Involved in Steel Producing on BOF Using Time Series Tools”. Metallurgical and Materials Engineering 30 (1):70-80.



Modeling and simulation in metallurgical and materials engineering