Bibliographic record
Abstract
Abstract Mercaptans occur naturally in sour crude and condensate and cause an unpleasant odor which is a nuisance to operators and surrounding communities. Regulatory authorities often require control of mercaptan odors. A facility in Alberta stores condensate for use as a bitumen diluent. The condensate, which is stored in a large 110,000 barrel (bbl) (17,500 m3) holding tank, typically contains 150 to 400 ppm of total mercaptans (as sulfur). The odor resulting from these mercaptans can be detected off-site and is a nuisance to neighboring facilities. Acrolein was selected as a sulfide scavenger to reduce the mercaptan level in the condensate. At the time of treatment, the condensate had a total mercaptan level of 224 ppm. Impellers in the tank were started to mix the condensate, and then 9,260 liters of acrolein were injected into the bottom of the tank while mixing. This amount of acrolein was calculated to achieve a 50% reduction in total mercaptan content. The total mercaptan level in the condensate was actually reduced by 59%, from 224 ppm to 91 ppm (133 ppm scavenged). Acrolein scavenges all mercaptans, but preferentially reacts with the lighter mercaptans first. Ethyl and propyl mercaptan were reduced 88% and 53%, respectively. These light mercaptans were the most volatile and worst smelling mercaptans present, so their selective removal made the treatment efficient for odor reduction. Mercaptan odor was not detected at the site during the application even though the tanks were being stirred. After the application, mercaptan odors were significantly reduced and not detectable off-site. The condensate was not adversely impacted by the treatment and was able to be transported by pipeline for use in processing bitumen. This application demonstrates that condensate containing problematic levels of mercaptans can be successfully treated using acrolein, and subsequently be safely transported and used as a bitumen diluent.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".