A mechanism study on laser-induced breakdown spectroscopy and machine learning-based characterization method for waste organic polymers.

Journal: Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA
Published Date: May 31, 2025

Abstract

The method based on machine learning and laser-induced breakdown spectroscopy (LIBS) is effective for rapid characterization of waste organic polymers (WOP). However, the lack of mechanistic interpretability leads to raises concerns regarding its reliability in practical applications. This study systematically investigated the fundamental chemical correlations between WOP fuel properties and LIBS spectral features through feature selection and machine learning interpretability analysis. Thirteen radical-associated key peaks were selected and strategically categorized into two groups for model construction. Under optimal conditions, the prediction accuracy for carbon, hydrogen, oxygen content and lower heating value (LHV) reach 97.74%, 91.22%, 91.28% and 97.02%, respectively. Notably, models utilizing 10 selected key peaks demonstrated superior performance compared to those employing raw LIBS spectra or principal components, especially with the absolute difference reaching 14.57% for O content prediction. Interpretability analysis showed that C2 swan bands had highest effects impacts on carbon, oxygen content and LHV prediction, whereas H I line was essential for hydrogen content prediction. This mechanistic investigation provided theoretical validation for LIBS-based rapid characterization systems, facilitating their practical implementation in downstream energy recovery processes. The established methodology offers a scientific foundation for advancing sustainable waste management and promoting circular economy development through efficient resource utilization.

Authors

  • Rui Liang
    School of Architecture and Urban Planning, Guangdong University of Technology, Guangzhou, Guangdong 510090, China.
  • Chao Chen
    Department of Neonatology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.
  • Junyu Tao
    Guangxi Scientific Research Center of Traditional Chinese Medicine, Guangxi University of Chinese Medicine, Nanning, Guangxi, China.
  • Wei Guo
    Emergency Department, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
  • Yaru Xu
    School of Mechanical Engineering, Tianjin University of Commerce, Tianjin 300134, China.
  • Xiaoling Hao
    School of Mechanical Engineering, Tianjin University of Commerce, Tianjin 300134, China.
  • Yude Gu
    School of Mechanical Engineering, Tianjin University of Commerce, Tianjin 300134, China.
  • Beibei Yan
    School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China; Tianjin Key Lab of Biomass Wastes Utilization/Tianjin Engineering Research Center of Bio Gas/Oil Technology, Tianjin 300072, China.
  • Guanyi Chen
    School of Mechanical Engineering, Tianjin University of Commerce, Tianjin 300134, China; School of Science, Tibet University, Lhasa 850012, China.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals