Characterizing the Performance of Gas-Permeable Membranes as an Ammonia Recovery Strategy from Anaerobically Digested Dairy Manure
Bibliographic record
Abstract
Capturing ammonia from anaerobically digested manure could simultaneously decrease the adverse effects of ammonia inhibition on biogas production, reduce reactive nitrogen (N) loss to the environment, and produce mineral N fertilizer as a by-product. In this study, gas permeable membranes (GPM) were used to capture ammonia from dairy manure and digestate by the diffusion of gaseous ammonia across the membrane where ammonia is captured by diluted acid, forming an aqueous ammonium salt. A lab-scale prototype using tubular expanded polytetrafluoroethylene (ePTFE) GPM was used to (1) characterize the effect of total ammonium nitrogen (TAN) concentration, temperature, and pH on the ammonia capture rate using GPM, and (2) to evaluate the performance of a GPM system in conditions similar to a mesophilic anaerobic digester. The GPM captured ammonia at a rate between 2.2 to 6.3% of gaseous ammonia in the donor solution per day. Capture rate was faster in anaerobic digestate than raw manure. The ammonia capture rate could be predicted using non-linear regression based on the factors of total ammonium nitrogen concentration, temperature, and pH. This use of membranes shows promise in reducing the deleterious impacts of ammonia on both the efficiency of biogas production and the release of reactive N to the environment.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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 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".