Biochar-enhanced removal of naphthenic acids from oil sands process water: Influence of feedstock and chemical activation
Bibliographic record
Abstract
Bitumen extraction generates massive volumes of oil-sands-process-water (OSPW), which are stored in tailings ponds in Alberta, Canada, requiring treatment to reduce freshwater use and ensure safe discharge. Naphthenic acids-NAs, which are toxic and persistent organic contaminants, pose major challenges. While biochars show promise for adsorption, their effectiveness for real-OSPW remains largely unexplored, as most studies focus on model compounds. This study investigated the adsorption of NAs from real-OSPW via biochars-derived from municipal sludge and peat—a novel feedstock in this context—with and without chemical activation, including a dual FeCl 3 +ZnCl 2 strategy not previously evaluated for OSPW treatment. The results demonstrate that feedstock type and activation methods significantly influence biochar properties and adsorption performance. Chemical activation increased mesopore-volume and surface functional-groups (-OH and C C), improving adsorption capacity. Pristine-biochars removed ∼23 % of total-NAs, whereas FeCl 3 +ZnCl 2 -activated biochars achieved >90 % removal of both classical-&-oxidized-NAs. The FeCl 3 -activated biochars exhibited higher selectivity for NAs with larger carbon numbers, whereas FeCl 3 +ZnCl 2 -activated biochars efficiently removed a broader range of NAs. The study also revealed that biochars performed better in real-OSPW than in model compound solutions, highlighting the necessity of testing adsorbents in actual-wastewater matrices. Among biochar properties, mesopore-volume identified as the most critical factor for maximizing NA removal, emphasizing the importance of pore-structure over total surface area. These findings suggest that FeCl 3 +ZnCl 2 -activated biochars are promising materials for OSPW treatment. Future research should focus on integrating biochar into passive treatment systems e.g., wetlands and exploring its potential for land reclamation and carbon sequestration in oil and sand remediation.
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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) | 0.000 | 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".