Mixed Gas Detection and Temperature Compensation Based on Photoacoustic Spectroscopy

Abstract

In recent years, with the continuous progress of technology and the development of society, the demand for updating trace gas detection technology has been increasing. The ability to quickly and accurately detect the composition and concentration of gases has become a hot topic in current research. In response to address issues such as difficulties in judging data for classification and recognizing gas components with low accuracy, a KNN-SVM algorithm has been proposed. The algorithm primarily reclassifies ambiguous data that are close to the hyperplane but do not have a clear affiliation, capturing data characteristics more comprehensively. It determines the weight ratio of each algorithm through experiments to improve the accuracy of gas category discrimination. Experimental results show that, compared to the traditional SVM algorithm, the KNN-SVM algorithm performs better in gas classification prediction, with an accuracy rate of 99.167% and an AUC indicator of 99.375%, enhancing the accuracy of gas detection. In response to the impact of temperature on the system during the experimental process, a WOA-BP temperature compensation model was established to compensate for temperature in gas concentration detection. After comparing various optimized BP neural network models, the performance of the WOA-BP temperature compensation was the most outstanding, with an R2 of 97.89%, MAE of 1.4868, RMSE of 2.0416, and a convergence speed after 15 iterations, reducing detection errors and thus achieving precise detection of low-concentration mixed gases.