Evaluation of the Performance of Newly Developed Demulsifiers on Dilbit Dehydration, Demineralization, and Hydrocarbon Losses to Tailings
Bibliographic record
Abstract
The oil sands industry is continuously looking for demulsifiers that effectively dehydrate and demineralize diluted bitumen, minimize rag layer formation by controlling oil/water interface, and reduce naphtha and bitumen losses to tailings. In this paper, the performance of two newly developed demulsifiers, “X” and “Y”, were evaluated on the basis of diluted bitumen (dilbit) dehydration, demineralization, and naphtha and bitumen losses to tailings. The results demonstrate that the water removal efficiency of demulsifier Y is 14.15% higher than that of demulsifier X at 50 ppm dosage after 15 min of settling time. The solids removal efficiencies of demulsifiers X and Y at 50 ppm dosage after 15 min settling time, for the top dilbit fraction, were 13.9 and 21.5%, respectively. Demulsifier Y reduced the diluent and bitumen losses to the underflow by 16.7 and 13.8%, respectively, at 50 ppm dosage after 15 min of residence time as compared to demulsifier X. Therefore, demulsifier Y performed superior on all the key performance indicators (KPIs) studied as compared to demulsifier X. To determine the reason why the performance of demulsifier Y is superior to that of demulsifier X on all the KPIs, solids were collected from the original froth and the top, interface, and bottom fractions of the diluted froth after demulsification tests and characterized by X-ray diffraction analysis (XRD), X-ray energy dispersive spectrometry, scanning electron microscopy, particle size distribution (PSD), and wettability studies. XRD data shows that demulsifier Y reduced the clays, iron, and zirconium oxide minerals from the top and interface dilbit fractions when compared to the control sample. PSD data shows that demulsifier Y reduced most of the particles of size less than 0.50 μm from the interface. Therefore, demulsifier Y helps to resolve the interfacial material by removing the minerals that tend to form a rag layer, especially siderite, pyrite, magnetite, rutile, and anatase, from the oil/water interface to the underflow.
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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".