Optimization Of Furnace Temperature System Based On Improved Particle Swarm Optimization

As one of the important equipment in steel rolling production line,the main function of the heating furnace is to meet the requirements of the rolling billet heating temperature.In actual industrial production,the heating furnace is a large energy consumption in iron and steel industry.Therefore,the study of step furnace optimization to improve the efficiency of heating,reduce the energy consumption of iron and steel industry has important significance.The purpose of furnace control is to set the furnace temperature according to the rhythm of rolling mill,so that the slab in the furnace fully heated in the slab baked when the furnace temperature and soaking temperature to meet the rolling requirements,the consumption of fuel as little as possible.At present,the temperature of the slab in the heating furnace is usually controlled by controlling the temperature of the furnace section.The main work of this paper is to study the stepping furnace optimization set.The main works are summarized as follows:The temperature of hot rolled slab is one of the important parameters of heating furnace optimization settings,in this paper,using the method of combining the mechanism analysis and discrete space established a one dimensional unsteady heat conduction model of the billet temperature changes in the process of hot rolling production.For the boundary conditions of the slab heating process,the heat flux of the slab surface is determined by total heat transfer factor method.The selection of total heat transfer factor of the furnace is very important for the calculation accuracy of the model.In this paper,the radiation absorption method is used to calculate the total heat absorption of the furnace.In view of the problems existing in the application of standard particle swarm optimization in the optimization of furnace temperature,the improvement is studied.Based on the standard particle swarm intelligence optimization algorithm,chaos initialization,adaptive adjustment of population size and inertia parameters are introduced in the form of concave function,and varying population size of particle swarm optimization algorithm is proposed,and the effectiveness of the improved algorithm is verified.The main problems existing in the slab heating process in furnace is energy consumption is too large,this paper established a minimum energy consumption of heating furnace as an objective function,and the constraint condition is the billet temperature,and the tapping temperature of steel billet,The section temperature difference and the temperature between the furnace sections are the constraint conditions.In the process of solving the optimization parameters,considering the complexity of the optimization objective function,varying population size of particle swarm optimization algorithm successfully applied to heating furnace temperature setting value.Finally,the surface temperature,the center temperature,the temperature difference of the slab surface with the furnace temperature before and after the optimization are compared and analyzed.