ASSESSING STRUVITE PRECIPITATION IN A PILOT‐SCALE FLUIDIZED BED CRYSTALLIZER
Bibliographic record
Abstract
The recovery of phosphates from biological wastewater treatment plants, through struvite crystallization, minimizes operational downtime and offers the potential for cost-effective recovery. The pilot-scale, fluidized bed reactor developed at the University of British Columbia (UBC) was found to be effective in recovering phosphate in the form of nearly pure struvite product, from an anaerobic digester centrate. The desired degree of phosphate removal was achieved by maintaining operating pH 8.0-8.2, and recycle ratio 5-9, to control the supersaturation conditions inside the reactor. The performance of the system was found to be optimal when the in-reactor supersaturation ratio was 2-6. In-reactor magnesium to phosphate molar ratio was found to be an important parameter to maintain system performance. In-reactor ammonium to phosphate molar ratio was also found to maintain a good correlation with phosphate removal. The influence of organic ligands on the struvite precipitation was investigated for a small molecular weight organic ligand, acetate, using a chemical equilibrium model PHREEQC. An acetate concentration below about 100 mg l(-1) was not found to affect the precipitation potential of struvite. Calcium and carbonate ion did not have any noticeable effect in struvite crystallization of struvite, under the operational concentrations utilized. Since the precipitation of calcium and carbonate compounds was controlled by kinetic factors, rather than thermodynamic solubility alone, the solid precipitates harvested were pure struvite, with undetectable impurities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".