Recovering Phosphorus from Human Urine by Electrochemical Precipitation Using Magnesium Alloy Tailings as Electrodes: Lab Scale and Pilot Scale Studies
Bibliographic record
Abstract
This study explored the recovery of phosphorus from human urine via electrochemical precipitation, employing magnesium alloy tailings as electrodes. The observed pH change during the process was primarily attributed to ammonium precipitation. However, the hydroxide produced by the cathode could potentially have a negative effect. There was a strong correlation between the amount of precipitate produced and pH, which can serve as a useful parameter for monitoring the effectiveness of the treatment. However, electrode corrosion significantly influenced the purity of the resultant struvite product. To counteract this, a device was designed to separate struvite from the electrode corrosion products using fluid flow, which led to an increase in purity. The pilot-scale application of magnesium electrodes proved to be an effective method for phosphorus recovery, achieving a high removal rate through adjustments to the residence time and current density. However, the limited source of wastewater resulted in a struvite production value that was considerably below the maintenance costs. Hence, the economic feasibility of this process necessitates further enhancement.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".