| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 1239486 | Spectrochimica Acta Part B: Atomic Spectroscopy | 2015 | 7 Pages |
•We develop an integrated and fully software controlled LIBS-based coal quality analyzer.•We propose a closed-loop feedback pulsed laser energy stabilization technology to enhance the measurement stability.•We use the spectral fitting with a combination of Lorentzian and Linear functions.•Support vector regression combined with principal component analysis are employed to realize more accurate measurement.
It is vitally important for a power plant to determine the coal property rapidly to optimize the combustion process. In this work, a fully software-controlled laser-induced breakdown spectroscopy (LIBS) based coal quality analyzer comprising a LIBS apparatus, a sampling equipment, and a control module, has been designed for possible application to power plants for offering rapid and precise coal quality analysis results. A closed-loop feedback pulsed laser energy stabilization technology is proposed to stabilize the Nd: YAG laser output energy to a preset interval by using the detected laser energy signal so as to enhance the measurement stability and applied in a month-long monitoring experiment. The results show that the laser energy stability has been greatly reduced from ± 5.2% to ± 1.3%. In order to indicate the complex relationship between the concentrations of the analyte of interest and the corresponding plasma spectra, the support vector regression (SVR) is employed as a non-linear regression method. It is shown that this SVR method combined with principal component analysis (PCA) enables a significant improvement in cross-validation accuracy by using the calibration set of coal samples. The root mean square error for prediction of ash content, volatile matter content, and calorific value decreases from 2.74% to 1.82%, 1.69% to 1.22%, and 1.23 MJ/kg to 0.85 MJ/kg, respectively. Meanwhile, the corresponding average relative error of the predicted samples is reduced from 8.3% to 5.48%, 5.83% to 4.42%, and 5.4% to 3.68%, respectively. The enhanced levels of accuracy obtained with the SVR combined with PCA based calibration models open up avenues for prospective prediction in coal properties.
