Treating Waste with Waste: Activated Bauxite Residue (ABR) as a Potential Wastewater Treatment
Bibliographic record
Abstract
Bauxite residue (or red mud) is a highly alkaline waste generated during the extraction of alumina. As a result of the substantial accumulation of bauxite residue in tailings facilities, there is a growing interest in exploring the potential for reusing this material for other purposes. The main objective of this study is to evaluate the use of activated bauxite residue (ABR) for remediating oil sands process-affected water (OSPW) and as a supplement to municipal wastewater treatment through bench-scale, proof-of-concept studies. The ABR is produced through a reduction roasting process that alters the physicochemical properties of bauxite residue, resulting in the generation of potentially effective adsorbent media. The treatment performance via chemical and biological activity removals (cytotoxicity, estrogenicity, and mutagenicity) was also assessed. For OSPW, ABR treatment resulted in the effective removal of recalcitrant acid-extractable organics (AEOs), with kinetics following the pseudo-second-order and comparable adsorption capacity to other waste materials (e.g., petroleum coke). ABR also effectively reduced the estrogenicity and mutagenicity of OSPW, albeit cytotoxicity increased at higher dosages, possibly due to some components leaching out of the material (e.g., metals). For municipal wastewater, ABR treatment reduced fecal coliform concentrations (>99%), total phosphorus (up to 98%), total ammonia-nitrogen (63%), estrogenicity (nondetectable), and mutagenicity (nondetectable), especially in the primary effluent. The ultimate end use of ABR is for the recovery of valuable metals (especially iron) and as a construction material, but additional work is needed to optimize the dosage (currently in the g/L range) and maximize the use of ABR as an adsorbent prior to its subsequent uses.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".