Aiming at the inherent defects of the traditional blast furnace temperature model, a prediction model of blast furnace molten iron temperature based on GRA-DE-KELM is proposed. Because the blast furnace ironmaking process is extremely complex and has the characteristics of multivariable, nonlinear, and strong coupling, the traditional modeling method cannot meet the requirements of high precision prediction of molten iron temperature. Firstly, because the parameters affecting the temperature of molten iron have strong correlation, in order to reduce the complexity of modeling and improve the performance of the model, it is necessary to extract the main parameters affecting the temperature of molten iron. In this paper, the GRA (gray relation analysis) method is used to analyze the input variables and determine the input variables of the model. Then the KELM (kernel extreme learning machine) prediction model is established by combining the analyzed variables, and DE (differential evolution) algorithm is used to optimize the model kernel parameters. Finally, the model is trained and tested using field-acquired data and compared to traditional predictive models. The results show that the model can quickly and accurately predict the molten iron temperature, and has a good guiding significance for the actual regulation of blast furnace temperature.

Aiming at the problem that the silicon content of molten iron can not be detected online, a model for predicting silicon content in molten iron based on Hybrid Kernel Extreme Learning Machine optimized by Improved Particle Swarm Optimization Algorithm (IPSO-HKELM) is proposed. Firstly, the input variables are reduced by PCA, and then the prediction model of molten iron content based on HKELM is established. In this paper, PSO is used to optimize the kernel parameters of HKELM. Aiming at the problem that PSO is easy to fall into local optimum, the Inertia weight reduced with the number of iterations and the random back-based learning mutation operation are introduced, so that PSO can jump out of the local minimum point more easily and get the optimal result. Experiments show that the prediction model of silicon-based silicon content based on IPSO-HKELM has high prediction accuracy and short time, which can meet the actual production needs.