支持向量机在混合气体定量分析中的应用

Application of Support Vector Machine in Quantitative Analysis of Mixed Gas

针对使用掺铥光纤激光器的气体传感系统进行混合气体测量时,吸收谱线重叠较为严重且相互交叉吸收干扰的现象造成的测量误差大、分析精度低的问题,提出一种基于自适应变异粒子群优化的支持向量机(SVM)方法,用于建立混合气体体积分数定量分析预测模型。对体积分数为0.5%~2%的氨气(NH3)和2%~5%的二氧化碳(CO_(2))混合气体的吸收光谱数据进行采集和处理,利用自适应变异粒子群优化(AMPSO)算法对SVM模型参数进行寻优,利用获得的最优模型参数构建氨气和二氧化碳气体体积分数定量分析模型,并与标准粒子群优化算法和网格搜索法进行对比。实验结果表明,基于自适应粒子群优化算法建立的氨气和二氧化碳气体体积分数定量分析模型在较为合适的寻优时间下,可以得到最佳的均方误差,效率较高,该模型对测试集中氨气和二氧化碳气体体积分数设定值与预测值的均方误差分别为0.000088和0.000170,决定系数R2均为0.9998,满足混合气体检测要求。

Objective Vehicle exhaust contains gases such as NH3 and CO_(2) and is becoming an essential source of air pollution and greenhouse effect.The intracavity absorption gas sensing technology based on fiber ring laser has many advantages,which are very suitable for realtime detection of toxic and harmful gases in environmental protection.However,when the gas sensing system based on a thuliumdoped fiber laser is applied for quantitative analysis of mixed gas,the gas detection accuracy is often affected by cross interference caused by overlapping spectral absorption lines between component gases,and a nonlinear shift led to by changes in temperature and pressure at the experimental sites.As a small sample machine learning method,support vector machine(SVM)based on statistical theory has high accuracy and good generalization ability.It can be combined with infrared spectrum analysis to build a mixed gas volume fraction regression prediction model and correct nonlinear interference,thus greatly improving the accuracy and reliability of the gas quantitative analysis.Methods In this paper,an active intracavity gas sensing system based on a thuliumdoped fiber laser is built to collect the absorption spectrum data of NH3 and CO_(2) gases.The system is mainly divided into an adjustable light source(part A),a sensing part(part B),a data acquisition and processing part(part C),and a gas distribution part(part D).Before collecting the gas spectrum,sufficient nitrogen is introduced into the gas chamber to eliminate the interference of water vapor and CO_(2) in the gas distribution instrument.The experimental environment is 0.1 MPa under normal pressure,and the sampling rate of the acquisition card is 20 kHz,with 20 groups of data being collected and 12 samples for each group of data.Before building the model,spectral data should be preprocessed to reduce the impact of background noise and improve the signaltonoise ratio.However,it is inappropriate to do too much preprocessing to avoid losing some important spectral information.We also preprocess the spectral data through the methods of denoising,baseline correction,and smoothing.With an aim to improve the modeling speed,principal component analysis(PCA)is employed to project the multidimensional linear transformation of the original gas absorption spectrum data into a highdimensional space to obtain the principal components corresponding to the maximum variance.The principal components at this time are leveraged to replace the eigenvalues in the original data,reduce the data dimension,and prevent the correlation between variables from affecting the extraction of these components and the prediction accuracy of the regression model.The standard particle swarm optimization(PSO)algorithm has fast convergence and short optimization time,whereas it features premature convergence of the model,low accuracy of optimal solution search,and low efficiency of later iteration.Therefore,we propose an improved algorithm,which is adaptive mutation particle swarm optimization(AMPSO).By introducing an adaptive mutation operator,the updated particle positions are randomly mutated so that particles can enter other regions of the solution space to continue searching,thereby improving the ability of particle swarm optimization to jump out of the local optimal solution and avoid premature convergence of the algorithm model.The optimal combination of parameters obtained from the NH3-SVM model and the CO_(2)-SVM model optimized by the AMPSO algorithm is input into the support vector machine to obtain the corresponding volume fraction regression model.The prediction results of training set samples and test set samples of the NH3-SVM model and the CO2-SVM model can be obtained(Fig.8).The determination coefficient R2 is adopted to evaluate the fit between the predicted volume fraction and set volume fraction.Results and Discussions Although the optimization time of the standard PSO algorithm is the shortest,due to premature convergence,the mean square error is large,and the regression prediction of the model is not good.The mean square error of the grid search method is close to that of the AMPSO algorithm and both errors are small.However,since the grid search method is a nonheuristic algorithm,each optimization needs to traverse all points in the grid,resulting in long optimization time.Compared with the two algorithms,the AMPSO algorithm can obtain the best mean square error at a more appropriate optimization time,with higher efficiency.When regression predictions on the volume fraction of the training set samples are conducted,the mean square errors of the volume fraction set point and the volume fraction prediction value of the NH3-SVM model and CO_(2)-SVM model are 0.000087 and 0.000128 respectively,and the determination coefficients R^(2) are 0.9997 and 0.9999 respectively.When volume fraction regression prediction for the test set samples is carried out,the mean square errors of the volume fraction set point and the volume fraction prediction value of the NH_(3)-SVM model and CO_(2)-SVM model test set are 0.000088 and 0.000170 respectively,and R2 is 0.9998.Conclusions An active intracavity gas sensing system based on a thuliumdoped fiber laser is built to collect the absorption spectrum data of NH3 and CO_(2) gases.The predicted volume fraction of the regression prediction model of NH3 and CO2 gas volume fraction is in good agreement with the actual volume fraction,with sound prediction ability and effect,and small error.The built AMPSO gas volume fraction regression model has high prediction accuracy and strong accuracy and can be applied for mixed gas volume fraction regression prediction.