Study on H <sub>2</sub> S Occurrence in Low Sulfur Coal Seams
Bibliographic record
Abstract
Coal samples from the Shanxi Shaping coal mine were selected to investigate the occurrence of H 2 S in low sulfur coal seams. The adsorption mechanism of coal to H 2 S was explored, and an occurrence equation for H 2 S in coal seams was fitted through adsorption experiment results. The results showed that under ambient temperature and pressure conditions, the H 2 S adsorbed by coal reached equilibrium within 24 h. The increase in H 2 S concentrations and the moisture content of coal samples resulted in an increase in the adsorption capacity of H 2 S. Chemical adsorption of H 2 S by the coal also occurred. The total sulfur content in the coal increased, and water promoted the conversion from H 2 S to sulfur in coal. After adsorption, most of the H 2 S remains in the coal structure in the form of inorganic sulfur, such as sulfur hydride, iron sulfide sulfur, and monomeric sulfur, and a small proportion of H 2 S is bonded in the structure of the coal in the form of organic sulfur such as thiophene, C-S-C, and C-SH. Therefore, the higher the total sulfur content in coal, the greater the occurrence of H 2 S. The total amount of H 2 S increased exponentially with the concentration of free H 2 S and the moisture content of coal at equilibrium. This meant that the total amount of H 2 S in the coal seam could be estimated by fitting an equation according to the concentration of free H 2 S and the moisture content of coal seams. The concentration of free H 2 S decreased linearly with the increase in moisture content of the coal, therefore, the concentration of H 2 S in space could be reduced by injecting water into coal seams.
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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 | 0.001 | 0.001 |
| 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".