4.3 Article

Optimization of stirred mill parameters for fine grinding of PGE bearing chromite ore

Journal

PARTICULATE SCIENCE AND TECHNOLOGY
Volume 39, Issue 6, Pages 663-675

Publisher

TAYLOR & FRANCIS INC
DOI: 10.1080/02726351.2020.1795016

Keywords

Chromite ore; PGE minerals; fine grinding; comminution energy; breakage characteristics; modeling

Funding

  1. Science and Engineering Research Board [SB/EMEQ-153/2014]

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The study conducted wet fine grinding experiments using low-grade chromite ore bearing platinum group of elements, and investigated the effect of various operating variables on the performance of stirred mills. Mathematical correlations were developed to predict product size and energy consumption, which were then optimized using quadratic programing to minimize the combination of product size and energy consumption.
The depletion of high-grade ores has forced the utilization of low-grade ores. The small liberation sizes in the low-grade ores require fine grinding which is an energy intensive operation. In the present study, low-grade chromite ore bearing platinum group of elements (PGE) was used as an experimental material. The previous study concluded the liberation size of around 1-50 mu m for chromite ore bearing PGE minerals. Therefore, the target product size for grinding was kept below 50 mu m. The wet fine grinding experiments were performed in a vertical stirred mill. Four significant operating variables stirrer speed, grinding time, feed size, and solids concentration were investigated for their effect on the performance of stirred mills. The performance of the mill was assessed in terms of achieving small product size at lower energy consumption. The detailed investigations were carried out according to popularly adopted methods of statistical design of experiment. The results revealed that stirrer speed, grinding time, and feed size played crucial role on the performance of stirred mill. Mathematical correlations were developed for predicting product size and energy consumption. The models developed were then optimized using quadratic programing to minimize the combination of product size and energy consumption.

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