Application of Particle Swarm Optimization BP Neural Network in Methane Detection

In order to accurately and quickly detect and predict the concentration of methane gas, a methane concentration detection system based on infrared differential absorption method was designed. The detection system adopted a double-chamber structure to reduce the influence of system component instability, and the input and output interfaces of the gas chamber were connected to the transmission fiber through a graded-index lens to reduce the loss of light intensity. The average error of the detection system is 0. 007 5. An error back propagation neural network algorithm based on particle swarm optimization was used to construct a prediction model with methane gas in the range of 0.2%similar to 2.0%. In the process of sample training, the accuracy of the prediction model reaches 10 (4), the correlation coefficient between the actual output value and the expected linear regression is 0.998 8, and the maximum relative standard deviation is 0. 248%. The experimental results show that the prediction performance of particle swarm optimization error back propagation neural network is better than that of error back propagation neural network prediction model in methane concentration prediction.