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| Citation: |
ZOU Yongming, WANG Yinhui, TIAN Fuchao. Research on temperature compensation of infrared CO analyzer based on GA-BP optimization model[J]. Safety in Coal Mines, 2024, 55（9）: 60−70. DOI: 10.13347/j.cnki.mkaq.20240569
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Spectral analysis is an important technical means for the quantitative analysis of coal mine gas. However, the portable infrared gas sensor widely used at present is greatly affected by the ambient temperature in the field test process. In order to improve the analysis accuracy and applicability of the gas analyzer, we carry out theoretical analysis and experimental research around the necessity of constant temperature heating of infrared gas analyzers, reasonable heating temperature range, and optimization model of test data error. Firstly, an experimental platform of infrared gas analyzer covering pre-temperature compensation, constant temperature heating test, post-temperature compensation and different test environment temperature conditions is constructed, and the experimental steps as well as the corresponding analytical methods are proposed; secondly, in the ambient temperature of 25-45 ℃ interval, the tested CO gas volume fraction is 800×10−6, through a lot of tests, it is found that the accuracy of CO gas analyzer is best when the heating temperature is 45 ℃, the test value is the closest to the true value of gas volume fraction. At the same time, CO (10×10−6-500×10−6) standard gas is selected for the accuracy test, and the absolute error of the calculated CO gas is only 0-9×10−6, which is better than the error range specified in the standard; then, setting the variable temperature environment of −15- 45 ℃, the temperature compensation verification of the heated CO gas sensor is carried out, the test results show that the lower the ambient temperature, the higher the volume fraction of the gas to be measured, the greater the sensor detection error, the maximum error can reach 110×10−6; based on this, the error analysis models of BP and GA-BP neural network are established, and the ambient temperature, test volume fraction and standard gas volume fraction are fused with the optimized model. It is found that MAE value obtained by GA-BP neural network model is reduced by 12.982 3×10−6 compared with BP neural network. The MSE and RMSE calculation results also verify the stability and feasibility of GA-BP model in gas analysis.
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