The Safe Processing of High Naphthenic Acid Content Crude Oils - Refinery Experience and Mitigation Studies
Bibliographic record
Abstract
Abstract The economics of processing opportunity crudes is often so attractive that more and more refineries are updating their strategy for purchasing these difficult crudes. The experience gained from treating over 50 high acid crude units over the last 20 years is used to manage the risks of processing new opportunity crudes. When developing a strategy for processing opportunity crudes, you must consider the total impact on the refinery - both positive and negative. This paper shows an effective way to analyze opportunity crudes for potential negative impacts on the process. Risk managing techniques for corrosivity studies, desalter emulsion stability, fouling prediction and stability issues are reported. Listed are laboratory and field evaluations utilising on-line monitoring systems, corrosion probes and corrosion coupons. Proper monitoring strategy is critical to successfully managing the risk of processing opportunity crudes. Finally, the use of high temperature corrosion inhibitors was successfully evaluated as a means to mitigate naphthenic acid corrosion.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".