Study on H <sub>2</sub> S Occurrence in Low Sulfur Coal Seams
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.003 |
| 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) | 0.000 | 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 it