Rapid detection of the total moisture content of coal fine by low-field nuclear magnetic resonance

•Rapid detection of the total moisture content of coal fine by LF-NMR was proposed.•The output signal of LF-NMR increased with moisture content of coal increased.•The equation of output signal fitting to moisture content was second-order linear.•The first peak point value predicted the moisture cont...

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Bibliographic Details
Published in:Measurement : journal of the International Measurement Confederation 2020-04, Vol.155, p.107564, Article 107564
Main Authors: Mao, Yuqiang, Xia, Wencheng, Xie, Guangyuan, Peng, Yaoli
Format: Article
Language:English
Subjects:
Amplitudes
Calorific value
Coal
Coal fine
Coal mining
LF-NMR
Linear equations
Moisture
Moisture content
NMR
Nuclear magnetic resonance
Rapid detection
Total moisture content
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Summary:•Rapid detection of the total moisture content of coal fine by LF-NMR was proposed.•The output signal of LF-NMR increased with moisture content of coal increased.•The equation of output signal fitting to moisture content was second-order linear.•The first peak point value predicted the moisture content of coal more accurate.•This method predicted the moisture content of coal efficiently and accurately. Coal total moisture content has an observable influence on its calorific value. In this investigation, a rapid detection method of total moisture of coal fine through 1H low-field nuclear magnetic resonance (LF-NMR) was proposed. The first peak point value and total signal amplitude analyzed by T2 spectrum were used to fit with the total moisture content of coal. The first peak point value and total signal amplitude increased as the total moisture content of coal increased. The fitting results of both first peak point value and total signal amplitude to the total moisture content of coal agreed with the second-order linear equation best. The first peak point value had higher prediction accuracy than the total signal amplitude and hence it is used preferentially to predict coal moisture content. This method provides a new and effective tool for online detecting the moisture content of coal in the coal preparation plant.
ISSN:0263-2241
1873-412X
DOI:10.1016/j.measurement.2020.107564