Bibliographic record
Abstract
Abstract There are Oil & Gas installations struggling with Mercury contaminated oil and gas streams. Mercury contaminants are not only toxic if exposed to staff, but it can also cause equipment damages and operational hazards. It is most often seen as an issue in the natural gas, especially in liquefaction operations, but there is an increasing interest and need to handle contaminated condensate streams, as well as glycol streams (MEG). The Mercury content in Oil & Gas streams vary significantly over the globe, even between specific fields. The Asia pacific region is generally known to be the most exposed, with Mercury levels often up to 100 times greater as an average, than in e.g. US or Canada Mercury rich wet gas wells will also have Mercury contaminated condensate, with Mercury present in different states and often adsorbed to other suspended particles. Up to now filtration processes for Mercury removal in condensate streams has primarily been used, however the presence of very fine particles, and Mercury levels in average several times above the target level, made filtration a troublesome offshore operation. In pursuit of better technologies, an improved solution has been found in disc stack centrifuge technology. The suitability has been assessed by using a fast and well-developed assessment process including laboratory tests and field pilot tests followed by full scale centrifuge installations. Particle sizes can be measured as a part of site testing. Mercury contaminants are typically small, often Hg resides in a relatively narrow particle size range, between 1–10 m micron, which explains why sufficient removal at process scale a can be achieved by filtration, but also why operation may be a challenging task with frequent filter blockages. A centrifuge processing system represent a separator option readily removing the Mercusry particle size range, including submicron particles, and can be made fully continuous, sealed and sized to handle required condensate flow processing. In average the stream leaving the centrifuge is found to be well below the acceptance level, moretowards concentrations typical for dissolved rather than particulate mercury level.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".