Phosphorus recovery from wastewater through struvite formation in fluidized bed reactors: a sustainable approach
Bibliographic record
Abstract
Recovery of phosphate as struvite (MgNH4PO4.6H2O), before it forms and accumulates on wastewater treatment equipment, solves wastewater treatment problems and also provides environmentally sustainable, renewable nutrient source for the agriculture sector. A pilot-scale fluidized bed reactor was used to recover phosphate through crystallization of struvite, from anaerobic digester centrate at the Lulu Island Wastewater Treatment Plant, Richmond, British Columbia, Canada. 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 in-reactor supersaturation ratio was 2-6. Among several other operating parameters, apparent upflow velocity and magnesium to phosphate molar ratio were also found important to maintain system performance, both in terms of efficiency of phosphate removal and recovery as struvite pellets. A narrow window of upflow velocity (400-410 cm/min) was found to be effective in removing 75-85% phosphate. TOC level inside the rector was found to affect the performance to some extent. The precipitation potential of struvite could be successfully predicted using a thermodynamic solubility product value of 10(-13.36) and its temperature dependence in PHREEQC.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".