Improving steel making off-gas predictions by mixing classification and regression multi-modal multivariate models

  • Marcelo Magalhães do Carmo IFES
  • Filipe W. Mutz IFES
  • Leandro C. Resendo IFES


This paper addresses the problem of real-time short-term multi-period off-gas prediction in a steel making batch process, denominated Linz-Donawitz Gas (LDG). Baselines, heuristic statistical methods, multi-modal multivariate Long Short-Term Memory (LSTM) and Ensemble Gradient Boosting Decision Tree (GBDT) strategies were proposed and compared. Proposed methods, mixing classification and regression tasks, achieved good results on recoverable LDG prediction, establishing a benchmark on subject for future works. Experiments suggest improvements from 19.4% to 15.85% on average in mean absolute percentage error (MAPE) over recent reviewed papers within a similar scenario at same steel making plant.


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CARMO, Marcelo Magalhães do; MUTZ, Filipe W.; RESENDO, Leandro C.. Improving steel making off-gas predictions by mixing classification and regression multi-modal multivariate models. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 19. , 2022, Campinas/SP. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 37-48. ISSN 2763-9061. DOI: