Struvite-Driven Integration for Enhanced Nutrient Recovery from Chicken Manure Digestate
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it