# Using the results of the phase composition of magnetit ore for prediction of the concentrate yield

Authors: Pelevin A.E.

The purpose of the research was the development of a mathematical model for calculating the yield of magnetite concentrate with a given quality and calculating the mass fraction of iron in the tailings according to the phase composition of the original ore for iron. Predicting of the yield of concentrate with a given quality is possible using theoretical and experimental models. The predicted values of the mass fractions of total and magnetite iron in the tailings are calculated according to the equations of the technological balance with using the theoretical values of the concentrate yield. The analytical model for calculating the concentrate yield includes the sum of two components. The first component is characterized by the recovery of the magnetic phase of iron into the concentrate. The second component is characterized by the recovery of the non-magnetic phase of iron into the concentrate. The values of individual factors included in the analytical model are determined with high errors. Therefore, the analytical model was replaced by a regression model that includes experimental coefficients and two factors. The factors used were the mass fractions of total and magnetite iron in the ore. The absolute maximum error of prediction of the concentrate yield was ±0.31% at P = 95%. The mathematical model allows planning the mass fractions of total iron and magnetite in the ore in order to obtain the required concentrate yield and to identify the reasons for the decrease of the concentrate yield or an increase in the mass fraction of iron in the tailings.

Keywords: mathematical model, magnetite ore, phase composition, concentrate yield, extraction of iron into concentrate, mass fraction of iron, mass fraction of magnetite iron, tailings.
For citation:

Pelevin A. E. Using the results of the phase composition of magnetit ore for prediction of the concentrate yield. MIAB. Mining Inf. Anal. Bull. 2022;(5—1):131—144. [In Russ]. DOI: 10.25018/0236_1493_2022_51_0_131.

Acknowledgements:
Issue number: 5
Year: 2022
Page number: 131-144
ISBN: 0236-1493
UDK: 622.778
DOI: 10.25018/0236_1493_2022_51_0_131
Article receipt date: 12.11.2021
Date of review receipt: 31.03.2022
Date of the editorial board′s decision on the article′s publishing: 10.04.2022

Pelevin A. E., Dr. Sci. (Eng.), Associate Professor, Professor of the Department of Mineral Processing at the Ural State Mining University, ORCID iD https://orcid.org/0000-00016063-3932, a-pelevin@yandex.ru, Ural State Mining University, 620144, 30, Kuibyshev st., Ekaterinburg, Russia.

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