Optimization and effect comparison of typical gas pressure compensation model in chemical industry park

Abstract

In chemical parks, the leakage of harmful gases can lead to poisoning and even explosions. Therefore, its monitoring and leakage warnings are crucial. This paper takes the harmful gases CO₂, CH₄, and CO as examples, and set up an infrared gas sensor pressure compensation experimental platform to carry out experiments, to solve the problem of decreased detection accuracy of gas sensors due to changes in ambient pressure during detection. The pressure compensation experimental ranges of the gas sensors are 0 ∼ 5 %, 0 ∼ 20 %, and 0 ∼ 1000 ppm, and the maximum absolute errors of the infrared gas test data obtained under different concentrations and pressures are 0.24 ∼ 0.67, 0.89 ∼ 1.12, and 45 ∼ 60 ppm, respectively. The pressure compensation model based on the least squares method was constructed, and the maximum absolute errors were obtained as 0.08 ∼ 0.19, 0.13 ∼ 0.64, and 24 ∼ 37 ppm, respectively. The pressure compensation model based on the GA-BP neural network was constructed, and the maximum absolute errors were 0.04 ∼ 0.10, 0.08 ∼ 0.10, and 0.60 ∼ 8.30 ppm, respectively. The GA-BP neural network combines the genetic algorithm and the backpropagation algorithm, which can better deal with nonlinear problems. The comparison of these two models reflects the superiority of the GA-BP neural network model in the compensation effect. The establishment of the neural network pressure compensation model optimized by the genetic algorithm can effectively improve the detection accuracy of the gas sensor, and it is expected that the results are of great practical significance to guarantee production safety and protect the environment in the enterprise chemical park.