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Research on cross-instrument LIBS quantitative analysis model for coal properties based on TrAdaBoost transfer learning
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Xiangbo ZOU1, 2, Mumin RAO1, Gongda CHEN1, Shuwen TAN3, Shiwei QIN1, Cao KUANG1, Ji YE1, Shunchun YAO3, Huaiqing QIN3
Thermal Power Generation | 2026, 55(5) : 147 - 156
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Thermal Power Generation | 2026, 55(5): 147-156
• Thermal energy science research •
Research on cross-instrument LIBS quantitative analysis model for coal properties based on TrAdaBoost transfer learning
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Xiangbo ZOU1, 2, Mumin RAO1, Gongda CHEN1, Shuwen TAN3, Shiwei QIN1, Cao KUANG1, Ji YE1, Shunchun YAO3, Huaiqing QIN3
Affiliations
  • 1.Guangdong Energy Group Science and Technology Research Institute Co., Ltd., Guangzhou 510630, China
  • 2.Guangdong Energy Group Co., Ltd., Guangzhou 510730, China
  • 3.School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China
Published: 2026-05-25 doi: 10.19666/j.rlfd.202509008
Outline
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[Objective]

Laser-induced breakdown spectroscopy (LIBS) holds significant potential for application in the field of coal property analysis, due to its advantages of eliminating complex sample pretreatment, enabling multi-parameter synchronous detection, and offering rapid analysis. However, discrepancies in spectral responses exist among different instruments. These discrepancies cause severe accuracy degradation when a quantitative model trained on spectra acquired by a master instrument is applied to slave instruments. Therefore, this study constructed cross-instrument LIBS quantitative analysis models of coal property by integrating TrAdaBoost transfer learning with various machine-learning algorithms.

[Methods]

Two LIBS-based coal analyzers were designated as the master and slave instruments respectively, and LIBS spectra were collected from different numbers of coal samples on both devices. Random forest (RF), support-vector regression (SVR), and their TrAdaBoost-enhanced counterparts (TrA-RF and TrA-SVR) were employed to build quantitative analysis models. Model performance was evaluated by predicting the coal properties of unknown coal samples on the slave instrument.

[Results]

The results indicated that both TrA-RF and TrA-SVR models significantly outperformed their non-transfer counterparts. The TrA-RF model achieved the highest accuracy for calorific value, ash content, and carbon content. Compared with RF model, the mean absolute errors decreased from 1.390 MJ/kg, 4.774 %, and 3.826 % to 0.654 MJ/kg, 2.338%, and 1.927%, respectively. TrA-SVR model yielded the highest accuracy for volatile matter prediction. Compared with the SVR model, the mean absolute error decreased from 2.722% (SVR) to 2.524%.

[Conclusion]

These findings demonstrate that coupling transfer learning with an appropriate base learner markedly enhances the adaptability of LIBS-based coal property models across different instruments.

laser-induced breakdown spectroscopy  /  transfer learning  /  coal property  /  quantitative analysis  /  machine learning
Xiangbo ZOU, Mumin RAO, Gongda CHEN, Shuwen TAN, Shiwei QIN, Cao KUANG, Ji YE, Shunchun YAO, Huaiqing QIN. Research on cross-instrument LIBS quantitative analysis model for coal properties based on TrAdaBoost transfer learning[J]. Thermal Power Generation, 2026 , 55 (5) : 147 -156 . DOI: 10.19666/j.rlfd.202509008
Funding
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  • National Key Research and Development Program(2021YFF0601001; 2024YFC3909002)
  • Technological Innovation Project for New Power Systems in Guangdong Province(1688950422168)
  • Youth Science Fund Project of National Natural Science Foundation of China(22403032)
  • Joint Funds of the National Natural Science Foundation of China(U22B20119)
Appendix
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Year 2026 volume 55 Issue 5
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383
208
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Article Info
doi: 10.19666/j.rlfd.202509008
  • Receive Date：2025-09-03
  • Online Date：2026-08-14
  • Published：2026-05-25
Article Data
Affiliations
History
  • Received：2025-09-03
  • Revised：2025-10-03
  • Accepted：2025-10-15
Funding
National Key Research and Development Program(2021YFF0601001; 2024YFC3909002)
Technological Innovation Project for New Power Systems in Guangdong Province(1688950422168)
Youth Science Fund Project of National Natural Science Foundation of China(22403032)
Joint Funds of the National Natural Science Foundation of China(U22B20119)
Affiliations
    1.Guangdong Energy Group Science and Technology Research Institute Co., Ltd., Guangzhou 510630, China
    2.Guangdong Energy Group Co., Ltd., Guangzhou 510730, China
    3.School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China
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https://castjournals.cast.org.cn/joweb/rlfd/EN/10.19666/j.rlfd.202509008
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