Struvite-Driven Integration for Enhanced Nutrient Recovery from Chicken Manure Digestate
Bibliographic record
Abstract
This study investigated the synergistic integration of clean technologies, specifically anaerobic digestion (AD) and struvite precipitation, to enhance nutrient recovery from chicken manure (CM). The batch experiments were conducted using (i) anaerobically digested CM digestate, referred to as raw sample (RS), (ii) filtered digestate sample (FS), and (iii) a synthetically prepared control sample (CS). The research findings demonstrated that the initial ammonia concentration variations did not significantly impact the struvite precipitation yield in the RS and FS, showcasing the materials inertness process's robustness to changing ammonia concentrations. Notably, the study revealed that the highest nitrogen (N) recovery, associated with 86% and 88% ammonia removal in the CS and FS, was achieved at pH 11, underscoring the efficiency of nutrient recovery. The RS achieved the highest nitrogen recovery efficiency at pH 10, at 86.3%. In addition, the research highlighted the positive impact of reducing heavy metal levels (Zn, Cu, Pb, Ni, Cd, Cr and Fe) and improving the composition of the microbial community in the digestate. These findings offer valuable insights into sustainable manure and nutrient management practices, emphasizing the potential benefits for the agricultural sector and the broader circular economy. Future research directions include economic viability assessments, regulatory compliance evaluations, and knowledge dissemination to promote the widespread adoption of these clean technologies on a larger scale. The study marks a significant step toward addressing the environmental concerns associated with poultry farming and underscores the potential of integrating clean technologies for a more sustainable agricultural future.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".