Prediction of Cleaned Coal Yield and Partition Coefficient in Coal Gravity Separation Based on the Modified Hyperbolic Tangent Model

- Organization:
- Society for Mining, Metallurgy & Exploration
- Pages:
- 12
- File Size:
- 2551 KB
- Publication Date:
- Oct 15, 2022
Abstract
Accurate prediction of the partition coefficients in coal gravity separation is key to improving the preparation efficiency of
coal and promoting its clean utilization. In China, the approximate formula method (AFM) has been applied to the calculation
of coal gravity separation partition coefficients for decades, but its accuracy is limited. To improve the accuracy of the
predicted partition coefficients, and thus the product yield of coal preparation plants, the present study proposes a modified
hyperbolic tangent model to derive direct calculation formulas for the heavy medium and water medium separation partition
coefficients. Four groups of coal samples were used to verify the accuracy of the direct formulas, and their predictions were
compared with those of the AFM. The results showed that the partition coefficients and partition curves predicted by the direct
formulas were closer to the actual values than those predicted by the AFM. Further, when the cleaned coal ash content was
basically the same, the cleaned coal yield predicted by the direct formula was lower than that predicted by the AFM, and was
also closer to the actual value. These results demonstrate that the direct formula produces more accurate results compared to
the conventional AFM. As this formula has a reasonable theoretical basis, is simple to apply, and produces accurate results,
it is expected to help improve the design and management level of coal preparation plants.
Citation
APA:
(2022) Prediction of Cleaned Coal Yield and Partition Coefficient in Coal Gravity Separation Based on the Modified Hyperbolic Tangent ModelMLA: Prediction of Cleaned Coal Yield and Partition Coefficient in Coal Gravity Separation Based on the Modified Hyperbolic Tangent Model. Society for Mining, Metallurgy & Exploration, 2022.