The metallurgical industry is the manufacturing industry with the highest carbon emissions in China. Carbon emission in blast furnace accounts for 73.6% of the total long process flow. The working state of chute directly affects the longevity, safety, efficiency and low-carbon of blast furnace. However, at present, there is no monitoring method for chute damage. In this paper, an intelligent chute fault detection method based on numerical simulation, machine vision and deep learning is proposed. Specifically, discrete element method (DEM), gray level co-occurrence matrix (GLCM) and kernel correlation filter (KCF) modules are introduced to screen the best detection frames. Comprehensive experiments show that the proposed algorithm is superior to other traditional classification methods and other deep learning classification network (SVM and LetNet-5), with an accuracy of 96.1%. This method not only contributes to understand the wear mechanism of rotating chute, but also provides a reference for automatic wear monitoring of chute.

Accurate prediction of temperature and carbon content of liquid steel plays an important role in steelmaking process. In order to enhance the accuracy of predicting the basic oxygen furnace (BOF) end-point temperature and carbon content of liquid steel, a hybrid model based on principal component analysis (PCA) − genetic algorithm (GA) − backpropagation (BP) neural network is proposed. PCA is used to reduce the dimensionality of the input variables and eliminate the collinearity among the variables, then the obtained principal components are seen as new input variables of the BP neural network. GA is employed to optimize the initialized weights and thresholds of the BP neural network. Data from a 250t BOF of H steel plant in China is used to test and validate the model. The results show that the prediction accuracy of the single output models is higher than that of the dual output models. The PCA-GA-BP neural network model with single output shows higher prediction performance than others. The root mean square error of temperature between predicted and actual values is 7.89, and that of carbon content is 0.0030. Therefore, the model can provide a good reference for BOF end-point control.

The segregation phenomena of burden distribution were analysed by the discrete element method in detail, and the calculation result of burden falling locations was validated by full-scale model experiment. The calculation results show that large particles mainly gather in the sub-centre and wall region of the stock surface, but the small particles mainly stay in the centre and platform region of the stock surface, and the size distribution of coke depends on the hopper structure mostly. The burden layer thickness is not symmetric in the radial direction of the stock surface owing to the particle velocity at the outlet of rotating chute is not stable, and the mass distribution of coke is also not uniform in the circumferential direction of the stock surface as the delay angle of the falling point is not constant.

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The Si content in pig iron is one of the important indicators for evaluating the technical level of ironmaking in the blast furnace. From the late twentieth century, the production of low-Si pig iron has attracted more and more attention in steel industries of the world. For ironmaking process, the decrease of Si content is beneficial for increasing the coefficient of utilization of the capacity of blast furnace, reducing the fuel consumption and increasing the output. For the following steelmaking process, the decrease of Si content is beneficial for reducing the amount of slag, consumption of flux and shortening steelmaking time. Overall, it is an important technology for productivity enhancement and energy saving. In this paper, the transfer mechanism and influence factors of Si in blast furnace have been reviewed and discussed in detail. This paper will provide theoretical basis and operation strategies for reducing the Si content.