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Article

Tailings Settlement Velocity Identification Based on Unsupervised Learning

Lianhua Campus, College of Land and Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China
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Author to whom correspondence should be addressed.
Academic Editors: Lijie Guo and Antoni Roca
Metals 2021, 11(12), 1903; https://doi.org/10.3390/met11121903
Received: 20 October 2021 / Revised: 16 November 2021 / Accepted: 23 November 2021 / Published: 26 November 2021
(This article belongs to the Special Issue Green Low-Carbon Technology for Metalliferous Minerals)
In order to reasonably and accurately acquire the settlement interface and velocity of tailings, an identification model of tailing settlement velocity, based on gray images of the settlement process and unsupervised learning, is constructed. Unsupervised learning is used to classify stabilized tailing mortar, and the gray value range of overflow water is determined. Through the identification of overflow water in the settlement process, the interface can be determined, and the settlement velocity of tailings can be calculated. Taking the tailings from a copper mine as an example, the identification of tailings settling velocity was determined. The results show that the identification model of tailing settlement speed based on unsupervised learning can identify the settlement interface, which cannot be manually determined in the initial stage of settlement, effectively avoiding the subjectivity and randomness of manual identification, and provide a more scientific and accurate judgment. For interfaces that can be manually recognized, the model has high recognition accuracy, has a rapid and efficient recognition process, and the relative error can be controlled within 3%. It can be used as a new technology for measuring the settling velocity of tailings. View Full-Text
Keywords: settlement velocity measurement; K-means; tailings backfill; unsupervised learning settlement velocity measurement; K-means; tailings backfill; unsupervised learning
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MDPI and ACS Style

Xie, J.; Qiao, D.; Han, R.; Wang, J. Tailings Settlement Velocity Identification Based on Unsupervised Learning. Metals 2021, 11, 1903. https://doi.org/10.3390/met11121903

AMA Style

Xie J, Qiao D, Han R, Wang J. Tailings Settlement Velocity Identification Based on Unsupervised Learning. Metals. 2021; 11(12):1903. https://doi.org/10.3390/met11121903

Chicago/Turabian Style

Xie, Jincheng, Dengpan Qiao, Runsheng Han, and Jun Wang. 2021. "Tailings Settlement Velocity Identification Based on Unsupervised Learning" Metals 11, no. 12: 1903. https://doi.org/10.3390/met11121903

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