Abstract
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.
Highlights
In the 14th Five-Year Plan, China explicitly listed “carbon emissions after peaking, steady and declining” as its long-term goal for 2035
An ideal solution for green and low-carbon development of the mining industry is the use of the backfilling mining method, which is widely used in many mines because of its clean and efficient qualities and its ability to solve the problem of surface tailing waste storage [1,2,3]
The settlement velocity decreases, the interface tends to be stable, and the interface position remains unchanged for a long period of time
Summary
In the 14th Five-Year Plan, China explicitly listed “carbon emissions after peaking, steady and declining” as its long-term goal for 2035. In many works in the literature, the settling velocity of tailings is the average velocity after the solid–liquid interface becomes clear and can be discriminated. This method obviously cannot fully reflect the settlement process of tailings and has a significant impact on the calculation of subsequent settlement data. An unsupervised learning method is proposed to realize automatic tracking and identification of the solid–liquid interface, as well as cluster analysis of sediment and overflow water, so as to accurately and quickly measure the settling velocity of tailings. Identification Model of Tailings Settlement Velocity Based on Unsupervised Learning
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