Cascade model for continuous prediction of silicon content of molten iron with coupled state variable nodes
Yang Han1,2, Ze-qian Cui1,2, Li-jing Wang1,2, Jie Li1,2, Ai-min Yang1, Yu-zhu Zhang2
1 Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes, North China University of Science and Technology, Tangshan 063210, Hebei, China 2 College of Metallurgy and Energy, North China University of Science and Technology, Tangshan 063210, Hebei, China
Cascade model for continuous prediction of silicon content of molten iron with coupled state variable nodes
Yang Han1,2, Ze-qian Cui1,2, Li-jing Wang1,2, Jie Li1,2, Ai-min Yang1, Yu-zhu Zhang2
1 Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes, North China University of Science and Technology, Tangshan 063210, Hebei, China 2 College of Metallurgy and Energy, North China University of Science and Technology, Tangshan 063210, Hebei, China
摘要 With the goal of achieving advanced and multi-step prediction of silicon content of molten iron in the blast furnace ironmaking process, a path adaptive optimization seeking strategy coupled with simulated annealing algorithm and genetic algorithm was proposed from the perspective of innovative intelligent algorithm application. It was further coupled with wavelet neural network algorithm to deeply explore the nonlinear and strong coupling relationship between the information of big data samples and construct a cascade model for continuous prediction of silicon content of molten iron with the intelligent research results of state variables such as permeability index as the node and silicon content forecast as the output. In the model construction process, the 3r criterion was used for non-anomaly estimation of abnormal data to build a time-aligned sample set for multi-step forecasting of iron content, the normalization method was used to eliminate the influence of dimensionality of sample information, and the spearman correlation analysis algorithm was used to eliminate the time delay between state variables, control variables, and silicon content of molten iron in the blast furnace smelting process. The results show that permeability and theoretical combustion temperature as the key state variable nodes have real-time correlation with the silicon content of molten iron, and there are accurate forecasting results on the optimal path with the endpoint of molten iron silicon content prediction. The path finding based on the improved genetic algorithm of simulated annealing has good effect on the downscaling and depth characterization of sample data and improves the data ecology for the application of wavelet neural network algorithm. The accuracy of the real-time continuous forecasting model for the silicon content of molten iron reaches 95.24%; the hit rate of continuous forecasting one step ahead reaches 91.16%, and the hit rate of continuous forecasting five steps ahead is 87.41%. This model, which can realize the nodal dynamics of state variables, has better promotion value.
Abstract：With the goal of achieving advanced and multi-step prediction of silicon content of molten iron in the blast furnace ironmaking process, a path adaptive optimization seeking strategy coupled with simulated annealing algorithm and genetic algorithm was proposed from the perspective of innovative intelligent algorithm application. It was further coupled with wavelet neural network algorithm to deeply explore the nonlinear and strong coupling relationship between the information of big data samples and construct a cascade model for continuous prediction of silicon content of molten iron with the intelligent research results of state variables such as permeability index as the node and silicon content forecast as the output. In the model construction process, the 3r criterion was used for non-anomaly estimation of abnormal data to build a time-aligned sample set for multi-step forecasting of iron content, the normalization method was used to eliminate the influence of dimensionality of sample information, and the spearman correlation analysis algorithm was used to eliminate the time delay between state variables, control variables, and silicon content of molten iron in the blast furnace smelting process. The results show that permeability and theoretical combustion temperature as the key state variable nodes have real-time correlation with the silicon content of molten iron, and there are accurate forecasting results on the optimal path with the endpoint of molten iron silicon content prediction. The path finding based on the improved genetic algorithm of simulated annealing has good effect on the downscaling and depth characterization of sample data and improves the data ecology for the application of wavelet neural network algorithm. The accuracy of the real-time continuous forecasting model for the silicon content of molten iron reaches 95.24%; the hit rate of continuous forecasting one step ahead reaches 91.16%, and the hit rate of continuous forecasting five steps ahead is 87.41%. This model, which can realize the nodal dynamics of state variables, has better promotion value.
Yang Han,Ze-qian Cui,Li-jing Wang, et al. Cascade model for continuous prediction of silicon content of molten iron with coupled state variable nodes[J]. Journal of Iron and Steel Research International, 2023, 30(05): 897-914.
2023, Vol. 30 
