基于直接吸收光谱深度学习神经网络模型的CO2浓度检测研究OA

Carbon dioxide(CO2)is a principal component of greenhouse gases,significantly impacting global climate change and environmental quality.Coal-fired power plants,being the largest source of CO2 emissions in China,face se-vere challenges.Therefore,to accurately,rapidly,and cost-effectively monitor the CO2concentration in coal-fired power plants and promote their low-carbon development,this paper utilizes a distributed feedback semiconductor laser to con-struct a high-sensitivity CO2 gas detection system.It also validates and employs the HITRAN database as the dataset for a deep learning model,establishes a one-dimensional convolutional neural network(1D-CNN)model,and a back-propagation neural network for CO2 concentration detection.These models are compared with direct absorption spec-troscopy technique,and the performance of the 1D-CNN model is enhanced through K-fold cross-validation and parame-ter adjustment.The results show that the determination coefficient(R2)of the 1D-CNN model can reach 0.9997,with a relative error of 1.07%and an absolute error of 7.88 mg/m3,indicating the model's suitability.By utilizing the optimal pa-rameters of the 1D-CNN model,a comparison between predicted and actual data reveals an average relative error of 6.06%,an average absolute error of 17.97 mg/m3,and an R2 of 0.99941,demonstrating high accuracy in the model's predictions.This gas concentration detection model based on direct absorption spectroscopy and a deep learning neural network exhibits high accuracy and reliability in measuring CO2concentrations,offering robust technical support for envi-ronmental monitoring and energy conservation and emission reduction within the power industry.