Open AccessResearch on coal moisture analysis model based on near infrared spectroscopy
Author(s) -
Yating Zhou,
Quanjun Zheng,
Xiaoying Hu,
Hui Liang,
Dong Chen
Publication year - 2022
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/983/1/012092
Subject(s) - partial least squares regression , mean squared error , water content , calibration , coal , near infrared spectroscopy , moisture , artificial neural network , mathematics , linear regression , set (abstract data type) , statistics , pattern recognition (psychology) , artificial intelligence , computer science , materials science , chemistry , engineering , optics , physics , geotechnical engineering , organic chemistry , programming language , composite material
In order to make full use of the advantages of rapid and non-destructive measurement of coal moisture by near-infrared spectroscopy, the 29 original coal samples in the experiment were artificially humidified, and the moisture content of each coal sample was controlled within a different interval of 0-40%; diffuse reflectance near-infrared spectra were collected on the coal samples. The sample was selected according to the random method, Kennard-Stone (KS) method, and Rank-KS method for the selection of calibration set and prediction set. The prediction models of coal moisture content were established by multiple linear regression (MLR) and partial least squares algorithm (PLS) combined with different spectral pretreatment methods, furthermore, water modeling based on BP neural network was established according to optimal sample classification. The results show that when Rank-KS algorithm was used to select correction set and prediction set, the prediction ability of water content prediction model can be significantly improved by using either MLR or PLS, and the root mean square error of prediction (RMSEP) can be minimized; the moisture prediction model of the BP neural network algorithm based on the full-spectrum feature information is the best, the RMSEP value is the smallest among all models, and the model has the best prediction ability.