Effects of hydrothermal dewatering of lignite on rheology of coal water slurry
Bibliographic record
Abstract
Abstract Maximizing the dry‐based solid loading of coal particles in water is essential in increasing the burning efficiency of coal water slurry, which has been widely used as a liquid fuel. Understanding the rheology of coal water slurry could provide fundamental guidance on designing and optimizing coal water slurry formulation. The rheological studies have shown that coal water slurries made with lignite samples after hydrothermal dewatering (HTD) exhibit a stronger shear thinning behaviour as compared with those made with raw lignite samples. The viscosity of coal water slurry at the shear rate of 100 s−1 decreases with an increasing HTD temperature, which is probably due to the decrease of volume of lignite particles caused by the permanent reduction of both bound and non‐freezable water (inherent moisture) after the HTD process. The reduction of the inherent moisture of lignite samples after HTD treatment was elucidated by differential scanning calorimetry (DSC) under the temperature well below the freezing point. A lignite water slurry with a solid loading of 62 wt% db (dry basis) is obtained after hydrothermal dewatering at 300 °C with the addition of 1.2 wt% of polycarboxylate ether (PCE). Our findings indicated that hydrothermal dewatering of lignite has profound impacts on the inherent moisture of lignite and the rheological properties of coal water slurry.
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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 | 0.001 |
| 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".