Research On Air-Fuel Ratio Optimization Based On Correlation Analysis And Comprehensive Evaluation

Hot blast stove is an important supporting equipment of blast furnace ironmaking and it is also a major part of energy consumption.Its main role is to heat the cold air blown from the blower to the required temperature and transport to the blast furnace to provide the desired high-temperature environment for the smelting process.In the production process,the ratio of air and blast furnace gas(air-fuel ratio)not only affects the quality of hot stove production,but also determines the gas consumption.With the rapid development of production technology,the continuous updating of production equipment,the ability of hot blast stoves to provide hot air also increases.Therefore,in addition to require stable output,continuous high temperature hot air,at the same time require the full use of gas as much as possible,in order to achieve energy conservation in the production process.The research object of this thesis is No.3 hot blast stove in a factory.Reference to the production process and operation requirements,combined with the historical data in the production process,from the guarantee of ensuring the heat storage capacity and saving fuel,the optimization of air-fuel ratio is explored and studied:(1)According to the factory data,statistical analysis of various parameters of hot stove,analyze the relationship between combustion efficiency and air-fuel ratio from the theoretical point of view.Considering the time delay between parameters,determine the lag time of the dome temperature and then adjust the data.In order to solve the influence of disturbing factors in optimization process,analyze the correlation between each parameter and vault temperature,according to the rules to filter the data,so as to ensure that the change of vault temperature is most affected by the air-fuel ratio,and then provide a data basis for the comprehensive evaluation system.(2)Establish the evaluation system,select reasonable evaluation index according to production situation,calculate index weight by using entropy method,comprehensively evaluate the selected data samples,and finally select the optimal air-fuel ratio according to the ranking.Design a visual air-fuel ratio optimization software that allows the operator to operate the software to achieve the corresponding functional requirements.(3)Based on the actual operation data of hot stove,after determining each parameter,the BP neural network is used to establish a model with gas air flow as input and temperature as output.Particle swarm optimization algorithm is used to optimize the model to improve the accuracy of the model,avoid falling into the local optimization,and finally obtain a better modeling accuracy.Using the established hot blast stove model,the new test data is determined according to the optimal air-fuel ratio and then input to the model.Comparing the output data with the real data,using the model and the comprehensive evaluation system to verify multiple air-fuel ratios,the results show that the optimal air-fuel ratio can reduce the gas consumption under the premise of ensuring the heat storage requirements of the hot blast stove.