Changes in the physicochemical characteristics and spontaneous combustion propensity of Ximeng lignite after hydrothermal dewatering
Bibliographic record
Abstract
Abstract Spontaneous combustion behaviour and physicochemical characteristics of Ximeng lignite dewatered by hydrothermal dewatering (HTD) were investigated. In addition, effect of upgrading temperature, as well as the mechanism for evolution of spontaneous combustion propensity was discussed. The results showed that after HTD, fixed carbon content of lignite increased, whereas equilibrium moisture content and volatile content decreased; aromatic carbons increased relatively at the expense of oxygen‐containing functional groups and aliphatic hydrocarbons. Pore structures of lignite were developed by different extents after HTD. Spontaneous combustion propensity of upgraded lignite was evaluated using the crossing‐point temperature method. The results indicated that the spontaneous combustion propensity of HTD‐upgraded lignite significantly depended on upgrading temperature. When upgrading temperature was lower than 230 °C, spontaneous combustion propensity was raised due to development in pore structure. However, when upgrading temperature exceeded 230 °C, spontaneous combustion propensity was suppressed because active sites involved in coal oxidation were extensively removed. In conclusion, a higher upgrading temperature is recommended to extend the upgrading effect and to further suppress the spontaneous combustion propensity of lignite.
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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".