Accuracy Enhancement of LIBS-XRF Coal Quality Analysis Through Spectral Intensity Correction and Piecewise Modeling

Abstract

The combination of laser-induced breakdown spectroscopy and energy dispersive X-ray fluorescence spectroscopy in the coal quality analysis was reported formerly. But in the practical test of the prototype instrument in the real power plant, the X-ray fluorescence signals suffered from intensity fluctuations over long-time measurements. The long-term signal fluctuations cause lower efficiency on the establishment of the calibration model and relatively larger root-mean-squared error of prediction (RMSEP) for unknown samples. Therefore, the spectral intensity correction was performed in the measurements; a randomly selected sample was measured several times in the whole measurements, including the modeling samples and unknown samples, recording the signal fluctuations and searching for a set of factors suitable for the intensity correction of a full-spectrum–based partial least square calibration model. In addition, as the signals of the coal samples of the power plant showed the potential of classification, the piecewise models were also established in case of further enhancement of the model or prediction accuracy. The RMSEPs of the calorific value, ash, volatile, and sulfur were lowered from 0.68 MJ/kg, 1.62%, 0.32%, and 0.24% to 0.51 MJ/kg, 1.34%, 0.16%, and 0.14% after spectral intensity correction, respectively. The piecewise modeling with spectral intensity correction achieved similar RMSEP for volatile and sulfur prediction but with more accurate models. The spectral intensity correction showed the ability to reduce the long-term signal fluctuation, and piecewise modeling also showed more efficiency in the model establishments for volatile and ash determination.

Application of Semi-Supervised Learning Model to Coal Sample Classification

“…Zheng et al [19] used this method to classify coal samples with a prediction accuracy of 96.7%. In addition to these classical machine learning algorithms, researchers have proposed many different methods, such as Piecewise Modeling [20] and Multiple-setting Spectra [21].…”

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

“…The combination of the two methods can not only measure organic elements in coal, but also measure the inorganic elements with high stability, thus forming a new coal quality analysis method with high measurement repeatability. We have previously used a chemometric regression algorithm combining principal component analysis (PCA) and PLS in experiments to verify the feasibility of this method, [26][27][28] and the measurement repeatability of the coal caloric value has met the requirements of national standard. It is worth mentioning that PCA is an unsupervised learning method that can not only adjust the combination of multivariate data information to extract fewer integrated variable features to explain most of the information obtained from the original data, but can also reduce the dimensionality of the high-dimensional data space by using the principle of minimal loss of data information.…”

Elemental and proximate analysis of coal by x-ray fluorescence assisted laser-induced breakdown spectroscopy

“…Recently, an XRF assisted LIBS method for high repeatability analysis of coal quality is proposed by our group, which not only inherits the ability of LIBS to directly analyze organic elements such as C and H in coal, but also uses XRF to make up for the lack of stability of LIBS in determining other inorganic ash-forming elements. We improved the accuracy of LIBS-XRF coal quality analysis through spectral intensity correction and piecewise modeling to solve the interference of x-ray emission intensity fluctuation on fluorescence signal in practical application [39]. The root-mean-squared errors of prediction (RMSEPs) of the calorific value, ash, volatile, and sulfur were reduced to 0.51 MJ kg −1 , 1.34%, 0.16%, and 0.14% after spectral intensity correction, respectively.…”