Fourier Transform Near Infrared Spectroscopy Analysis of Power Plant Coal Quality

Abstract

Studied the correlation of near-infrared spectra data and six coal indices, found ash and calorific value has low correlations with spectra data; then use dynamic principal components PLS method predict the coal index; this method could predict Volatile and Hydrogen content better, however, the ability to predict Carbon content and Nitrogen content is lower. It is found that using reflection spectroscopy analyzes the coal need a strong energy source, because the color of coal is deep and reflection is very weak, this leads to noisy signals. Only by improving the energy source could solve the problem of poor spectra data fundamentally; spectral data mining cannot fundamentally improve the quality of data. The current near-infrared reflection spectroscopy common platform such as BRUKER is not suitable for coal analysis, analysis of coal to be better need to develop special near-infrared measuring instruments.