Ammonia removal from poultry manure leachate via struvite precipitation: a strategy for more efficient anaerobic digestion
Bibliographic record
Abstract
To improve poultry waste management, the feasibility of enabling efficient anaerobic digestion of poultry manure through reduction of ammonia accumulation is examined. This study employs struvite precipitation to control ammonia accumulation, and focuses on the efficacy of ammonia removal under neutral reaction conditions (pH = 7). The impacts of phosphate and magnesium additives, pH, temperature and the N:Mg:P molar ratio are quantified. Magnesium chloride (MgCl2 • 6H2O) and monopotassium phosphate (KH2PO4) are shown to be the most efficient combination of additives for total ammoniacal nitrogen (TAN) reduction of poultry manure leachate under neutral reaction conditions (pH = 7), demonstrating a TAN reduction of 90.3%. Modification of molar ratios (NH4:Mg:PO4) evidenced no significant benefit with regard to TAN reduction. However, increasing the fraction of supplementary magnesium resulted in a statistically significant (p < 0.05) decrease in phosphate concentration within the leachate. This study demonstrates the advantages of struvite precipitation, as a method of ammonia control, to improve anaerobic digestion and hence management of poultry manure. Although an effective means of TAN control, struvite precipitation from poultry manure is an ineffective means of obtaining pure struvite due to the formation of co-precipitates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".