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| Rapid Classification and Identification of Heavy Metal-Containing
Electroplating Sludge by Combining EDXRF With Machine Learning |
| LI Wei-yan1, TENG Jing2*, ZHENG Zhi-hui3, 4, SHI Jing-jing4, SHI Yao4*, LI Zhi-hong4, ZHANG Chen-mu4 |
1. School of Electrical Engineering, Tongling University, Tongling 244061, China
2. School of Medicine and Healthcare, Guangxi Vocational and Technical College of Industry, Nanning 530001, China
3. School of Chemistry and Chemical Engineering, Chongqing University, Chongqing 401331, China
4. CAS Key Laboratory of Green Process and Engineering, National Engineering Research Center of Green Recycling for Strategic Metal Resources, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China
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Abstract The rapid identification, classification, and pollution source tracing of hazardous wastes containing heavy metals is crucial to regional ecological and environmental quality supervision. This study used the energy-based X-ray fluorescence spectroscopy device (EDXRF) self-developed by the research group to collect spectral information of 8 different types of electroplating sludge from over 100 companies in Dongguan City. After spectral information noise reduction and data standardization, key classification factors were identified and used as input variables. The best method system for rapid X-fluorescence classification and identification of electroplating sludge containing heavy metals was determined through training and comparison of different machine models. The results show that the characteristic spectral line signals corresponding to the six metal elements of iron, copper, nickel, zinc, lead, and calcium can be used as a key factor to distinguish different types of electroplating sludge. Although random forest (RF), support vector machine (SVM), and linear discriminant (LDA) could achieve accurate classification and identification of electroplating sludge using X-ray fluorescence spectrum, only the RF model achieves 100% accuracy, precision, and sensitivity. The combination of machine learning and EDXRF technology can solve key problems such as the long, time-consuming, and poor timeliness of traditional chemical analysis methods for identifying hazardous wastes containing heavy metals. In the future, it will have broad application prospects in ecological environment monitoring and management such as rapid traceability of heavy metal pollution in soil and rapid identification of hazardous wastes containing heavy metals.
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Received: 2024-05-09
Accepted: 2024-12-06
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Corresponding Authors:
TENG Jing, SHI Yao
E-mail: liga0929@163.com；yaoshi@ipe.ac.cn
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