|
|
|
|
|
|
Variety Identification of Bulk Commercial Coal Based on Full-Spectrum Spectroscopy Analytical Technique |
REN Yu1,2, SUN Xue-jian2*, DAI Xiao-ai1, CEN Yi2, TIAN Ya-ming1, WANG Nan2, ZHANG Li-fu2 |
1. College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China
2. The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China |
|
|
Abstract To obtain the precise result, complex chemical analysis or complicated sample preparation is needed in universal coal analysis methods. In the paper, a new method to distinguish the type of bulk commercial coal using full spectroscopy which combined visible and near-infrared reflectance spectroscopy (Vis-NIRS) and short-wave infrared reflectance spectroscopy (SWIR) analytical technique and Multilayer Perceptron (MLP) classification method was advanced. The method was non-contact with no sample preparation and no chemical analysis. Besides, the classification information of coal can be quickly and efficiently obtained by this method. In the paper, the band range of original spectral data whose noise was excessive was deleted. The noise of remaining part was denoised by wavelet threshold denoising method. The spectral data pretreated was divided into three data sets: Vis-NIRS data set (500~900 nm), SWIR data set (1 000~2 350 nm) and full-spectrum data set (500~2 350 nm). Principal component analysis (PCA) was adopted in three datasets. The extracted principal components were entered in the MLP classification model. Multilayer perceptron was consist of input layer, hidden layers (two layers), softmax classifier. The contrastive study of classification accuracywas made among the three datasets. Random forest and Support Vector Machine (SVM) was used to verification analysis. The research showed: in the classification research of bulk commercial coal, because of the abundant data information of full-spectrum data, a better classification result can be obtained. When the number of training sample was 132, using the MLP classifier can achieve the highest classification accuracy which was 98.03%. The classification results of random forest and SVM verified the superiority and universality of the full spectrum dataset. The method provides reliable technical support for on-line analysis of coal and development of portable coal detecting instrument.
|
Received: 2017-05-14
Accepted: 2017-10-05
|
|
Corresponding Authors:
SUN Xue-jian
E-mail: sunxj@radi.ac.cn
|
|
[1] Hel L, Melnichenko Y B, Mastalerz M, et al. Energy Fuels, 2012, 26(3): 1975.
[2] Zhang Y, Zhang X L, Jia W B, et al. Applied Spectroscopy, 2016, 70(1): 101.
[3] SHAN Qing, ZHANG Xin-lei, ZHANG Yan, et al(单 卿,张新磊,张 焱,等). Journal of Nanjing University of Aeronautics & Astronautics(南京航空航天大学学报), 2015, 47(5): 767.
[4] Tahmasebi A, Yu J, Li X, et al. Fuel Processing Technology, 2011, 92(10): 1821.
[5] CHENG Dong, WEN He, TENG Zhao-sheng, et al(程 栋,温 和,滕召胜,等). Chinese Journal of Scientific Instrument(仪器仪表学报),2014,35(10):2263.
[6] LEI Meng, LI Ming(雷 萌,李 明). CIESC Journal(化工学报), 2012, 63(12): 3991.
[7] LI Ming, CHEN Fan, LEI Meng, et al(李 明, 陈 凡, 雷 萌,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2016, 36(9): 2793.
[8] WANG Ya-sheng, YANG Meng, LUO Zhi-yuan, et al(王雅圣, 杨 梦, 骆志远, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2016, 36(6): 1685.
[9] CHU Xiao-li(褚小立). Molecular Spectroscopy Analytical Technology Combined with Chemometrics and its Applications(化学计量学方法与分子光谱分析技术). Beijing: Chemical Industry Press(北京: 化学工业出版社), 2011.
[10] Mahmoudi J, Arjomand M A, Rezaei M, et al. Civil Engineering Journal, 2016, 2(1).
[11] Chakraborty M, Ghosh A. Computer Science, 2012, 60(13): 1.
[12] FANG Kuang-nan, WU Jian-bin, ZHU Jian-ping, et al(方匡南, 吴见彬, 朱建平, 等). Statistics & Information Forum(统计与信息论坛), 2011, 26(3): 32.
[13] DING Shi-fei, QI Bing-juan, TAN Hong-yan(丁世飞, 齐丙娟, 谭红艳). Journal of University of Electronic Science and Technology of China(电子科技大学学报), 2011, 40(1): 2.
[14] Wang Y, Yang M, Wei G, et al. Sensors & Actuators B Chemical, 2014, 193(3): 723.
[15] HE Xuan-ming(何选明). Coal Chemistry(煤化学). Beijing: Metallurgical Industry Press(北京: 冶金工业出版社), 2010. |
[1] |
WANG Cai-ling1,ZHANG Jing1,WANG Hong-wei2*, SONG Xiao-nan1, JI Tong3. A Hyperspectral Image Classification Model Based on Band Clustering and Multi-Scale Structure Feature Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 258-265. |
[2] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
[3] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
[4] |
FANG Zheng, WANG Han-bo. Measurement of Plastic Film Thickness Based on X-Ray Absorption
Spectrometry[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3461-3468. |
[5] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
[6] |
JIA Zong-chao1, WANG Zi-jian1, LI Xue-ying1, 2*, QIU Hui-min1, HOU Guang-li1, FAN Ping-ping1*. Marine Sediment Particle Size Classification Based on the Fusion of
Principal Component Analysis and Continuous Projection Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3075-3080. |
[7] |
CHEN Jia-wei1, 2, ZHOU De-qiang1, 2*, CUI Chen-hao3, REN Zhi-jun1, ZUO Wen-juan1. Prediction Model of Farinograph Characteristics of Wheat Flour Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3089-3097. |
[8] |
XUE Fang-jia, YU Jie*, YIN Hang, XIA Qi-yu, SHI Jie-gen, HOU Di-bo, HUANG Ping-jie, ZHANG Guang-xin. A Time Series Double Threshold Method for Pollution Events Detection in Drinking Water Using Three-Dimensional Fluorescence Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3081-3088. |
[9] |
JIA Hao1, 3, 4, ZHANG Wei-fang1, 3, LEI Jing-wei1, 3*, LI Ying-ying1, 3, YANG Chun-jing2, 3*, XIE Cai-xia1, 3, GONG Hai-yan1, 3, DING Xin-yu1, YAO Tian-yi1. Study on Infrared Fingerprint of the Classical Famous
Prescription Yiguanjian[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3202-3210. |
[10] |
CAO Qian, MA Xiang-cai, BAI Chun-yan, SU Na, CUI Qing-bin. Research on Multispectral Dimension Reduction Method Based on Weight Function Composed of Spectral Color Difference[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2679-2686. |
[11] |
ZHANG Zi-hao1, GUO Fei3, 4, WU Kun-ze1, YANG Xin-yu2, XU Zhen1*. Performance Evaluation of the Deep Forest 2021 (DF21) Model in
Retrieving Soil Cadmium Concentration Using Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2638-2643. |
[12] |
CHEN Wan-jun1, XU Yuan-jie2, LU Zhi-yun3, QI Jin-hua3, WANG Yi-zhi1*. Discriminating Leaf Litters of Six Dominant Tree Species in the Mts. Ailaoshan Based on Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2119-2123. |
[13] |
WANG Yu-hao1, 2, LIU Jian-guo1, 2, XU Liang2*, DENG Ya-song2, SHEN Xian-chun2, SUN Yong-feng2, XU Han-yang2. Application of Principal Component Analysis in Processing of Time-Resolved Infrared Spectra of Greenhouse Gases[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2313-2318. |
[14] |
HU Hui-qiang1, WEI Yun-peng1, XU Hua-xing1, ZHANG Lei2, MAO Xiao-bo1*, ZHAO Yun-ping2*. Identification of the Age of Puerariae Thomsonii Radix Based on Hyperspectral Imaging and Principal Component Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1953-1960. |
[15] |
LIU Yu-juan1, 2, 3 , LIU Yan-da1, 2, 3, SONG Ying1, 2, 3*, ZHU Yang1, 2, 3, MENG Zhao-ling1, 2, 3. Near Infrared Spectroscopic Quantitative Detection and Analysis Method of Methanol Gasoline[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1489-1494. |
|
|
|
|