Study On Optimizing Combustion Control Strategy Of Continuous Steel Rolling Heating Furnace

Heating furnace is energy-consuming facility in rolling process.Under condition that heating quality of billet meets requirements,precise control of furnace temperature,improvement of fuel utilization rate,reduction of exhaust gas emissions have become main research topics.At present,there are many problems in control of heating furnace.Temperature measurement of billet is difficult,heating quality cannot be guaranteed,it caused excessive combustion;fuel utilization rate is low,corresponding flow rate cannot be adjusted through change of calorific value.Therefore,research on optimal combustion control of heating furnace is important in the steel rolling industry.Based on continuous heating furnace of steel plant production line,combustion control strategy of heating furnace is established.Using real data,prediction model of billet discharge temperature is established.Based on model,algorithm is used to optimize furnace temperature setting value,it is used in combustion control system of heating furnace to achieve goal of optimizing fuel control.After MATLAB simulation,billet temperature model and furnace temperature setting optimization model are obtained.In the heating furnace combustion control system,the double crossover limit control is improved.After simulation,the improved system response speed is accelerated.Billet temperature prediction modeling,through the BP(Back Propagation)neural network,and adopted the ant colony algorithm to optimize its network structure,build a new BP neural network structure,after two kinds of network model simulation,its accuracy is better than the original,therefore,using ant colony algorithm optimization BP neural network model prediction billet temperature.The setting value of the furnace temperature is optimized by artificial bee colony algorithm and genetic algorithm are optimized,and after simulation,the furnace temperature set value optimized by the artificial bee colony algorithm is lower than the actual value and the genetic algorithm.Moreover,the relatively low setting value can better optimize the combustion control of the heating furnace,so this algorithm is selected to optimize the furnace temperature set value.Figure 32;Table 5;Reference 56...