A high‐throughput method for screening surfactant additives and structure–property relationships for the removal of water from bitumen
Bibliographic record
Abstract
Abstract This paper describes the development of a new high‐throughput (HT) method for screening surfactant additives for the removal of water from bitumen extracted from oil sands. The method begins by isolating bitumen froth from Canadian oil sands via the hot water extraction and flotation process. The froth is then diluted with naphtha to form “dilbit” The dilbit is homogenized and subsequently mixed twice to ensure a uniform distribution of water and sediment. Then, aliquots of the dilbit are dispensed into separate vials, and surfactant additives are mixed in at the desired concentrations. Next, the samples are transferred to centrifugation cells and centrifuged. Finally, the top third of the sample volume is removed, and Karl Fischer titration is used to measure the residual water. The HT method was used to screen the dewatering performances of 67 surfactants. Of the surfactants screened, (ethylene oxide)‐(propylene oxide)‐(ethylene oxide) (EOx‐POy‐EOx) triblock copolymer surfactants with molecular weight (MW) values >2000 Da and hydrophilic–lipophilic balance (HLB) values <16 were found to be the most effective demulsifying additives. The research approach presented here may enable the rapid development of structure–property relationships to guide the selection of surfactant additives for the improvement of commercial bitumen froth extraction and upgrading processes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".