Electronic nose detection for hydrocarbon gas based on feature extraction

In order to solve the problem that the gas logging technology in oil and gas exploration can not be used in the field of fast detection, the electronic nose is introduced in the fast detection of hydrocarbon gas in oil and gas exploration. The feature extraction methods, such as maximum value, average value, Fourier transform, Gaussian transform, exponential curve fitting, sine curve fitting, are used to preprocess the data of the electronic nose. The analysis shows that the correlation between the maximum value and the original data is the highest, and the parameter of exponential curve fitting is the lowest. The results of principal component analysis and machine learning classification show that principal component analysis can not identify hydrocarbon gases of different components. When the maximum value is selected as the feature, the effect of machine learning classification is the best, and the recognition rate of logical regression, K nearest neighbor, CatBoost, GBDT and Bagging can reach 1. Compared with different machine learning algorithms, SVM has the best overall classification effect, and the average recognition rate of all feature extraction methods is 0.989. © 2020, Jilin University Press. All right reserved.