Attention-Based Convolutional Aggregation: An Efficient Model for Off-Gas Profile Forecasting and Dynamic Pre-Control of BOF Steelmaking

This study proved that the curves of carbon monoxide (CO), carbon dioxide (CO₂), and CO + CO₂ in the off-gas profile were forecastable, and realized a 32-s-ahead forecasting for them. It established a technical foundation for addressing the delay in off-gas profile display and for enabling pre-control in BOF steelmaking based on the forecasted curves’ features. First, a data pre-processing method was proposed based on the characteristics of the off-gas curves, where there are many samples, but each sample contains limited time-steps. It is termed the mixed-batch approach. The importance of the time series’ channels and time-steps were also analyzed by models with attention mechanism. Then, a deep-learning model is proposed to forecast the dynamic off-gas profile, named attention-based convolutional aggregation (ABCA). It incorporates artificial intelligence (AI) techniques, such as aggregation structures, causal dilation convolution, attention mechanisms, residual connections, etc. Its forecasting coefficient of determination (R²) values for the curves of CO, CO₂, and CO + CO₂ reached 0.9386, 0.8566, and 0.9428, respectively, while the mean squared errors (MSEs) values were 47.3884, 11.9314, and 54.3583, respectively. These results outperform the benchmark state-of-the-art (SOTA) models. Additionally, ABCA was implemented in a forecasting tool for external validation. The results of external validation showed that ABCA has good forecasting accuracy and robustness. What is more, approaches in four aspects of pre-control of BOF steelmaking process with forecasted off-gas profile were also provided as pre-control examples.