Moisture removal behaviour of single hard lignite particle during drying and quantitative characterization for its surface damage
Bibliographic record
Abstract
Abstract It is extremely meaningful to study the damage behaviour of lignite for safe operation during the drying process. In this study, a single hard lignite particle (SHLP) was prepared for drying. The drying characteristics of an SHLP were described, and its drying kinetics were studied from the perspective of the drying model and effective diffusion coefficient ( D eff ). The development of surface cracks in drying were obtained, and the changes in surface damage were quantitatively described by relative crack rate ( RCR ). The results showed that the drying process for an SHLP was divided into three stages following the slope of the drying curve. The rapid removal of a large proportion of moisture mainly occurred in stage I. The Page model is the best model for describing the drying of an SHLP. In addition, D eff increased with increasing drying time. High temperature promoted the moisture transfer from the inside to the surface of an SHLP. Finally, surface cracks on the SHLP developed rapidly in the early drying stage; specifically, the primary cracks widened and lengthened, and the small cracks mainly attached to the primary cracks. In the later drying stage, cracks shrank and cracks in the branch structure closed. High drying temperature made the RCR of an SHLP reach the maximum value more quickly in stage I.
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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".