Experimental and Modeling Study of Residual Liquid Recovery from Spent Sand in Bitumen Extraction Processes from Oil Sands
Bibliographic record
Abstract
Disposing solid residue with high liquid content into the environment may impact the immediate ecosystem and its surroundings. In bitumen recovery process from oil sands, it is environmentally and economically desirable to effectively recover as much of the liquid trapped in the spent solids as possible, prior to releasing it into the environment. An experiment was designed to investigate the effect of capillary force to enhance liquid recovery by using a thin, semipermeable layer as the membrane. The results indicate that by employing a membrane at the outlet, and pressurizing the air above the sand bed, the average liquid saturation can be decreased by 50%; however, the maximum pressure applied is restricted by the physical characteristics of the membrane. A mathematical model is developed to predict the liquid saturation profile along the sand pack during transient and steady-state conditions, and results are validated against measured average saturation using two different sand types. Results suggest that more liquid can be recovered from the spent sand bed by increasing the height of the bed; however, the required time to achieve the maximum recovery is increased as well. This method can be applied to reduce the liquid content of spent sand from any process before it is disposed of, thereby reducing possible hazards which may affect the environment.
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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) | 0.001 | 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".