Quantitative Analysis of Coal Quality by a Portable Laser Induced Breakdown Spectroscopy and Three Chemometrics Methods

Abstract

Laser-induced breakdown spectroscopy (LIBS) technology has the characteristics of small sample demand, simple sample preparation, simultaneous measurement of multiple elements and safety, which has great potential application in the rapid detection of coal quality. In this paper, 59 kinds of coal commonly used in Chinese power plants were tested by a lab-designed field-portable laser-induced breakdown spectrometer. The data set division methods and the quantitative analysis algorithm of ash content, volatile matter and calorific value of coal samples were carried out. The accuracy and prediction accuracy of three kinds of dataset partitioning methods, random selection (RS), Kennard–Stone (KS) and sample partitioning based on joint X-Y distances (SPXY), coupled with three quantitative algorithms, partial least squares regression (PLS), support vector machine regression (SVR) and random forest (RF), were compared and analyzed in this paper. The results show that the model featuring SPXY combined with RF has the best prediction performance. The R2 of ash content by the RF and SPXY method is 0.9843, the RMSEP of ash content is 1.3303 and the mean relative error (MRE) is 7.47%. The R2 of volatile matter is 0.9801, RMSEP is 0.7843 and MRE is 2.19%. The R2 of calorific value is 0.9844, RMSEP is 0.7324 and MRE is 2.27%. This study demonstrates that the field-portable LIBS device combining appropriate chemometrics algorithms has a wide application prospect in the rapid analysis of coal quality.

Keywords

• laser-induced breakdown spectroscopy
• portable device
• coal quality analysis
• data set partitioning
• PLSR
• SVR