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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".