Why Crude Oil Vapor Pressure Should Be Tested Prior to Rail Transport
Bibliographic record
Abstract
Recent crude oil rail car accidents have forced US and Canadian authorities to issue Emergency Testing Orders to ensure safe transportation of crude oils. One crucial parameter in meeting these safety requirements is the testing of the vapor pressure (VP) of crude oil. This paper explains the impact of highly volatile components inside the crude oil on the vapor pressure measurement. It describes typical VP measurement errors and discusses guidelines and technology for proper crude oil classification. It offers measurement data to show the effect of sample outgassing and to describe the impact of temperature changes and the vapor-liquid ratio (V/L) on vapor pressure test results. The second part of the paper discusses methods to measure the True Vapor Pressure (TVP) and Bubble Point Pressure (BPP) of Crude Oils for safety purposes. Key words : Crude oil; Volatility testing; Safety data sheet; Hazardous material regulations (HMR); Emergency testing order; Vapor pressure (VP); ASTM D6377; Reid vapor pressure (RVP); ASTM D323; True vapor pressure (TVP); Vapor-liquid ratio (V/L); Bubble point pressure (BPP); Floating piston cylinder (FPC)
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".