电力科技与环保2024，Vol.40Issue(1)：44-52,9.DOI:10.19944/j.eplep.1674-8069.2024.01.006
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
Research on CO2 concentration detection based on deep learning neural net-work model of direct absorption spectroscopy
摘要
Abstract
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.关键词
燃煤电厂/碳排放/深度学习/吸收光谱/CO2/HITRAN数据库Key words
coal fired power plants/carbon emissions/deep learning/tunable diode laser absorption spectroscopy/ar-bon dioxide/HITRAN database分类
能源科技引用本文复制引用
史文辉,李凯亮,宫廷,田亚莉,孙小聪,郭古青,邱选兵,李传亮..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[J].电力科技与环保,2024,40(1):44-52,9.基金项目
国家重点研发计划(2023YFF0718100) （2023YFF0718100）
国家自然科学基金(52076145&12304403) （52076145&12304403）
山西省留学人员科技活动项目(20230031) （20230031）
山西省省筹资金资助回国留学人员科研资助项目(2023-151) （2023-151）
山西省基础研究计划(202203021222204&202303021212224) （202203021222204&202303021212224）
太原科技大学科研启动基金(20222121&20232033) （20222121&20232033）
