Experimental Study on Dehydration Characteristics of the Wet-recycling Supersonic Separator
Bibliographic record
Abstract
The wet-recycling supersonic separator can effectively separate the liquids and hydrocarbon in natural gas from hydrocarbons, and separate the gas to recycling, thus improving separation efficiency. To carry out a systematic and comprehensive study on the dehydration performance of the device, an indoor test device has been built. The results show that, compared with the developed supersonic separator, higher dew point drop can be obtained by recycling supersonic separator under the same pressure loss ratio, which indicates a better dehydration separation performance. When the pressure loss ratio is 0.81, the maximum dew point drop of the device can reach 28.12℃. The pressure loss ratio is the key factor to determine the dehydration performance of the recycling supersonic separator. Appropriate increase of the pressure loss ratio within the allowable range is an effective way to improving the dehydration performance of recycling supersonic separator. Keeping the air flow in the Laval nozzle throat inside the recycling supersonic separator at critical state and reaching the critical flow rate is the lowest limit requirements to ensure the good working performance of the recycling supersonic separator, otherwise the device’s dehydration performance will be reduced. The wet gas outlet pressure has little effect on the dehydration performance of the recycling supersonic separator. Decreasing the wet gas outlet pressure could help attaining lower dew point or higher dew point drop, but the effect is not obvious.The conclusion can provide the reference for the structural optimization and field application of the wet-recycling supersonic separator.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".