Real-time monitoring of carbon concentration using laser-induced breakdown spectroscopy and machine learning

The spectral analysis based on laser-induced breakdown spectroscopy (LIBS) is an effective approach to carbon concentration monitoring. In this work, a novel LIBS-based method, together with a system designed independently, was developed for carbon monitoring. The experiments were conducted in two modes: static and dynamic. In static monitoring, gases in three scenarios were selected to represent different carbon concentrations, based on which measurements of carbon concentrations were performed through a mathematical model. Then, K-nearest Neighbors (KNN) was adopted for classification, and its accuracy could reach 99.17%, which can be applied for the identification of gas composition and pollution traceability. In dynamic monitoring, respiration and fossil fuel combustion were selected because of their important roles in increasing carbon concentration. In addition, the simulation of combustion degree was performed by the radial basis function (RBF) based on the spectral information, where the accuracy reached 96.41%, which is the first time that LIBS is proposed to be used for combustion prediction. The innovative approach derived from LIBS and machine learning algorithms is fast, online, and in-situ, showing far-reaching application prospects in real-time monitoring of carbon concentrations.