基于双通道卷积神经网络的煤灰分太赫兹预测  

Terahertz Coal Ash Prediction Method Based on Dual-Channel Convolutional Neural Network

作　　者：任姣姣 焦铁鑫[1,2,3] 顾健 陈奇 李丽娟[1,2,3] 张霁旸[3] Ren Jiaojiao;Jiao Tiexin;Gu Jian;Chen Qi;Li Lijuan;Zhang Jiyang(Key Laboratory of Photoelectric Measurement and Optical Information Transmission Technology of Ministry of Education,Changchun University of Science and Technology,Changchun 130022,Jilin,China;College of Optoelectronic Engineering,Changchun University of Science and Technology,Changchun 130022,Jilin,China;Zhongshan Institute of Changchun University of Science and Technology,Zhongshan 528400,Guangdong,China)

机构地区：[1]长春理工大学光电测控与光信息传输技术教育部重点实验室,吉林长春130022 [2]长春理工大学光电工程学院,吉林长春130022 [3]长春理工大学中山研究院,广东中山528400

出　　处：《光学学报》2023年第22期312-318,共7页Acta Optica Sinica

基　　金：吉林省科技发展计划项目(20220508032RC);中山市第九批创新科研团队(GXTD2022010);中山市第二批社会公益和基础研究项目(2022B2012)。

摘　　要：利用太赫兹时域光谱技术对不同煤灰分含量的光谱进行分析,发现在0.5~3 THz频段内,随着煤灰分含量的增加,其折射率会逐步提高,吸收效应也会逐步增强;考虑到煤样品厚度对光谱的影响,提出一种基于厚度校正的吸收系数特征提取方法,提高了低灰分煤样品吸收曲线的数据区分度;利用双通道卷积神经网络提取折射率和吸收系数特征,建立了煤灰分预测模型。实验结果显示,训练集的拟合度为R2=98.21%,预测精度ERMS=0.1442,而预测集的R2=93.56%,ERMS=0.2037,均优于传统PLSR、BP和LSSVM等方法。可见,所提方法在解决选煤厂煤灰分检测问题上具有较好的表现,为选煤厂提供了一种新的技术路径。Objective Coal plays a crucial role in China's economy and energy strategy as one of the main sources of energy and an important component of energy security.The ash content has always been a challenging issue for coal preparation plants to control product quality during the coal production process.By collecting and analyzing ash content detection data and maintaining stable ash content,the quality of coal washing products can be ensured,energy utilization can be improved,carbon emissions can be reduced,and environmental protection can be promoted.In China,rapid or slow ash methods are mainly used to detect ash content.This process takes 2-3 h,resulting in long detection cycles,low efficiency,and significant delays in obtaining detection results from coal sampling to analysis.In recent years,breakthroughs and progress have been made in online ash content detection technology.Naturalγ-ray measurement-based online detection technology has poor adaptability to different coal types,while X-ray absorption-based online detection technology offers high measurement precision and accuracy but is inconvenient for management and safety production.Therefore,there is a demand for a fast,accurate,safe,and real-time monitoring method for coal ash content in industrial production.Methods Terahertz spectroscopy is an emerging spectral technique that bridges the gap between microwave and infrared spectroscopy.It encompasses the physical,structural,and chemical information of substances within its frequency range,thus meeting the practical technological requirements of the coal industry.In this study,to address the prediction of coal ash content,46 samples were tested using a terahertz spectrometer to extract the absorption spectrum and refractive index spectrum of the coal samples.The absorption characteristics and refractive properties of different ash content samples in the terahertz frequency range were investigated.To eliminate the influence of sample thickness on the absorption coefficient,a method based on thickness model c

关 键 词：光谱学 太赫兹时域光谱技术 煤灰分 折射率 吸收系数 卷积神经网络 预测 

分 类 号：TQ533.6[化学工程—煤化学工程]