Enhancing low-temperature anaerobic digestion of nitrogen-rich feedstocks: Mitigating free ammonia and short-chain fatty acid inhibitions
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Bibliographic record
Abstract
High ammonia levels (>5 g N/L) have been a critical barrier in the anaerobic digestion (AD) process, with limited studies achieving long-term stability—until now. This research investigates strategies to counteract inhibition from free ammonia nitrogen (FAN) and short-chain fatty acids (SCFAs) in low-temperature AD (LT-AD) of nitrogen-rich feedstocks. LT-sequencing batch reactors (LT-SBRs) were tested under total ammonia nitrogen (TAN) concentrations up to 12.5 ± 1.56 g N/L at 24.5 ± 0.5 °C and 20 ± 0.5 °C. Results demonstrated that LT-SBR systems maintained stability, with VFA/alkalinity ratio below 1 and the propionic/acetic acid ratio ≤ 1.4. FAN/TAN conversion ratio decreased from 2.61 % at 24.5 °C to 1.17 % at 20 °C, ensuring minimal inhibition. Despite high TAN, methane production was resilient, with specific methane yields of 0.44 L CH 4 /gCOD at 24.5 °C and 0.22 L CH 4 /gCOD at 20 °C. These findings demonstrate that LT-AD can handle high‑nitrogen feedstocks, achieving robust methane yields and stable performance. • The low-temperature process showed resilience, managing high‑nitrogen feedstocks. • At 20 °C and 12.5 g total ammonia nitrogen/L, methane yield was 0.20 LCH 4 /gCOD. • Free to total ammonia ratio dropped from 2.61 % to 1.17 %, minimizing inhibition. • Volatile fatty acid removal exceeded 90 % (24.5 °C) and 82 % (20 °C) with high ammonia. • Volatile fatty acid/alkalinity and propionic/acetic ratios ≤1 and ≤ 1.4 ensured stability.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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