Endpoint prediction of BOF steelmaking based on state-of-the-art machine learning and deep learning algorithms

Abstract

To enhance the efficiency and sustainability, technical preparations were made for eliminating the Temperature, Sample, Oxygen test of basic oxygen furnace (BOF) steelmaking process in this work. Utilizing data from 13,528 heats and state-of-the-art (SOTA) machine learning (ML) and deep learning algorithms, data-driven models with different types of inputs were developed, marking the first use of time series data (off-gas profiles and blowing practice related curves) for BOF steelmaking’s endpoint prediction, and the tabular features were expanded to 45. The prediction targets are molten steel’s concentrations of phosphorus (Endpoint [P], %) and carbon (Endpoint [C], %), and temperature (Endpoint-Temp, °C). The optimal models for each target were implemented at a Hesteel Group’s BOF steelmaking facility. Initially, SOTA ML models (XGBoost, LightGBM, Catboost, TabNet) were employed to predict Endpoint [P]/[C]/Temp with tabular data. The best mean absolute errors (MAE) achieved were 2.276 × 10−3% (Catboost), 6.916 × 10−3% (Catboost), and 7.955°C (LightGBM), respectively, which surpassed the conventional models’ performance. The prediction MAEs of the conventional models with the same inputs for Endpoint [P]/[C]/Temp were 3.158 × 10−3%, 7.534 × 10−3%, and 9.150°C (Back Propagation neural network) and 2.710 × 10−3%, 7.316 × 10−3%, and 8.310°C (Support Vector Regression). Subsequently, predictions were explored to be made using SOTA time series analysis models (1D ResCNN, TCN, OmniScaleCNN, eXplainable Convolutional neural network (XCM), Time-Series Transformer, LSTM-FCN, D-linear) with the original time series data and SOTA image analysis models (Pre-activation ResNet, DenseNet, DLA, Dual path networks (DPN), GoogleNet, Vision Transformer) with resized time series data. Finally, the concat-model and the paral-model architectures were designed for making predictions with both tabular data and time series data. It was determined that the concat-Model with TCN and ResCNN as the backbone exhibited the highest accuracy. It’s MAE for predicting Endpoint [P]/[C]/Temp reaches 2.153 × 10−3%, 6.413 × 10−3%, and 5.780°C, respectively, with field test’s MAE at 2.394 × 10−3%, 6.231 × 10−3%, and 7.679°C. Detailed results of the importance analysis for tabular data and time series are provided.

Keywords

• bof
• endpoint
• machine learning
• deep learning
• time series
• field application