Research On Optimization Strategy Of Combustion Process Of Hot Blast Stove Based On Air-Fuel Ratio Extreme Value Optimization

As a heat exchange equipment in the ironmaking process,the hot blast stove is used to generate and deliver high temperature hot air to the blast furnace to meet the heat demand of the iron ore reduction process.During the combustion of the hot blast stove,the air-fuel ratio is an important parameter in the combustion process of the hot blast stove,which greatly affects the combustion efficiency of the hot blast stove.At present,most steel companies still use manual methods to regulate the air-fuel ratio of hot blast stoves.Manual operation cannot achieve the desired control effect due to blindness and hysteresis.Therefore,optimizing the air-fuel ratio of the hot blast stove helps the iron-making process to achieve energy saving,cost reduction and efficiency increase.In view of the problems of low combustion efficiency,large energy consumption and low supply air temperature in the hot blast stove control of most domestic steel enterprises,this paper takes the hot blast stove of 1880m3 blast furnace of a steel enterprise as the research object.Based on the modeling and analysis of the optimal air-fuel ratio,an intelligent optimization control strategy for the combustion process of hot blast stove based on the optimization of air-fuel ratio extreme value is proposed and studied and applied in depth.In order to optimize the extreme value of air-fuel ratio model,the paper first analyzes the variation characteristics of air-fuel ratio in the combustion process of hot blast stove,and then chooses the system identification method to construct the air-fuel ratio mathematical model.Then,the complex nonlinear object is simulated by single hidden layer BP neural network and double hidden layer BP neural network respectively.According to the simulation results,the double hidden layer BP neural network is used to identify the air-fuel ratio model online.In order to optimize the air-fuel ratio,the genetic algorithm,nonlinear programming genetic algorithm,particle swarm optimization algorithm and adaptive mutation particle swarm optimization algorithm are used to optimize the complex nonlinear objects.After comparing and analyzing the simulation results,the adaptive mutation particle swarm optimization algorithm is used to optimize the air-fuel ratio and find the optimal air-fuel ratio at different times.Finally,this paper applies the air-fuel ratio intelligent optimization control system to the industrial field.The field operation results show that the system can improve the combustion efficiency of the hot blast stove and improve the air supply quality,achieving the purpose of energy saving,cost reduction and efficiency increase.