Corrosivity Study of Sulfur Compounds and Naphthenic Acids under Refinery Conditions
Bibliographic record
Abstract
Abstract The potential corrosivity of crude oils is a major concern for refineries. Plant experience has shown that current methods based on crude sulfur content and total acid number (TAN) do not reliably predict corrosion rates. In particular, a better understanding of the relative importance of sulfidic and naphthenic acid corrosion mechanisms, when both are present, is needed to better predict crude corrosivity. Previous work focused on the influence of organic acid structure on corrosivity. In this paper, the relative corrosivities of different types of sulfur species are explored. Four model sulfur compounds were chosen based on relative thermal stability of carbon-sulfur bonds, which increased in the order of 1-octanethiol < dioctyl sulfide < diphenyl sulfide < dibenzothiophene. Corrosion rates for these compounds in white oil were measured for 1018 carbon steel (UNS(1) G10180) coupons in a test unit that simulated a vacuum distillation tower. Test conditions were varied, including temperature, total sulfur content, and whether or not naphthenic acids were present. The results were consistent with a sulfidic corrosion mechanism that depends on the release of hydrogen sulfide (H2S) by thermolysis of the carbon-sulfur bonds. When naphthenic acids were present, there was clearly competition between H2S and naphthenic acids for metal surfaces. Corrosion rates of three types of stainless steel (UNS S41000, S30400, and S31600) were then compared to that of the 1018 carbon steel.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".