Development of a Rapid Coal Analyzer Using Laser-Induced Breakdown Spectroscopy (LIBS)

Abstract

Determination of coal quality plays a major role in coal-fired power plants and coal producers for optimizing the utilization efficiency and controlling the quality. In this work, a rapid coal analyzer based on laser-induced breakdown spectroscopy (LIBS) was developed for rapid quality analysis of pulverized coal. The structure of the LIBS apparatus was introduced in detail. To avoid time-consuming and complicated sample preparation, a pulverized feeding machine was designed to form a continuously stable coal particle flow. The standard deviation (SD) of characteristic peaks was used to estimate the spectral valid data in this experiment. Coupled with cluster analysis, artificial neural networks and genetic algorithm are employed as a nonlinear regression method in order to indicate the relationship between coal quality and the corresponding plasma spectra. It is shown that the average absolute error of ash, volatile matter, fixed carbon, and gross calorific value for the validation set is 0.82%, 0.85%, 0.96%, and 0.48 MJ/kg. The average standard deviation of repeated samples is 1.64%, 0.92%, 1.08%, and 0.86 MJ/kg, showing a high sample-to-sample repeatability. This rapid coal analyzer is capable of performing reliable and accurate analysis of coal quality.

Ultra-repeatability measurement of calorific value of coal by NIRS-XRF

“…For example, Wang 6 et al investigated the intensity and stability of C and H lines in LIBS spectra that are highly related to the calorific value of coal in the atmosphere, helium and argon respectively, and conducted the quantitative analysis using partial least squares (PLS), and found that the signal to noise ratio as well as the prediction accuracy were the best in argon. Yao 7 et al used artificial neural networks and genetic algorithm to analyze the LIBS spectra of pulverized coal, and the average absolute error and standard deviation of calorific value were 0.48 MJ kg −1 and 0.86 MJ kg −1 , respectively. Although LIBS has great potential in online analysis and remote detection, its measurement repeatability is difficult to be further improved due to the laser pulse energy fluctuation, Rayleigh–Taylor instability, self-absorption effects, etc.…”

Development and industrial application of LIBS-XRF coal quality analyzer by combining PCA and PLS regression methods

“…In recent years, there are many reports on coal quality analysis by LIBS. Yao et al 6 combined cluster analysis, artificial neural networks (ANNs), and genetic algorithms (GA) to analyze the LIBS spectra of pulverized coal, and the mean standard deviation (SD) of the calorific value was 0.86 MJ kg −1 . Lu et al 7 also used the spectral analysis method of ANNs combined with GA to conduct LIBS analysis on the calorific value of coal, with an SD of 0.38 MJ kg −1 .…”

Detection of Carbon Content from Pulverized Coal Using LIBS Coupled with DSC-PLS Method

“…To evaluate the efficacy of a coal processing method and determine whether or not it is improving efficiency, the detection of coal composition is essential [1,2]. Methods such as X-ray fluorescence (XRF) and prompt gamma neutron activation analysis (PGNAA) have recently been used for coal analysis and measurements [3][4][5][6]. However, these methods can exert radiation hazards and are very expensive, so an alternative analysis method that is simultaneously safe and cost-effective is highly desired.…”