Abstract
in the conditions of significant variability of processed polymetallic ores of the Akbastau Deposit, it is essential to minimize the variability of technological indicators of enrichment. Due to the multifactorial nature and non-linearity of the flotation process, the use of classical regression models does not provide the necessary level of reliability, therefore, there is a significant variability in the extraction of precious metals. To solve this problem, the paper substantiates the use of the neural network modeling methodology, which allows to estimate the variability of gold and silver extraction depending on the variation of the content of metals in the ore.
Highlights
The problem of variability is solved by calculating the regression dependence between the parameters of the source ore and the extraction of metal, but this approach does not always lead to positive results [1,2,3]
Due to the multifactorial nature and non-linearity of the flotation process, the use of classical regression models does not provide the necessary level of reliability, which is why there is a significant variability in the extraction of precious metals [4,5]
The correlation matrix shows a negative correlation between the content of precious metals in the ore and their extraction into the copper concentrate [10,11]
Summary
The problem of variability is solved by calculating the regression dependence between the parameters of the source ore and the extraction of metal, but this approach does not always lead to positive results [1,2,3]. Due to the multifactorial nature and non-linearity of the flotation process, the use of classical regression models does not provide the necessary level of reliability, which is why there is a significant variability in the extraction of precious metals [4,5]
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