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Identification of Coal Geographical Origin Using Near Infrared Sensor Based on Broad Learning

School of Information and Electrical Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(6), 1111;
Received: 11 February 2019 / Revised: 6 March 2019 / Accepted: 12 March 2019 / Published: 15 March 2019
(This article belongs to the Section Energy)
PDF [3002 KB, uploaded 20 March 2019]


Geographical origin, an important indicator of the chemical composition and quality grading, is one essential factor that should be taken into account in evaluating coal quality. However, traditional coal origin identification methods based on chemistry experiments are not only time consuming and labour intensive, but also costly. Near-Infrared (NIR) spectroscopy is an effective and efficient way to measure the chemical compositions of samples and has demonstrated excellent performance in various fields of quantitative and qualitative research. In this study, we employ NIR spectroscopy to identify coal origin. Considering the fact that the NIR spectra of coal samples always contain a large amount of redundant information and the number of samples is small, the broad learning algorithm is utilized here as the modelling system to classify the coal geographical origin. In addition, the particle swarm optimization algorithm is introduced to improve the structure of the Broad Learning (BL) model. We compare the improved model with the other five multivariate classification methods on a dataset with 243 coal samples collected from five countries. The experimental results indicate that the improved BL model can achieve the highest overall accuracy of 97.05%. The results obtained in this study suggest that the NIR technique combined with machine learning methods has significant potential for further development of coal geographical origin identification systems. View Full-Text
Keywords: near-infrared spectroscopy; coal; geographical origin identification; broad learning near-infrared spectroscopy; coal; geographical origin identification; broad learning

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Lei, M.; Rao, Z.; Li, M.; Yu, X.; Zou, L. Identification of Coal Geographical Origin Using Near Infrared Sensor Based on Broad Learning. Appl. Sci. 2019, 9, 1111.

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