O3/H2O2 and UV-C light irradiation treatment of oil sands process water
Bibliographic record
Abstract
The oil sands industry generates large volumes of oil sands process water (OSPW). There is an urgent need for OSPW treatment to reduce process water inventories and to support current reclamation approaches. This study discusses how efficient ozone (O3)-based combined advanced oxidation processes (AOPs), including hydrogen peroxide (H2O2) and UV-C, are at achieving mineralization while reducing the toxicity arising from such organic components as naphthenic acids (NAs) in OSPW. The results showed that the dissolved organic carbon (DOC) removals of 45%, 84%, 84% and 98%, obtained after 90-min treatments with O3, O3/H2O2, UVC/O3 and UVC/O3/H2O2, respectively, at a production rate of 6 g/L·h O3 were considerably higher than at lower O3 production rates. The acute toxicity on Vibrio fischeri was significantly reduced by all the treatments, which explains the high percentages of NA removal (up to 99% as confirmed by UPLC-QTOF-HRMS.) Mineralization (expressed as DOC removal) was highest with UVC/O3/H2O2 at ca. 2 mg C/L in the treated effluent, which means that it could be used as cooling/boiling process water in bitumen upgrading units. However, considering the energy demand of the treatments tested, the treatment using O3/H2O2 was found to be the most realistic for large-scale applications.
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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) | 0.002 | 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".