Heterogeneous catalytic ozonation of naphthenic acids in water
Bibliographic record
Abstract
Abstract A major challenge the oil sands industries are facing is the urgent need to treat large amounts of oil sands process‐affected water (OSPW). OSPW is toxic to many species mainly due to the presence of naphthenic acids (NAs). Ozonation can significantly reduce both concentration and toxicity of the NAs. Unfortunately, this method suffers from its relatively high cost. To enhance NAs removal, we initiated heterogeneous catalytic ozonation of synthetic OSPW. Two types of catalysts were investigated: i) alumina supported metal oxides, and ii) unsupported catalysts. The alumina supported oxides included MnO2, MnO2/Co3O4, and MnO2/Li2O, while the unsupported catalysts were alumina and activated carbon (AC). All tested catalysts enhanced the ozone removal of NAs. Specifically, AC was found to be a very effective catalyst in addition to its adsorbability. AC significantly enhanced the removal of NAs and COD, the detoxification of the OSPW, and the biodegradability of NAs. For 85 % removal of NAs, the AC catalyzed ozonation needed 15 min whereas over 45 min were required by its non‐catalyzed counterpart. By using AC catalyzed ozonation, the efficiencies of detoxification and COD removal were both over four times and the biodegradability of the synthetic OSPW was over five times of those treated by non‐catalyzed ozonation. The mechanism study confirmed that due to the presence of AC the hydroxyl radical concentration increased 31 %, resulting in 26.4 % reduction in ozone consumption compared to non‐catalyzed ozonation. The removal of NAs was enhanced because of the higher oxidation power of hydroxyl radicals than molecular ozone.
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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".