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Online in situ Diagnosis and Traceability of Rubber Combustion: Utilizing LIBS, Mass Spectrometry, and Machine Learning
Abstract
Аn online in situ detection system based on laser-induced breakdown spectroscopy (LIBS) is developed for the diagnosis and tracing of the rubber combustion process. The feasibility and accuracy of the system are verified by taking styrene-butadiene rubber, fluoro rubber, silicone rubber, chloroprene rubber, and natural rubber as samples. Metallic elements such as Ca, Mg, and Na are detected in the LIBS spectra of rubber combustion. The results show that the smoke produced contains different elements for different kinds of rubbers. Based on the self-developed single particle aerosol mass spectrometry system, the smoke produced by rubber combustion is detected by mass spectrometry. The mass spectrum data are compared and supplemented for LIBS spectral data. Then, the system, combining LIBS with principal component analysis and backpropagation artificial neural network, achieves efficient detection and identification of different rubber smoke, as well as tracking of the rubber combustion process. The identification accuracy reached 94.00%. The combination of LIBS and algorithm helps to improve the efficiency of processing a large amount of spectral information data. Furthermore, the element differences among various types of rubber slabs are analyzed, thereby validating the accuracy of the diagnosis and traceability system. The aforementioned results indicate that the online in situ diagnosis and traceability of the rubber in the combustion process with LIBS is feasible, and using spectral detection for different types of rubber recycling is also promising.
Keywords
laser-induced breakdown spectroscopy,
single particle aerosol mass spectrometry system,
Rubber combustion,
principal component analysis,
backpropagation artificial neural network
Supporting agencies: We would like to thank Miss Xiaomeng A for revising the article and Miss Junyi Zhu for assisting in drawing Fig. 1.
About the Authors
D. Tian
Anhui Mining Machinery and Electrical Equipment Coordination Innovation Center (Anhui University of Science and Technology); State Key Laboratory Cultivation Base of Atmospheric Optoelectronic Detection and Information Fusion, Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology
China
China
Dongpeng Tian
Huainan, Nanjing
G. Chen
Anhui Mining Machinery and Electrical Equipment Coordination Innovation Center (Anhui University of Science and Technology)
China
China
Gang Chen
Huainan, Nanjing
W. Zhou
State Key Laboratory Cultivation Base of Atmospheric Optoelectronic Detection and Information Fusion, Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET)
China
China
Wentao Zhou
Nanjing
L. Li
Institute of Mass Spectrometer and Atmospheric Environment, Guangdong Provincial Engineering Research Center for On-line Source Apportionment System of Air Pollution, Jinan University
China
China
Lei Li
Guangzhou
Y. Liu
Anhui Mining Machinery and Electrical Equipment Coordination Innovation Center (Anhui University of Science and Technology); State Key Laboratory Cultivation Base of Atmospheric Optoelectronic Detection and Information Fusion, Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology; Jiangsu International Joint Laboratory on Meteorological Photonics and Optoelectronic Detection, Nanjing University of Information Science and Technology
China
China
Yuzhu Liu
Huainan, Nanjing
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Review
For citations:
Tian D., Chen G., Zhou W., Li L., Liu Y. Online in situ Diagnosis and Traceability of Rubber Combustion: Utilizing LIBS, Mass Spectrometry, and Machine Learning. Zhurnal Prikladnoii Spektroskopii. 2025;92(3):417.
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