Investigation of Fouling Mechanisms of a Light Crude Oil Using an Alcor Hot Liquid Process Simulator
Bibliographic record
Abstract
Fouling mechanisms of a light conventional crude were investigated by characterizing the crude oil, performing fouling tests using a bench-scale Alcor hot liquid process simulator (HLPS) unit and characterizing fouling deposits by means of elemental analysis, scanned electron microscopy (SEM), thermogravimetric analysis (TGA), and photoacoustic infrared spectroscopy (PAS-IR). In addition, a mathematical fouling model was developed under a laminar flow regime following Epstein’s methodology. Fouling tests were conducted at different temperatures and bulk velocities. Although the asphaltene content in the crude oil is low, the asphaltenes are still unstable because of a high saturate content and this crude oil has a high fouling propensity. On the basis of the fouling test results, fouling model analysis, and characterization of fouling deposits, the fouling mechanism of this crude oil can be explained as follows: In a laminar flow regime, unstable asphaltenes transport to the hot surface, become attached to the surface, and then, through chemical reactions, form fouling deposits. Mass transfer of entrained suspended particulates in the crude oil also contributes to fouling, although it is not the main cause. However, under turbulent flow conditions, such as those that prevail in industrial operations, it is expected that suspended particles would play a greater role in fouling.
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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 | 0.000 | 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".