Interrelationships between Asphaltene Precipitation Inhibitor Effectiveness, Asphaltenes Characteristics, and Precipitation Behavior during <i>n</i>-Heptane (Light Paraffin Hydrocarbon)-Induced Asphaltene Precipitation
Bibliographic record
Abstract
Three carefully chosen chemicals dodecylbenzenesulfonic acid (DDBSA), nonyl phenol (NP), and toluene were studied for their asphaltene precipitation inhibition effectiveness during light-paraffin-hydrocarbon-induced asphaltene precipitation of three Saskatchewan crude oils, as well as to evaluate possible interrelationships between their inhibition effectiveness, asphaltene precipitation behavior (in terms of kinetics and equilibrium), and crude oil/asphaltene characteristics. Results showed that asphaltene precipitation rate dependence on asphaltene content ( m ) was a strong function of the content of heteroatoms (nitrogen (N), sulfur (S), and oxygen (O)) of both the crude oil and asphaltenes, as well as the aromatic carbon fraction and degree of branching of the alkyl side chain of the asphaltene molecules. On the other hand, the asphaltene precipitation rate dependence on the amount of n -heptane (i.e., light paraffin hydrocarbon) added ( n ), the frequency factor ( k 0 ), and the activation energy for asphaltene precipitation ( E a ) were strong functions of the paraffin fraction of the asphaltenes and the propensity of the asphaltene molecules for aggregation. Furthermore, the equilibrium parameter (onset point) increased as the paraffin fraction of the asphaltene molecules increased but decreased as the iron content of the oil increased. DDBSA was more effective with the least-aromatic medium oil, in terms of the kinetic parameters m and n, whereas it was more effective with the more-aromatic oil, in terms of the equilibrium parameter. A significant benefit obtained with NP and toluene was the drastic reduction of the rate constant ( k ), which resulted in a decrease in the overall rate of asphaltene precipitation. NP exhibited the maximum inhibition efficiency (∼10%), in terms of the onset point on the most-stable oil with the lowest iron content, and highest average number of carbons per alkyl side chain (i.e., high paraffin fraction) of the asphaltene molecules.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".