Evaluation of the Performance of Air Dense Medium Fluidized Bed (ADMFB) for Low-Ash Coal Beneficiation. Part 2: Characteristics of the Beneficiated Coal
Bibliographic record
Abstract
Low-rank coals are widely used as fuel in coal-fired power plants. Continuing the use of these fuels is under huge pressure because of stringent environmental regulations. The air dense medium fluidized bed (ADMFB), which is a dry physical coal beneficiation method, can offer an efficient and economical solution for ash removal. The performance of the ADMFB separator in preparing higher quality coals has been studied by detailed characterization of the ADMFB clean coal products with minimum ash content, maximum organic material recovery, or different feed particle sizes in the present work. The percentage of ash removal for the selected samples with particle size of 1–13.2 mm was between 9 and 22% with respect to the feed sample. An increase of all CHNS components and higher heating value (HHV), regardless of the coal particle size, and a very efficient mercury rejection of between 33.7 and 48.6% were observed for most of the beneficiated samples. Inductively coupled plasma–mass spectrometry (ICP–MS) analysis confirmed the decrease of most of the hazardous elements, such as Pb, Ag, Ba, Cu, Mn, Be, and K, indicating a positive affinity of these elements with the ash-forming minerals of the coal. Some elements, such as As, Se, and Sb, exhibited inconsistent results, which indicates various degrees of organic bonding for these elements. X-ray fluorescence (XRF) analysis of the ashes of the beneficiated coals revealed different decreasing levels of Si, Al, and Mg oxides (main components of clay minerals) and an increase of Na, Ca, and Fe oxides. Lower viscosities, a lower reducing ash fusion temperature (< ∼1250 °C), and consequently, an increased slagging propensity based on a number of simple slagging indices for beneficiated products were obtained. The change in the reactivity of the clean coal products was discussed on the basis of the maximum rate of weight loss ( R max ) and the peak temperature ( T max ) obtained by differential thermogravimetry (DTG). The fine and middle size beneficiated samples showed various degrees of reactivity improvement. R max for the middle size was found to increase by at least 84.5%, and T max for the same was found to decrease by at least 62 °C.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".