Hybrid model associating thermodynamic calculations and artificial neural network in order to predict molten steel temperature evolution from blowing end of a BOF for secondary metallurgy

Abstract

The developed model is an association of thermodynamic calculations for dissolution of alloys, slag formers and the deoxidation reaction in the molten steel with two artificial neural network (ANN) models trained with industrial data, to predict the molten steel temperature drop from the blowing end of the BOF until the first measurement at secondary metallurgy. To calculate the associated energy for deoxidation, an experiment was designed to set up the parameters for oxygen partitioning among deoxidants, with timed aluminum addition during teeming being the main parameter. The temperature control in the teeming stage presented a standard deviation for the error of prediction of 5.46 o C, for transportation from the rinsing station to the secondary metallurgy of 2.79 o C. The association of all calculations presented an error standard deviation of 7.49 o C. The operational validation presented superior accuracy compared with the current method for controlling the temperature, resulting in a reduction in the aluminum consumption for heating at secondary metallurgy with a potential economy of U$ 4.07 million per year for a steel shop producing 5 million tons of steel yearly. The artificial neural network model confirmed its capacity for modeling a complex multivariable process and the separation of thermodynamic calculation provides a better adaptability to different steel grades with different teeming strategies.

Toward learning steelmaking—A review on machine learning for basic oxygen furnace process

“…Feature engineering, on the other hand, leverages domain knowledge and well established principles to derive new, informative features based on existing data [26,33] . Feature engineering serves as a bridge between the raw data and the domain expertize, thereby enabling a more integrative approach to modeling that combines both data‐driven and physical‐based methods.…”

Using Machine Learning for Robust Target Prediction in a Basic Oxygen Furnace System

“…There are many variants of using ANNs for the modeling of the BOF process. [4,8,9,[14][15][16][17][18][19][20][21][22][23][24][25] For all of these, only between 6 and 18 features are used, and the number of used samples varies from 17 to 2500 with a majority at the lower end. The prediction accuracy typically reaches around 90 pct, but the data used are typically selectively chosen, and it is therefore likely that the result is biased in a positive direction.…”

End‐Point Prediction of Converter Steelmaking Based on Main Process Data

“…To improve prediction accuracy, some researchers employ principal component analysis, [16] metallurgical mechanism models, and other feature optimization techniques. [17] Comparative studies of different model applications are also conducted. [18][19][20] Notably, the integration of deep-learning methods in metallurgy has led to the use of core process data (time-series data), like oxygen flow and lance position in converter end-point prediction.…”