Abstract
The stability of blast furnace temperature is an important condition to ensure the efficient production of hot metal. Accurate prediction of silicon content in hot metal is of great significance to the control of blast furnace temperature in iron and steel plants. At present, the accuracy of most silicon prediction models can only be guaranteed when the furnace condition is stable. However, due to many factors affecting the silicon content in hot metal of blast furnace, and there are large noises, large delays and large fluctuations in the data, the previous prediction results are of limited guiding significance to the actual production. In this paper, combined with the actual situation, the convolution neural network is used to extract the furnace condition characteristics, and then combined with the attention mechanism and the IndRNN model to get the prediction results, so that the prediction can better adapt to the fluctuating data set. The experimental results show that the prediction error of this model is lower than that of other models, which provides a new solution for the research of silicon content in hot metal of blast furnace.
Highlights
In iron and steel production, the blast furnace is generally selected as the reaction vessel to produce molten iron
The key to maintain high-efficiency hot metal production is to ensure the stability of blast furnace temperature
More and more silicon content prediction models are applied in hot metal production of blast furnace
Summary
In iron and steel production, the blast furnace is generally selected as the reaction vessel to produce molten iron. For the data-driven model, according to experts' experience, in order to take timely and appropriate control measures, the predicted silicon content in hot metal is generally required to be three hours ahead of the chemical analysis value. Wang et al Used the method of combining PCI with the least square method, Sun Jie et al.[5] used the method of combining genetic algorithm with extreme learning machine These two methods can achieve good results when the temperature of blast furnace is stable, but the effect is not ideal in the case of fluctuation; Jian et al.[6] used SVM to predict silicon content, and the result has large deviation, Ye Fei[7] With Yang et al.[8], BP neural network was applied to silicon content prediction, while Li Ze-long[9] proposed LSTM to predict silicon content, and both methods achieved good results.
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