Study on prediction method of hot metal temperature in blast furnace

In order to improve the accuracy of the blast furnace hot metal temperature prediction, the Least Squares Support Vector Machine optimized by an improved Particle Swarm Optimization is proposed to solve this problem. By using the improved optimization algorithm to optimize the support vector machine parameters, the prediction model can obtain well prediction effect. What's more, to avoid particle swarm easily falling into local minim problem during the process of the optimization, the chaotic method is introduced to help the particle swarm to jump out of the local optimal solution in the process of particle swarm optimization. Besides, the varying learning factors and the quadratic decreasing of the inertia weight are introduced to enhance the local search ability of the algorithm and pick up the convergence speed. The prediction model is established by the least squares support vector machine based on the chaos particle swarm optimization, then this method is compared with other prediction methods. The simulation result indicates that the prediction model has higher prediction accuracy. Therefore, a more accurate prediction method of the blast furnace hot metal temperature is proposed.