Supersonic Ejectors for Hydrocarbon Emissions Capture
Bibliographic record
Abstract
Supersonic ejectors can be applied to capture low-pressure leakage gas from the gas seal vents of a centrifugal compressor. This captured gas can be re-injected into the fuel gas line of the gas turbine driver or the captured gas can be used as a fuel for gas fired utility heaters. By capturing the gas that is normally emitted to the atmosphere the operator can reduce operating cost and enjoy a reduction in hydrocarbon foot print. Because the supersonic ejector does not have moving parts, the system operating and maintenance costs are lower than functionally comparable traditional systems. In this study, a prototype of a supersonic ejector system was developed and tested at a pipeline compressor station. The obtained test data were used for developing and tuning a mean-line aerodynamic analysis tool, which predicts the ejector’s operating map. A family of three ejectors was designed to cover a range of operating conditions associated with gas turbine driven pipeline compressors. These ejectors were built, installed on a specially designed panel, described as the ejector system, and tested on inert gas at the original equipment manufacturer’s (OEM’s) facility. A comparison of predicted and as-tested supersonic ejector performance maps is discussed and conclusions are made about the system operating range.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".