Forward osmosis for treatment of oil sands produced water: systematic study of influential parameters
Bibliographic record
Abstract
Steam-assisted gravity drainage (SAGD) is a thermally enhanced heavy oil recovery method which is widely practiced for the bitumen extraction from the oil sands in Alberta, Canada. This study is the first application of forward osmosis (FO) for the treatment of SAGD produced water with the intent to reuse the treated water. The effects of temperature, flow rate, and pH of the feed water (produced water) and concentration and flow rate of the draw solution (salt solution) on the water flux as well as undesired diffusion of organic matter toward the draw solution were studied. Since no interaction between parameters is predicted, a fully saturated L16 Taguchi design was used to investigate these five parameters each at four levels. It was found that increasing the feed water temperature and the draw solution concentration enhanced the water flux. The change in feed pH did not have any significant effect on water flux. Increasing the flow rate of both the BFW and the draw solution reduced the concentration polarization layer on both sides, thus increased the efficiency of the separation process. Analysis of variance showed that the feed water temperature and the draw solution concentration were the most influential parameters. This study provides valuable insights regarding the feasibility of the FO process for the treatment of oil sands produced water.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".