Real-time in situ detection and source tracing of different soot

Abstract

Coal combustion produces large amounts of carbon dioxide, sulfur dioxide and metal ions. They are the main causes of greenhouse effect, acid rain and haze-fog respectively. It brings a great threat to the environment and human health. As a new photoelectric detection technology, laser-induced breakdown spectroscopy (LIBS) has become an important method in the field of environmental monitoring. In this paper, LIBS is applied to real-time in situ detection of soot from burning different coal. It is found that the main elements in soot are Al, Fe, Ca, Mg, Mn, Si, Sr and Na. In addition, the CN molecular bands can be observed. By comparing the spectra of different samples, differences between them are discussed. What’s more, principal component analysis (PCA) is used for data dimensionality reduction. After that, the data is distributed in a three-dimensional space, and then different kinds of soot are classified successfully. Combined with error back propagation training artificial neural networks (BP-ANN), the source tracing of soot from different coal are conducted, and the results are very good. The recognition accuracy is more than 92%. It is proved that this method is very promising for the detection and identification of soot. It is of great benefit to the environment and human health.