Performance Evaluation of a Newly Developed Demulsifier on Dilbit Dehydration, Demineralization and Hydrocarbon Losses to Tailings
Bibliographic record
Abstract
Abstract Chemical demulsification is a cost-effective, convenient, quick, and efficient method for breaking water-in-diluted bitumen emulsions in oil sands processing. The industry is always seeking demulsifiers that act as effective dehydrators and demineralizers, minimize rag layer formation by controlling oil/water interface, and reduce the diluent/bitumen losses to tailings. In this paper, the performance of a newly developed demulsifier “Z” was investigated for its performance on dilbit dehydration, demineralization, and hydrocarbon losses to tailings and compared against the first and second generation demulsifiers “X” and “Y”, respectively. The dehydration and demineralization efficiencies of demulsifier “Z” are 2.0 and 7.6%, respectively, higher than demulsifier “Y” and 6.4 and 10.7%, respectively, higher than demulsifier “X” at 50 ppm dosage after 15 minutes of residence time. Demulsifier “Z” also works better on the diluent and bitumen losses to the underflow as compared to demulsifier “X” and “Y”. To find the reason why demulsifier “Z” performs superior to “X” and “Y”, the solids were collected from the original froth and the top, interface, and bottom fractions of the diluted froth for characterization by X-ray diffraction analysis (XRD), X-ray energy dispersive spectrometry (EDS), scanning electron microscopy (SEM), and particle size distribution (PSD). XRD data shows that demulsifier “Z” reduced the amount of clays, iron, and zirconium oxide minerals from the top and interface dilbit fractions as compared to the control sample. PSD data shows that demulsifier “Z” reduced most of the particles of size less than 0.50 µm from the interface. Thus, demulsifier “Z” helps to resolve the interfacial material by separating the minerals that tend to form rag, especially siderite, pyrite, magnetite, rutile and anatase, from the oil/water interface and sends them to the underflow.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".