Aging of Water from Steam-Assisted Gravity Drainage (SAGD) Operations Due to Air Exposure and Effects on Ceramic Membrane Filtration
Bibliographic record
Abstract
The performance of first generation steam assisted gravity drainage (SAGD) plants for bitumen recovery has improved through operational experience, but there remain ample opportunities for the introduction of technologies that can further improve energy efficiency and plant reliability. Laboratory testing and validation is an important initial step in technology development. A major factor that determines the applicability and validity of testing results is the integrity of process water samples used in lab and field studies for this purpose. The results presented in this paper demonstrate aging of SAGD process water and its direct implication on membrane performance testing. Aging in samples collected after primary bitumen/water separation occurred mainly through reactions of dissolved organic species with air. This resulted in a gradual change in appearance, accompanied by a significantly higher tendency to foul membranes in dead-end filtration tests. The root cause for this change was proposed to be the reaction of phenolic species with oxygen, leading to more compressible and tightly packed filter cakes on the membrane surface. This effect was mitigated by minimizing air exposure during sample collection and handling. These results establish that preventing oxygen exposure to the sample is critical for maintaining sample integrity during a test program. Although this study focuses on filtration, aging effects can also lead to misleading results in laboratory testing of other water treatment processes and must be carefully considered during technology evaluation and development.
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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 | 0.001 |
| 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) | 0.001 | 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".