Design And Optimization Of Gas Calorimeter

Energy is an important issue concerning the economic and industrial development of a country.Gas is an important industrial fuel in industrial production,and also a necessity of national life.In order to realize China's sustainable development strategy,for the energy problem,we must do is to maximize energy conservation,improve energy efficiency.In this paper,based on the research background of metallurgy and iron and steel industry,a gas calorific value detection and control system is designed by using the analysis method of residual oxygen content in flue gas,and the gas can achieve the best combustion state by controlling the air flow.The system is designed based on PLC S7-200.The monitoring system is configured based on MCGS software.It uses touch screen and realizes the control of the system through communication.Then,the correlation model between pressure,residual oxygen content and production target is established to optimize the set value of air-fuel ratio.Carry out the following exploration and research.(1)Make the control plan of calorific value system process and define the control task.Complete PLC selection and program design,meet the field control requirements,and meet the production site use standards.(2)According to the needs of field operation,a CO concentration detection device and a co compensation air volume model are established.(3)According to the data analysis,it can be seen that steel production has a large optimization space,and the gas combustion efficiency is determined as the optimization target.(4)A large number of historical data of calorific value are preprocessed and the production target model is established.The correlation analysis of the data is carried out.The air pressure and residual oxygen content of the gas are selected as the input of the target function.The BP neural network model is used to predict the calorific value of the gas.The simulation results show that the BP neural network has a certain prediction ability,but the error is large.Further optimization of the model is needed.(5)Combining the advantages of genetic algorithm and BP neural network,genetic algorithm is used to optimize BP neural network.Simulation results show that the optimized model can predict calorific value more accurately.The air-fuel ratio can be setmore accurately by guiding the operators.