Fundamental Study on Mercury Release Characteristics during Thermal Upgrading of an Alberta Sub-bituminous Coal
Bibliographic record
Abstract
An Alberta sub-bituminous coal was tested for the level of mercury removal obtained by a low-temperature thermal upgrading. The mercury removal characteristics, relative to increasing temperature and upgrading time, were determined. Rapid thermal upgrading at 400 °C released ∼72% of the mercury in the original coal, with a negligible total thermal energy loss. A corresponding increase in the calorific value of the upgraded coal was observed, from ∼20 900 kJ/kg to 25 900 kJ/kg. A further increase in the upgrading temperature, from 400 °C to 600 °C, produced only a minimal further increase in mercury removal. Rapid thermal upgrading at 400 °C resulted in a higher mercury removal efficiency than the temperature-programmable thermal upgrading. Thermal upgrading of sub-bituminous coal could be considered to be a viable option for mercury emission control. A volume reaction model of a first-order process was applied to simulate mercury removal from coal during the thermal upgrading. A comprehensive time constant was introduced in this model to describe the mercury release kinetics. Both the activation energy (22.6 kJ/mol) and the pre-exponential factor (8.65/min) for mercury release were determined by fitting the experimental isothermal data into this model. The determined parameters were used to predict the nonisothermic release of mercury during thermal upgrading experiments.
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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 | 0.000 |
| 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".