Abstract
The spatial distribution of the bed density plays a crucial role in enhancing the separation efficiency of coal and gangue in the Gas-solid Fluidized Bed Coal Beneficiator (GFBCB). To investigate the effects of operating conditions on the spatial distribution of the bed density, experiments were conducted in a 0.37 × 0.37 m square fluidized bed with a static bed height of 110 cm. Using a small fraction of Geldart C ultrafine coal particles to modulate the Geldart A magnetite particles, the resulting particle systems, the Geldart A-minus (or Geldart A−) particles, have been shown to be able to improve fluidization quality. The bed density can be adjusted from 2000 kg/m3 to 1600 kg/m3 by adding 0–23% volume fraction of Geldart C ultrafine coal particles. Measurements along the longitudinal and lateral directions revealed that the uniformity and stability of the fluidized bed were evidently improved by the addition of ultrafine coal particles. In contrast to conventional binary particles, Geldart A-minus (Geldart A-) particles offer a broader range of adaptability with respect to bed density and various other fluidization characteristics. A new predictive model based on Neural Network Algorithm techniques is also proposed to predict the spatial distribution of the bed density in the GFBCB with high accuracy, providing insight for the industrial design of coal beneficiators.
Original language | English |
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Article number | 119349 |
Journal | Powder Technology |
Volume | 436 |
DOIs | |
Publication status | Published - 1 Mar 2024 |
Externally published | Yes |
Keywords
- GFBCB
- Geldart A particles
- Neural network algorithm
- Predictive model
- Spatial bed density
ASJC Scopus subject areas
- General Chemical Engineering