Bibliographic record
Abstract
The root cause of hydrotreater feed filter fouling in a bitumen upgrading plant was revealed through a step-by-step scientific investigation. It was first confirmed that the fouling problem was related to a process flow sheet change that introduced a heavy vacuum gas oil (HVGO) stream into the coker combined gas oil (KCGO) stream prior to filtration. Characterization of the foulant and the feed indicated that the fouling reactions are likely oxidative polymerization. Iron naphthenate or naphthenic acid in the HVGO stream could act as a catalyst for such a reaction. A bench-scale oxidation test was carried out to compare the oxygen uptake rates and the C 7 -insoluble contents after oxidation in KCGO, KCGO plus HVGO, KCGO plus iron naphthenate, and KCGO plus naphthenic acid streams. While the oxygen uptake kinetics for these samples were similar, the C 7 -insoluble contents for KCGO plus HVGO and KCGO plus iron naphthenate increased significantly after oxidation compared to the base case of KCGO. No significant increase of the C 7 -insoluble content was observed for KCGO plus naphthenic acid, indicating that it was the iron naphthenate that catalyzed the fouling reactions. Iron naphthenate was a corrosion product in the HVGO stream, which could be eliminated by preventing corrosion in the vacuum distillation unit. The filter fouling problem indeed disappeared after the installation of corrosion-resistant equipment.
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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".