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Bulat L. Gatiatullin
Bulat L. Gatiatullin
Engineer
Saint Petersburg Mining University
Engineer
Saint Petersburg Mining University

Articles

Metallurgy and concentration
  • Date submitted
    2022-06-20
  • Date accepted
    2022-09-06
  • Date published
    2022-11-03

Adaptive approach formation using machine vision technology to determine the parameters of enrichment products deposition

Article preview

In this paper, an adaptive approach has been developed for automatic initialization of the thickening curve using machine vision technology, which makes it possible to determine with high accuracy the material parameters necessary for the design of thickening and clarification apparatuses. Software has been developed that made it possible to search for the coordinates of the condensation critical point in automatic mode. Studies on two samples of materials (tailings of apatite-containing ores and gold-bearing concentrate) were carried out and made it possible to statistically prove the reproducibility of the results obtained using the parametric criteria of Fisher and Bartlett. It has been established that the deposition curves are approximated with high accuracy by the Weibull model, which, together with the piecewise linear approximation, makes it possible to formalize the method for determining the critical point coordinates. The empirical coefficients of the Weibull model for two samples are found, and the final liquefaction and settling rates of the studied materials are determined.

How to cite: Romashev A.O., Nikolaeva N.V., Gatiatullin B.L. Adaptive approach formation using machine vision technology to determine the parameters of enrichment products deposition // Journal of Mining Institute. 2022. Vol. 256. p. 677-685. DOI: 10.31897/PMI.2022.77