Aerobic Granular Sludge for Treatment of Naphthenic Acids in Semi-Continuous and Batch Modes
Bibliographic record
Abstract
Wastewater from the Canadian mining oil sands industry is currently stored in tailings ponds, due to the difficulty in treatment of toxic recalcitrant compounds called naphthenic acids (NAs). The current project aimed at NA treatment using aerobic granular sludge (AGS) in two separate experiments. The first experiment was a proof-of-concept study aimed at assessing the shock response and treatability of commercial NAs over 21 days. It was conducted in three phases, i.e. introduction, starvation and monitoring. Each phase had chemical oxygen demand (COD) removal efficiencies of 54.8%, 23.9% and 96.1%, and NA removal efficiencies of 71.8%, 43.3% and 67.0%, respectively. Specific COD removal rates ranged between 2678 - 6864 g COD/m3/d, whereas specific NA removal rates ranged between 0.5-12.2 g NA/m3/d. These high rates were attributed to higher AGS biomass requiring higher COD consumption, and larger AGS surface area facilitating biodegradation and biosorption. The second experiment subjected mature AGS to three model NA concentrations (10, 50 and 100 mg/L), at three varying supplemental carbon source concentrations (600, 1200 and 2500 mg/L) in batch reactors. Cyclohexane carboxylic acid (CHCA), cyclohexane acetic acid (CHAA) and 1-adamantane carboxylic acid (ACA) were chosen to study structure-based degradation kinetics. The optimal COD was found to be 1200 mg/L. CHCA was removed completely with biodegradation rate constants increasing with lower NA concentrations and lower COD concentrations. CHAA was also removed completely, however, an optimal rate constant of 1.9 d-1 was achieved at NA and COD concentrations of 50 mg/L and 1200 mg/L, respectively. ACA removal trends did not follow statistically significant regressions; however, degradation and biosorption helped remove ACA up to 19.9%. Pseudomonas, Acinetobacter, Hyphomonas and Brevundimonas spp. increased over time, indicating increased AGS adaptability to NAs.
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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".