Mercury Removal Characteristics during Thermal Upgrading of Fractionated Alberta Subbituminous Coal
Bibliographic record
Abstract
In response to mercury emission control from coal combustion flue gases, coal cleaning and thermal upgrading are being considered as precombustion mercury emission control options. In our previous study, dry coal cleaning using an air dense medium fluidized bed (ADMFB) was shown to reject a substantial fraction of mercury in original coal while retaining acceptable combustible recovery. In this study, mercury removal characteristics by thermal upgrading were studied using Alberta subbituminous coals fractionated by a air dense medium fluidized bed (ADMFB). It was found that the bottom 28% of the run-of-mine coal cleaned by ADMFB separator contained over 57% ash-forming mineral matters and 46% mercury. Mercury removal from this fraction of coal increased rapidly at temperatures over 206–329 °C. During thermal upgrading, the mass of coal decreased and the calorific values increased with increasing upgrading temperature. The observed weight loss and increase in the calorific value of upgraded coal at temperatures between 106 and 329 °C were attributed mainly to the evaporation of moisture which does not contribute to calorific value, with a small amount to the loss of combustible volatiles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".