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.
The real-time and accurate prediction of the molten iron silicon content of the blast furnace plays an important role in regulating the temperature of the blast furnace and stabilizing the furnace condition. When the time is large, the accuracy and credibility of the forecast results decrease rapidly, which is not conducive to on-site operators to carry out production operations according to the forecast results. To this end, this paper adds a state variable to each piece of data through the flexible least square parameter estimation method, and selects the training set in a state similar to the test sample. This makes the selection of training data more accurate and reliable. Application examples show that the method proposed in this paper improves the accuracy of silicon content prediction results and has good guiding significance for actual production operations.
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