Optimization of struvite pellet formation in a fluidized bed reactor : investigation of agglomeration and growth processes
Bibliographic record
Abstract
The precipitation of struvite (MgNH₄PO₄∙6H₂O) from phosphorus-rich wastewaters is well studied, but the agglomeration and growth mechanisms to form struvite pellets in fluidized-bed reactor (FBR) technologies are not well understood. Of primary concern is the unwanted production of fine struvite crystals that do not agglomerate and are generally lost to recovery. A pellet form is desirable as a final product; this can be used directly as a slow-release fertilizer and is important for the recovery process as it is easily separated from any colloidal material present in the wastewater. The purpose of this research was to increase the fundamental knowledge of struvite pellet formation and mechanisms contributing to it within the UBC FBR system, with the goal to maximize growth rates while preventing fines losses. New methods were developed to understand struvite-pellet growth and morphologies under simulated FBR conditions. Precipitation experiments were used to determine induction times and zeta potential values of nucleating and growing crystals. A pilot UBC-FBR and lab-scale flow cells were used for separating pellet growth and agglomeration processes, and performing individual crystal and pellet growth experiments, respectively. Growth and morphology data was obtained from scanning electron microscope images. Fundamental knowledge gained from this work includes: crystal growth was the dominant process found in pellet formation; a preferential, pellet branching structure was identified; repulsive forces of precipitating and growing struvite changes as solution conditions change; agglomeration can be independently controlled in an FBR; radial growth rates for individual crystals increase with an increase in concentrations, supersaturation ratio (SSR) and fluid velocity; and individual pellet growth shows an increase with higher relative fluid velocities. An overall FBR SSR between 2-6 is recommended to optimize the pelletization process. This will reduce crystal protrusions that could turn into fines while desirable pellet morphologies form, and also reduce repulsive forces that could prevent agglomeration. Higher relative fluid velocities can maximize growth rates and recover phosphorus at a faster rate, potentially reducing FBR footprints and/or changing technology designs. An alternate FBR operational process was proposed to maximize pellet growth and phosphorus recovery, while preventing fines losses by independently controlling growth and agglomeration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".