Effects of low molecular weight compounds in coal on the characteristics of its spontaneous combustion
Bibliographic record
Abstract
Low molecular weight compounds (LMWCs) account for 10–23 % of total coal mass. However, their role in coal's spontaneous combustion has not been well understood. In this paper, we first experimentally tested the effects of LMWCs on coal's oxygen consumption as well as heat release and transfer, and analyzed the components of LMWCs in coal, then we measured the oxidation characteristics of main active groups of LMWCs using model compounds method and last presented the mechanisms of LMWCs affecting coal spontaneous combustion. Raw coal with low metamorphic grade had greater oxygen consumption rate and the heat release intensity than the extracted residual coal, while the extracted residual coal of anthracite coal at the higher temperature had higher oxygen consumption rate and the heat release intensity than the raw coal. LMWCs could increase coal's thermal conductivity, lower coal's specific heat capacity, and shorten coal's self‐igniting period. Extraction of LMWCs could increase total pore volume and specific surface area of low rank coals. The extracted LMWCs had far greater, even one hundred more times higher, oxygen consumption rates than raw coal. Taken together, LMWCs could significantly promote the spontaneous combustion of low rank coal. Addition of substances that could dissolve LMWCs or inhibit their oxidation during water or cement injecting can significantly prevent spontaneous combustion of LMWCs‐rich coal mines.
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".