Attention mechanism-aided data- and knowledge-driven soft sensors for predicting blast furnace gas generation

Blast furnace gas (BFG) is an important energy-carrying byproduct of the iron and steel industry. High-accuracy prediction of BFG generation is the basis of the dynamic balance of gas supply-demand and energy scheduling. However, due to instrument faults, measurements of BFG are discontinuous or inaccurate, making it difficult to accurately predict future BFG generation by using historical data, which seriously restricts the development of intelligent management and coordination between various gas sources and users. To solve this problem, an attention mechanism-aided data-and knowledge-driven soft sensor is proposed to predict BFG generation. To reduce the complexity of the samples, the proposed method selects key features to simplify the model input by using attention mechanism. Genetic algorithm (GA) is used to optimize hyperparameters to improve the stability of the model. In addition, combined with the knowledge of the blast furnace process, the prediction results are reasonably constrained. The results show that the prediction accuracy of the A-DK-GA-XGBoost model is higher than that of the other prediction models, with a mean absolute error of 68.2 m3/min, a symmetric mean absolute percentage error of 0.83%, a root mean square error of 68.71 m3/min, and an R squared of 99.06%. It is proven that the A-DK-GA-XGBoost model has superior performance.

Automated summary

Blast furnace gas (BFG) is an important byproduct of the iron and steel industry, and accurate prediction of its generation is crucial for gas supply-demand and energy scheduling. This study proposes a data-and knowledge-driven soft sensor aided by an attention mechanism to predict BFG generation. The model selects key features and optimizes hyperparameters using genetic algorithm, achieving high accuracy. By combining knowledge of the blast furnace process, the prediction results are constrained, leading to superior performance.