Mineral Processing
Fatemeh Kazemi; Ali akbar Abdollahzadeh
Abstract
This research work aims to explore the intricate mineralogy and texture of the tailing piles of iron ore processing plants to present a particle-based prediction for magnetite recovery. Three samples were taken from different points of tailings piles of an iron ore processing plant. Davis tube tests ...
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This research work aims to explore the intricate mineralogy and texture of the tailing piles of iron ore processing plants to present a particle-based prediction for magnetite recovery. Three samples were taken from different points of tailings piles of an iron ore processing plant. Davis tube tests were performed on each sample under various operating conditions. Process mineralogy studies were conducted to determine the mineralogy modal of the feed and product of each test. An Artificial Neural Network (ANN) model was used to make a model that related the grade and recovery of magnetite in the product to the mineralogy modal of the tailing piles. The magnetite grade and association index of feed, the magnetic intensity, and the water flow rate were the inputs to this network. The grade and magnetite recovery correlation coefficients were 0.954 and 0.86, respectively. The grade of magnetite in the feed emerged as a limiting factor on the grade and recovery of magnetite in concentrate. An increase of one unit in magnetite grade in the feed resulted in a 1.68 decrease in the recovery. The association index changes with the coefficients of -0.173 cause the changes in predicted magnetite recovery in the concentrate.
Mineral Processing
S. Mohammadi; B. Rezai; A. A. Abdolahzadeh
Abstract
Geometallurgy tries to predict the instability the behavior of ores caused by variability in the geological settings, and to optimize the mineral value chain. Understanding the ore variability and subsequently the process response are considered to be the most important functions of an accurate geometallurgical ...
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Geometallurgy tries to predict the instability the behavior of ores caused by variability in the geological settings, and to optimize the mineral value chain. Understanding the ore variability and subsequently the process response are considered to be the most important functions of an accurate geometallurgical study. In this paper, the geometallurgical index is presented as a new tool to optimize the mining activities. Geometallurgical index is described as any geological feature that makes a footprint on the process performance of ores. In a comprehensive research work at the Sarcheshmeh porphyry copper mine, the geological features that affect the main process responses including the product grade and recovery and plant’s throughput are subjected to investigation. In the current report, the rock hardness variability in terms of semi-autogenous grinding power index (SPI) and its effects on the mill throughput and energy consumption are presented. Ninety samples are collected based on the geological features including lithology, hydrothermal alteration, and geological structures. The samples are mineralogically characterized using XRD, XRF, and electron and optical microscopy. The Starkey laboratory mill, commercialized by Minnovex, is used to perform the SPI comminution test. The SPI results show a wide range of hardness, varying from 12 to 473 minutes. The correlation between the SPI results and the geological features show that lithology is a key geological feature that defines the hardness variability. In addition, the hydrothermal alteration would be an effective parameter in the period that the plant is fed with a single lithology.
