Estimation of Vinyl Chloride Emissions from Gasholders and Validation of In Situ Emission Reduction Methods
Bibliographic record
Abstract
Gasholders consist of a floating bell in a tank filled with water. Water provides a seal between the enclosed gas and ambient air. Gasholder emissions come from the contaminated water seal exposed to ambient air and the wet bell wall. The study objectives were to define parameters influencing gasholder emissions, determine the quantities of vinyl chloride (VC) emitted, generate a correlation equation between parameters of influence and mass emissions, and evaluate the efficiency of emission reduction methods. The research project was carried out on a laboratory-scale representation of a gasholder. The classic two-phase resistance model was used successfully to generate a correlation equation, which can be used to calculate the gasholder water seal emissions. A strictly empirical model was generated to estimate the wet wall emissions. Two in situ reduction methods were evaluated with the laboratory installations: floating objects and an oil layer. Both methods showed significant emission reductions, but the oil layer was the most effective. To reduce emissions even further, it is recommended that the water level of the gasholder be set to the lowest achievable level, that a windshield be placed around the water seal perimeter, and that hydrophobic paint be used on the bell wall.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".