Experimental Investigation into Liquid and Solid Separation by an Industrial Mesh-Vane-Type Separator on Natural Gas at 4–5 MPa, Different Liquid Loadings and Gas Flows
Bibliographic record
Abstract
Abstract Inline vertical separators are commonly employed in natural gas transmission facilities (e.g., receipt stations) to filter liquid contaminants such as compressor oil, glycol, and free water from the gas stream. Manufacturers of these separators have claimed liquid removal efficiencies of 98–99% for droplet sizes ≥ 8 μm. However, these contaminants have been found invariably in piping systems downstream of these separators, suggesting inadequate separator performance. Currently, there is a lack of ability to verify manufacturer claims due to difficulties in quantifying liquid contaminants and droplet characteristics. The potential consequences of having such contaminants include lower gas quality, impaired gas metering accuracy, corrosion and damage to equipment/instrumentation, and adverse impacts on industrial and residential end users. This paper presents performance testing of a mesh-vane-type vertical separator conducted on high pressure, pipeline quality, natural gas in the range of 4–5 MPa and flow velocity in the range of 1.3–13 m/s in the DN150 separator inlet (hence turndown ratio of 10:1). Four different spray nozzles were used to inject and atomize industrial compressor oil with a liquid-to-gas mass loading ratio of 0.06–1.8%. Test results revealed that the average bulk efficiency separation performance for this separator is approximately 90.34%. This was found to be independent of the liquid-to-gas mass loading ratio. The effective Souders–Brown K-factor was found to be 0.15 m/s. This is below literature published data in the range of 0.27–0.3 m/s for horizontal flow, vane-pack-type separators. Liquid separation tests were also conducted following the injection of solid glass beads. Liquid separation efficiency decreased by approximately 9%, following the injection of 7 kg of industrial glass beads over a period of approximately 4 h. This was attributed to accumulation of solids in the vane pack.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".