Affiliation:
1. School of Metallurgical Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China
2. State Key Laboratory of New Technology of Iron and Steel Metallurgy, Beijing University of Science and Technology, Beijing 100083, China
Abstract
In the process of blast furnace smelting, the stability of the hearth thermal state is essential. According to the analysis of silicon content in hot metal and its change trend, the operation status of the blast furnace can be judged to ensure the stable and smooth operation of the blast furnace. Based on the error back-propagation neural network (BP), the flower pollination algorithm (FPA) is used to optimize the weight and threshold of the BP neural network, and the prediction model of silicon content is established. At the same time, the principal component analysis method is used to reduce the dimension of the input sequence to obtain relevant indicators. The relevant indicators are used as the input, and silicon content in the hot metal is used as the output, which is substituted into the model for training and utilizes the trained model to predict. The results show that the hit rate of the prediction model is 16% higher than the non-optimized BP prediction model. At the same time, the evaluation indicators and operation speed of the model are improved compared with the BP prediction model, which can be more accurately applied to predict the silicon content of the hot metal.
Funder
Natural Science Basic Foundation of China
Key Research and Development Project of Shaanxi Province
Shaanxi Provincial Innovation Capacity Support Plan
Subject
General Materials Science,Metals and Alloys
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