Agglomeration-assisted demulsification in naphthenic froth treatment of oil sands: Synergy between wettability modifiers and emulsion breakers
Bibliographic record
Abstract
Chemical demulsification using emulsion breakers (EBs) is the primary method in naphthenic froth treatment of oil sands production. However, conventional EBs often struggle to fully remove highly stable interfacial fine particles and water droplets from bitumen. This study selects ethyl cellulose (EC) as the emulsion breaker and introduces wettability modifiers to bitumen froth before adding EBs, aiming to enhance the agglomeration of fine solids and the coalescence of small water droplets through synergistic effects. Agglomeration-assisted demulsification bottle tests were conducted by combining wettability modifiers with EBs, and the separated bitumen and solids were characterized. Experimental methods and density functional theory (DFT) calculations were employed to uncover the underlying mechanisms. The results demonstrated that the combination of 50 ppm poly(ethylene glycol)- block -poly(propylene glycol)- block -poly(ethylene glycol) copolymer (PEG-PPG-PEG) as the wettability modifier and 200 ppm EC was the most effective, simultaneously reducing water content to below 0.5 wt% and solid content to below 0.1 wt% in the separated bitumen. Effects of PEG-PPG-PEG and EC on interfacial properties and droplet coalescence were demonstrated. The adsorption, desorption, and agglomeration behaviors of PEG-PPG-PEG on bitumen-coated surfaces were confirmed, with DFT simulations indicating that PEG-PPG-PEG adsorbs on solid surfaces through ionic and hydrogen bonds. This adsorption alters the wettability of solids, facilitating their movement from the bitumen-water interface and increasing the efficiency of EBs by increasing interfacial accessibility. As a result, EBs synergize with wettability modifiers to more effectively agglomerate fine solids and coalesce small water droplets, thereby enhancing bitumen-water separation. This work provides fundamental insights into the role of wettability modifiers in agglomeration-assisted demulsification, offering practical guidance for industrial applications. • PEG-PPG-PEG and EC synergize to enhance solids agglomeration and water coalescence. • The combination of 50 ppm PEG-PPG-PEG with 200 ppm EC is the most effective. • PEG-PPG-PEG and EC break interfacial films and reduce interfacial rigidity. • Multiple techniques reveal the adsorption and agglomeration mechanisms of PEG-PPG-PEG. • PEG-PPG-PEG adsorbs on kaolinite via ionic and hydrogen bonds.
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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) | 0.000 | 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 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".