[Improving precision in coal moisture detection using wavelet transform].

Moisture, as a core determination of the economic value of coal, can result in the utilization and energy inefficiency. Near-infrared (NIR) spectroscopy, with advantages of high accuracy and low cost, provides significant solution to the quick and non-invasive detection of coal moisture. In the present paper, the improvement of the coal moisture analysis was conducted based on the precision of 1% and insufficient comparisons in recent experiments, and aspects of spectrum pretreatment and wavelength selection were mainly discussed. The optimized result with R-square of 0.995, RMSEC of 0.06% and RMSEP of 0.27% indicates the priority of wavelet decomposition and reconstruction, compared with other methods, in the noise reduction and baseline removing of original spectra (1 300-2 400 nm) before PLS modeling, and the stability experiment validates its robust potential in improving precision of coal moisture detection based on the NIR spectroscopy.