Adaptive approach formation using machine vision technology to determine the parameters of enrichment products deposition
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Saint-Petersburg Mining University
2022-11-01
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| Series: | Записки Горного института |
| Subjects: | |
| Online Access: | https://pmi.spmi.ru/index.php/pmi/article/view/15903?setLocale=en_US |
| _version_ | 1828058258415288320 |
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| author | Artem O. Romashev Nadezhda V. Nikolaeva Bulat L. Gatiatullin |
| author_facet | Artem O. Romashev Nadezhda V. Nikolaeva Bulat L. Gatiatullin |
| author_sort | Artem O. Romashev |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-10T21:24:26Z |
| format | Article |
| id | doaj.art-dffd850d574d4c59b3802819f8436ca0 |
| institution | Directory Open Access Journal |
| issn | 2411-3336 2541-9404 |
| language | English |
| last_indexed | 2024-04-10T21:24:26Z |
| publishDate | 2022-11-01 |
| publisher | Saint-Petersburg Mining University |
| record_format | Article |
| series | Записки Горного института |
| spelling | doaj.art-dffd850d574d4c59b3802819f8436ca02023-01-20T02:04:52ZengSaint-Petersburg Mining UniversityЗаписки Горного института2411-33362541-94042022-11-0125667768510.31897/PMI.2022.7715903Adaptive approach formation using machine vision technology to determine the parameters of enrichment products depositionArtem O. Romashev0https://orcid.org/0000-0003-3210-8000Nadezhda V. Nikolaeva1https://orcid.org/0000-0001-7492-1847Bulat L. Gatiatullin2https://orcid.org/0000-0001-6947-1909Saint Petersburg Mining UniversitySaint Petersburg Mining UniversitySaint Petersburg Mining UniversityIn 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.https://pmi.spmi.ru/index.php/pmi/article/view/15903?setLocale=en_USthickeningmachine visionweibull modelprecipitationcritical pointgold concentratetailsthickener calculation |
| spellingShingle | Artem O. Romashev Nadezhda V. Nikolaeva Bulat L. Gatiatullin Adaptive approach formation using machine vision technology to determine the parameters of enrichment products deposition Записки Горного института thickening machine vision weibull model precipitation critical point gold concentrate tails thickener calculation |
| title | Adaptive approach formation using machine vision technology to determine the parameters of enrichment products deposition |
| title_full | Adaptive approach formation using machine vision technology to determine the parameters of enrichment products deposition |
| title_fullStr | Adaptive approach formation using machine vision technology to determine the parameters of enrichment products deposition |
| title_full_unstemmed | Adaptive approach formation using machine vision technology to determine the parameters of enrichment products deposition |
| title_short | Adaptive approach formation using machine vision technology to determine the parameters of enrichment products deposition |
| title_sort | adaptive approach formation using machine vision technology to determine the parameters of enrichment products deposition |
| topic | thickening machine vision weibull model precipitation critical point gold concentrate tails thickener calculation |
| url | https://pmi.spmi.ru/index.php/pmi/article/view/15903?setLocale=en_US |
| work_keys_str_mv | AT artemoromashev adaptiveapproachformationusingmachinevisiontechnologytodeterminetheparametersofenrichmentproductsdeposition AT nadezhdavnikolaeva adaptiveapproachformationusingmachinevisiontechnologytodeterminetheparametersofenrichmentproductsdeposition AT bulatlgatiatullin adaptiveapproachformationusingmachinevisiontechnologytodeterminetheparametersofenrichmentproductsdeposition |