Nutrient removal from anaerobically digested cattle manure by struvite precipitation
Bibliographic record
Abstract
This paper studied the removal of nutrients including phosphate, ammonium, and potassium from anaerobically digested and centrifuged manure effluents by struvite precipitation. Due to coexistence of high levels of NH4+ and K+ ions in manure effluents, struvite precipitation should include formation of MgNH4PO4·6H2O (struvite) and MgKPO4·6H2O (K-struvite). Nutrient removal experiments with MgCl2 as the magnesium source were conducted with and without phosphate addition to evaluate the influences of the Mg/PO43– ratio, pH, temperature, PO43–/NH4+ ratio, and reaction time. It was found that the required Mg/PO43– molar ratio was more than 5 times higher than the stoichiometric value of 1 when no phosphate was added. Meanwhile, the ammonium removal efficiency increased with the increase of phosphate addition. However, the ammonium removal from digested manure effluents by struvite precipitation is not highly efficient, as the removal efficiency reached only 56% even at a Mg/PO43–/NH4+ molar ratio of 1.5:1.25:1. Furthermore, the potassium removal efficiency was significantly lower than that of ammonium, and the molar percentage of the removed K+ over the total removed NH4+ and K+ was less than 10%. The composition analysis in the precipitated solids indicated that some phosphate in digested manure effluents be removed as magnesium phosphates other than struvite. In addition, different magnesium salts were tested for struvite precipitation. Their superiority for phosphate removal followed an order of MgCl2 > MgSO4 > MgO > Mg(OH)2 > MgCO3. Key words: phosphate removal, ammonia removal, potassium, struvite precipitation, digested cattle manure, magnesium.
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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 | 0.000 | 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".