Individual micron-sized coal aerosol particle for quantitative analysis based on hollow laser trapping-LIBS and machine learning.

Fine particulate matter is one of the major pollutants emitted from coal combustion. Particulate matter and gaseous pollutants emitted from coal combustion are one of the main pollutants that are concerned. Laser-induced breakdown spectroscopy (LIBS) is a powerful and versatile analytical tool for real-time, simultaneous, and all-element detection. However, its broader applications are constrained by spectral interference, matrix effects, weak spectral intensity and so on. To address this challenge, multi-element quantitative analysis methods based on hollow laser trapping assisted LIBS signal enhancement of fiber collimated system were established by machine learning. The influence of five different spectral preprocessing methods and three different variable selection methods on the prediction performance of the RF calibration model was investigated. The Savitzky-Golay convolution derivative-variable importance projection-random forest (SG-VIP-RF) (Fe) and first-order derivative-variable importance measurement-random forest (D1st-VIM-RF) (Ca) calibration models were constructed based on the optimal spectral preprocessing method and variable selection method. The prediction performance of Fe and Ca elements are shown as follows: Fe (Rp2 = 0.9861, MREP = 0.0477, RMSEP = 1.408 %) and Ca (Rp2 = 0.9580, MREP = 0.0627, RMSEP = 2.1461 %), and their relative standard deviation (RSD) values are 2.2 % and 5.2 %, respectively. The results demonstrate that the RF calibration model based on the optical fiber collimated LIBS signal enhancement method is successfully applied to the quantitative analysis of standard coal samples. It is expected to provide theoretical basis and technical support for in-situ online rapid monitoring of coal-fired energy materials, and further promote the wide application of LIBS in on-line monitoring fields such as environmental monitoring, geological exploration and metallurgical analysis, adhere to green and low-carbon sustainable development, and help promote the goal of carbon peak carbon neutralization.