Evaluation of the Performance of Air Dense Medium Fluidized Bed (ADMFB) for Low-Ash Coal Beneficiation. Part 1: Effect of Operating Conditions
Bibliographic record
Abstract
Low-rank coals are widely used as fuel in coal-fired power plants. However, feeding such a coal in addition to lower efficiency generates a variety of problems mostly associated with the ash-forming minerals. An efficient dry coal cleaning method can offer a solution while avoiding problems associated with wet methods and generates partially dried coal. Air dense medium fluidized bed (ADMFB) can be an efficient and economical dry ash removal technology, but the comprehensive understanding on its performance is not yet openly available. In the present work, a detail study of the factors affecting the beneficiation of a low-ash lignite coal using ADMFB is provided. Response surface methodology supported by a central composite experiment design method is employed to study the effect of superficial air velocity ( V ), residence time ( T ), and bed height ( H ) on the performance of a batch ADMFB separator at three levels. Also, the effects of sample weights and particle size of lignite coal are separately studied. The system was found to effectively decrease the ash content of the clean coal product. The organic material recovery to clean coal product was affected negatively by H, V, and T, while T, H, and V were found to affect the separation efficiency positively. Various levels of interactions between parameters were also revealed and discussed. The optimum operating condition for maximizing the recovery was found to be 15 cm/s, 90 s, and 15 cm for V, T, and H, respectively. This condition led to a clean coal ash content, recovery, and separation efficiency of 10.6, 95.63, and 15.29%, respectively. The beneficiation test results also revealed that higher ash removal (23%) and recoveries (86%) are obtainable for coarser coal particles. Higher recovery and separation efficiency are achievable for larger sample weights (84.6 and 30.7%, respectively).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".