Enhancing low-temperature anaerobic digestion of nitrogen-rich feedstocks: Mitigating free ammonia and short-chain fatty acid inhibitions
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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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".