Fouling Characteristics of a Light Australian Crude Oil
Bibliographic record
Abstract
Australian crude oils, which generally contain little asphaltenes, nevertheless give rise to fouling in refinery pre-heat trains. In this research, the fouling of a series of such crude oils and their blends is being assessed. The present work focuses on thermal fouling resulting from heating Gippsland crude oil at moderate temperatures. The oil is maintained under nitrogen at a pressure of 379 kPa and re-circulated at bulk temperatures of 80–120ˆC through an electrically heated annular probe at velocities in the range 0.25–0.65 m/s with surface temperatures from 180–260ˆC. Experiments are run for periods up to ninety hours at constant heat flux. Fouling is detected by the increase of wall temperature of the probe. The oil is characterized by its filterable solids content, density, and viscosity both before and after the fouling run. The trends in fouling rates are compared to predictions of the threshold-fouling model proposed by Ebert and Panchal [6] Ebert, W. A. and Panchal, C. B. 1995. “Analysis of Exxon Crude Oil Slip-Stream Coking Data”. In Fouling Mitigation of Industrial Exchange Equipment, Edited by: Panchal, C. B. pp. 451–460. New York: Begell House. [Google Scholar]. Data on deposit composition are presented, and the fouling mechanism is discussed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".