Sensitivity of Asphaltene Properties to Separation Techniques
Bibliographic record
Abstract
Asphaltene properties vary with separation method and sometimes with individual technique. Factors such as contact time, solvent-to-crude oil ratio, and temperature influence asphaltene precipitation and are somewhat standardized. However, the final step in most separations, washing the asphaltene filter cake with solvent, is not standardized. Asphaltene properties can be very sensitive to small amounts of resins and therefore may be sensitive to the amount of washing. Asphaltenes were extracted with three different levels of washing from four source oils (Athabasca, Cold Lake, Lloydminster, and Peace River). In all cases, increased washing decreased asphaltene yield and slightly increased asphaltene density. Increased washing significantly increased molar mass and decreased the solubility of the extracted asphaltenes. A new washing method using a Soxhlet apparatus removed the largest amount of resinous material and yielded asphaltenes with significantly different properties from conventionally washed asphaltenes. Since more resinous material was removed, the Soxhlet method allows a more direct comparison between asphaltenes from different sources. Asphaltenes were also extracted using three standard separation methods, IP 143, ASTM D4124, and a method proposed by Speight. Some property variations between the methods were observed and a set of criteria to obtain consistent samples is proposed.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".