Industrial at-line analysis of coal properties using laser-induced breakdown spectroscopy combined with machine learning

Weiran Song, Zongyu Hou, Weilun Gu, Hui Wang, Jiacheng Cui, Zhenhua Zhou, Gangyao Yan, Qing Ye, Zhigang Li, Zhe Wang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

33 Citations (Scopus)


Coal analysis is of great importance to improve coal combustion/utilization efficiency and operation safety and to reduce pollution. In this work, we develop a LIBS system for at-line coal analysis, which can pre-treat coal blocks into pressed pellets, acquire sample spectra and quantify coal properties automatically and continuously. A series of techniques are integrated in the system, including laser energy monitor and control, plasma modulation and collinear spectra collection. Moreover, the system has an integrated circuit board that is designed to precisely control the time-sequence of the hardware components; the overall design of the system ensures environmental factors are stabilised. These design considerations improve raw signal repeatability and signal-to-noise ratio. Furthermore, to improve the quantification accuracy without the use of LIBS physics knowledge, a new machine learning method is proposed, namely synergic regression (SR), which embeds a linear model in nonlinear regression. It inherits the high accuracy of nonlinear methods whilst being able to explain how specific variables contribute to the prediction. The system was demonstrated and evaluated in a real power plant for 10 weeks. The average prediction errors of calorific value (MJ/kg), sulphur (%) and volatile (%) were 0.299, 0.077 and 0.590, respectively. The evaluation demonstrated that the developed LIBS system meets the industry standards for at-line and in-situ coal analysis. Therefore, the LIBS system has significant potential impact on practices in coal utilization and similar industrial processes.

Original languageEnglish
Article number121667
Early online date14 Aug 2021
Publication statusPublished - 15 Dec 2021
Externally publishedYes

Bibliographical note

Funding Information:
This work was supported by National Natural Science Foundation of China (No. 51906124 and No. 61675110 ) and Shanxi Province Science and Technology Plan, China ( 20201101013 ).

Publisher Copyright:
© 2021 Elsevier Ltd


  • Coal properties
  • Coal-fired power plant
  • Laser-induced breakdown spectroscopy
  • Machine learning
  • Quantitative analysis
  • Regression

ASJC Scopus subject areas

  • General Chemical Engineering
  • Fuel Technology
  • Energy Engineering and Power Technology
  • Organic Chemistry


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