Accuracy improvement of quantitative analysis of calorific value of coal by combining support vector machine and partial least square methods in laser-induced breakdown spectroscopy

Abstract

Laser-induced breakdown spectroscopy (LIBS) is a potential technology for online coal property analysis, but successful quantitative measurement of calorific value using LIBS suffers from relatively low accuracy caused by the matrix effect. To solve this problem, the support vector machine (SVM) and the partial least square (PLS) were combined to increase the measurement accuracy of calorific value in this study. The combination model utilized SVM to classify coal samples into two groups according to their volatile matter contents to reduce the matrix effect, and then applied PLS to establish calibration models for each sample group respectively. The proposed model was applied to the measurement of calorific values of 53 coal samples, showing that the proposed model could greatly increase accuracy of the measurement of calorific values. Compared with the traditional PLS method, the coefficient of determination (R2) was improved from 0.93 to 0.97, the root-mean-square error of prediction was reduced from 1.68 MJ kg−1 to 1.08 MJ kg−1, and the average relative error was decreased from 6.7% to 3.93%, showing an overall improvement.