Determination of ash content, volatile matter, and calorific value in coal by OLS combined with laser-induced breakdown spectroscopy based on PC recombination-Reference-Cited by-同舟云学术

Determination of ash content, volatile matter, and calorific value in coal by OLS combined with laser-induced breakdown spectroscopy based on PC recombination

Published:2023 Issue:9 Volume:38 Page:1851-1864
ISSN:0267-9477
Container-title:Journal of Analytical Atomic Spectrometry
language:en
Short-container-title:J. Anal. At. Spectrom.

Author:

Zhu Shengen¹^ORCID,Zhang Wenhao¹²^ORCID,Song Guangdong¹,Li Yadong³²,Hu Binxin¹,Zhu Feng¹,Zhang Hua¹,Wei Yubin¹,Sun Tengfei⁴,Tang Jing⁵

Affiliation:

1. Laser Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, 250014, China

2. Shanxi Key Laboratory of Advanced Semiconductor Optoelectronic Devices and Integrated Systems, Jincheng, Shanxi, 048000, China

3. Jincheng Research Institute of Opto-Mechatronics Industry, Jincheng, Shanxi, 048000, China

4. Centre for Advanced Laser Manufacturing (CALM), School of Mechanical Engineering, Shandong University of Technology, Zibo, Shandong, China

5. Shandong Huatow Environmental Protection Technologies, Jinan, Shandong, China

Abstract

The accuracy of LIBS coal quality detection was improved after PC recombination, and the main element was determined by interpretability tests.

Funder

Natural Science Foundation of Shandong Province

National College Students Innovation and Entrepreneurship Training Program

Publisher

Royal Society of Chemistry (RSC)

Subject

Spectroscopy,Analytical Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2023/JA/D3JA00147D

Reference41 articles.

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3. Temporally and spatially resolved study of laser-induced plasma generated on coals with different volatile matter contents

4. Review of methodological and experimental LIBS techniques for coal analysis and their application in power plants in China

5. LIBS analysis of the ash content, volatile matter, and calorific value in coal by partial least squares regression based on ash classification

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