The effect of cationic surfactants on bitumen's viscosity and asphaltene nanostructure under thermal partial upgrading
Bibliographic record
Abstract
Abstract Bitumen extracted from oil sands is a highly viscous fluid; thus, its transportation via pipelines in its original form resembles a major challenge. Partial upgrading is a recently proposed approach that aims to reduce bitumen's viscosity to meet the pipeline specifications. To optimize the process and make it more cost‐effective, a novel approach is proposed and researched in this study. Ionic surfactants were for the first time employed to promote thermal cracking reactions and dispersion of asphaltenes in bitumen at elevated upgrading temperatures. Three surfactants representing the cationic, nonionic, and anionic charges were studied at the thermal upgrading conditions (360–400◦C) at their optimal addition ratios to mimic the bitumen partial upgrading conditions. The results demonstrated that the cationic surfactant (dodecyltrimethylammonium bromide, DTAB) surpassed the other two surfactants and that with the addition of only 0.25 wt% of it, it can effectively reduce the viscosity of bitumen by up to 60% more than the upgraded bitumen with no additives under the same upgrading temperature. The Saturates, Aromatics, Resins, and Asphaltene analysis revealed that the contents of saturates and aromatics within the upgraded bitumen were also enhanced when DTAB was added. Moreover, the detailed asphaltene nanostructural analysis using X‐ray diffraction, high‐resolution transmission electron microscope, and thermogravimetric analysis revealed that the addition of the cationic surfactant (DTAB) resulted in an increased asphaltene nanostructural disorder, smaller polycyclic aromatic hydrocarbons size, and increased fringe curvature, as compared with the upgraded bitumen's asphaltene with no surfactants. Hence, the results have suggested that DTAB, a cheap and readily available ionic surfactant, can serve as a potential upgrading additive for designing partial upgrading procedures to produce upgraded bitumen with much less viscosity at lower upgrading temperatures. This upgrading technique will result in an upgraded bitumen that requires significantly reduced volumes of diluent for transportation.
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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 | 0.001 |
| 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".