Tuning Ionic Liquids for Simultaneous Dilution and Demulsification of Water-In-Bitumen Emulsions at Ambient Temperature
Bibliographic record
Abstract
Summary Thermal and flotation processes are widely used to produce bitumen from oil sand in Alberta. However, bitumen contains many surface-active components that tend to form water-in-oil (w/o) emulsion stabilized by fines and/or asphaltenes. Although several demulsifiers have been proposed in the literature to treat such emulsions, these chemicals are sometimes not effective. We propose ionic liquids (ILs) whose composition has been designed to enable effective treatment of these emulsions. Different ILs were synthesized and tested for their efficiency in treating bitumen emulsion obtained from a field in Alberta. ILs tested are mixtures of organic bases (primary and tertiary amines) with oleic acid. Mixtures of ILs and bitumen emulsion were prepared at several mass ratios. The two components were mixed under ambient conditions. After mixing, segregation of different components in the mixture was accelerated by centrifugation for rapid assessment of the degree of emulsion breaking. Optical microscopy, rheology, thermal gravimetric analysis, and viscosity measurements were used to assess the effect of ILs on bitumen emulsions. The first set of ILs with primary amine cations of different alkyl chain lengths (N-butylammonium oleate, N-octylammonium oleate) were able to separate the water from the emulsion. However, these ILs tended to form gels when mixed with water. The IL prepared from a tertiary amine with short alkyl chain length, triethylammonium oleate, also formed a gel with water. The number and length of alkyl chains proved critical for avoiding gel formation. ILs with tertiary amine cations of longer alkyl chain lengths (tri-N-butylammonium oleate and tri-N-octylammonium oleate) were immiscible with the separated water and did not gel. These ILs were very efficient in diluting and demulsifying bitumen emulsion. The emulsion droplet sizes increased upon addition of the IL. The IL mixes into the bitumen phase released from the emulsion, yielding a viscosity at an ambient temperature close to the pipeline specifications. This work demonstrates that ILs can be tailored to break bitumen emulsions effectively without heat input. The process developed in this paper can replace current practice for the demulsification and dilution of bitumen emulsions, which requires the emulsion to be heated significantly. Hence the IL process reduces the heat requirements and hence greenhouse gas emissions.
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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".