LAPSE:2023.1466
Published Article
LAPSE:2023.1466
Online Ash Content Monitor by Automatic Composition Identification and Dynamic Parameter Adjustment Method in Multicoal Preparation
February 21, 2023
Abstract
The online measurement of coal ash has overcome the shortcomings of chemical tests. However, there could be large fluctuations and errors in the results of online ash monitors because of the transient change in coal quality resulting from different geological conditions in the mining process. In this study, to resolve the problems of the dual-energy γ-ray online ash monitor in the Linhuan Coal Preparation Plant, we investigated the internal factors, such as the composition of multimineral and multicoal, and external factors, such as the moisture and impurities, which affect the measurement results of the coal ash monitor. Furthermore, we developed a mathematical model to determine the effect of relevant factors on the coefficient of the online ash monitor, which revealed the relationship between coal composition and the parameters of the ash monitor, ensuring the stable and accurate measurement of ash in clean coal. The method of determining parameters used in the case of coal blending has been applied in the Linhuan Coal Preparation Plant. By comparing with tested ash content, the average absolute error and relative error for daily ash content measured in April are 0.21 and 2.18%, respectively. Meanwhile, it shows certain accuracy and reproducibility while opposed to the daily average absolute error of 0.22 and relative error of 2.39% in May.
The online measurement of coal ash has overcome the shortcomings of chemical tests. However, there could be large fluctuations and errors in the results of online ash monitors because of the transient change in coal quality resulting from different geological conditions in the mining process. In this study, to resolve the problems of the dual-energy γ-ray online ash monitor in the Linhuan Coal Preparation Plant, we investigated the internal factors, such as the composition of multimineral and multicoal, and external factors, such as the moisture and impurities, which affect the measurement results of the coal ash monitor. Furthermore, we developed a mathematical model to determine the effect of relevant factors on the coefficient of the online ash monitor, which revealed the relationship between coal composition and the parameters of the ash monitor, ensuring the stable and accurate measurement of ash in clean coal. The method of determining parameters used in the case of coal blending has been applied in the Linhuan Coal Preparation Plant. By comparing with tested ash content, the average absolute error and relative error for daily ash content measured in April are 0.21 and 2.18%, respectively. Meanwhile, it shows certain accuracy and reproducibility while opposed to the daily average absolute error of 0.22 and relative error of 2.39% in May.
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Keywords
dual-energy γ-ray, Linhuan Coal Preparation Plant, multicoal, multimineral, online ash monitor
Subject
Suggested Citation
Yu A, Liu H, Wang C, Lv J, Wang F, He S, Wang L. Online Ash Content Monitor by Automatic Composition Identification and Dynamic Parameter Adjustment Method in Multicoal Preparation. (2023). LAPSE:2023.1466
Author Affiliations
Yu A: State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, College of Material Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China
Liu H: State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, College of Material Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China
Wang C: State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, College of Material Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China; Key Laboratory of Coal Processing and Efficient [ORCID]
Lv J: State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, College of Material Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China
Wang F: State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, College of Material Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China
He S: State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, College of Material Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China
Wang L: State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, College of Material Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China
Liu H: State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, College of Material Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China
Wang C: State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, College of Material Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China; Key Laboratory of Coal Processing and Efficient [ORCID]
Lv J: State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, College of Material Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China
Wang F: State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, College of Material Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China
He S: State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, College of Material Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China
Wang L: State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, College of Material Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China
Journal Name
Processes
Volume
10
Issue
8
First Page
1432
Year
2022
Publication Date
2022-07-22
ISSN
2227-9717
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Original Submission
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PII: pr10081432, Publication Type: Journal Article
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LAPSE:2023.1466
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https://doi.org/10.3390/pr10081432
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