Phosphorus removal from anaerobically digested swine wastewater through struvite precipitation
Bibliographic record
Abstract
Phosphorus removal from agricultural wastewater streams is an important aspect of managing surface water quality, due to the contribution of phosphorus to eutrophication. Removal of phosphorus through struvite precipitation allows for its recovery as a potential fertilizer, and by determining the best conditions for struvite precipitation the removal process can be optimized. The effects of pH, Mg:P ratio, and time on struvite precipitation from anaerobically digested swine manure effluent were investigated. Effluent with Mg:P ratios from 1.0:1 to 1.6:1 were adjusted to pH values between 7.5 and 9.5 and left to equilibrate for 24 h. Results indicate that phosphorus removal increased with increasing pH and Mg:P ratio; the maximum phosphorus removal achieved was 80% at pH 9.0 and a Mg:P ratio of 1.6:1. The purest struvite precipitate was found at pH 7.5, with calcium carbonate and struvite precipitating at higher pH values. A continuously stirred batch of centrate was adjusted to pH 8.4 to determine the struvite formation rate constant. The rate constant was found to be 1.55 h(-1), with 17% phosphorus removal during the first 20 min. The results indicate that struvite precipitation could be a viable method of phosphorus removal from anaerobically digested swine manure.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".