Anaerobic digestion of poultry manure: Implementation of ammonia control to optimize biogas yield
Bibliographic record
Abstract
Anaerobic digestion of poultry manure has been historically challenging, and infrequently implemented on a full scale, due to the high total solids (TS) and high ammonia content of the substrate. Conventional digestion processes require extensive substrate dilution in order to employ liquid-phase pumping systems and reduce the risk of ammonia accumulation. The necessary substrate dilutions result in an economically infeasible process. Anaerobic leaching bed reactors (ALBRs), coupled with a strategy for ammonia removal, provide a solution to both issues. ALBRs are utilized to maintain digester homogeneity without an active mixing regime. Struvite precipitation is employed concurrently with digestion to reduce total ammoniacal nitrogen (TAN) and prevent inhibition of methanogenic archaea. Nutrient distribution is achieved by recirculation of the percolate through the substrate, enabling the utilization of high solids feedstocks. Partitioning leachate from the bulk substrate allows ammonia removal methodologies to be employed without disturbing anaerobic archaea. Investigations reported herein characterize the influence of moisture content, TAN and temperature on biogas yield during poultry manure digestion. High solids digestion is shown to be most efficient in mesophilic temperature ranges with influent total solids (TS) between 15% and 25%. Digestion at thermophilic temperatures demonstrates increased ammonia inhibition, in contrast to mesophilic digestion. Co-digestion of poultry manure with grease and corn silage demonstrate increased biogas yields of 22% and 44%, respectively, in comparison to digestion of poultry manure alone. The impact of phosphate and magnesium additives, pH, temperature and the N:Mg:P molar ratio on struvite precipitation efficacy are quantified. Magnesium chloride (MgCl2·6H2O) and monopotassium phosphate (KH2PO4) are shown to be the most efficient combination of additives for TAN reduction of poultry manure leachate under neutral reaction conditions. Precipitation is shown to progress efficiently at a pH of seven, in contrast to the commonly reported optimum of nine. Thus allowing leachate treatment without disturbing the microbial consortium. Results reported herein evidence an increase in biogas yield of 30% during batch digestion trials and an increase of 235% during semi-continuous trials, when employing struvite precipitation methodologies. Methane content of the biogas is also shown to increase significantly (p<0.05), when employing struvite precipitation.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".