Poisonable Mixed Gas Detection Method Based on Neural Network and Fourier Spectrum Analysis
The identification of gas which is easy to produce drugs plays an important role in inhibiting the cir-culation of drugs,but the current research on the concentration detection of gas which is easy to produce drugs is not mature.In this paper,the back propagation(BP)neural network model is established by collecting Fourier infrared spectrum information for detecting the gas mixture that is easy to produce drugs.The model of BP-FTIR is verified and analyzed by taking the mixed gas experiment of ether and acetone as an example.The results show that the global regression R value of the multi-component gas spectrum data collected by BP-FTIR absorption system is 0.99273,and the correlation is strong.In the mixed gas test,the maximum predic-tion error of ether gas is 28 ppm,and the maximum prediction error of acetone gas is 11 ppm.The overall pre-diction error is small,indicating that the model can predict the concentration of ether acetone mixture well.Therefore,the prediction result of concentration inversion of multi-component gas which is easy to produce drugs by neural network model is highly accurate,and this study also provides a new research idea for the de-tection of gas which is easy to produce drugs and other mixed gases.
BP neural networkFourier transform infrared spectrumpoisonable chemicalsmixed gas detec-tion
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