Modelling ammonia emission from swine slurry based on chemical and physical properties of the slurry
Bibliographic record
Abstract
Cortus, E.L., S.P. Lemay, E.M. Barber and G.A. Hill. 2009. Modelling ammonia emission from swine slurry based on chemical and physical properties of the slurry. Canadian Biosystems Engineering/Le genie des biosystemes au Canada 51: 6.9� 6.22. The slurry pit is one of the main sources of ammonia emission in swine enterprises. Ammonia emission from slurry is considered a convective mass transfer process, but there are different methods to predict the mass transfer coefficient and the amount of ammonia in the gas film at the slurry surface. The objective of this research is to develop a new model to simulate the ammonia emission rate from swine slurry that can be applied to slurries of varying physical and chemical composition (i.e., pH, tempera- ture, concentration, etc.). Slurry samples were collected from pigs fed diets differing in crude protein and sugarbeet pulp content and placed in emission boxes where the headspace ammonia concentration was measured and used to calibrate and validate a new ammonia emission rate model for slurry. The new model relates the fraction (f) of ammonia in the slurry relative to the total ammoniacal nitrogen concentration (TAN) as a linear function of pH and TAN, based on both single and multiple variable regression analyses. The average bias between the simulated and measured emission box concentration levels for seven datasets was � 3%. The new model was deemed accurate for slurry with TAN concentration levels between 0.3 and 1.0 mol l � 1 , and pH levels between 8 and 9. Because the value f is based on a linear relationship with pH, the ammonia emission rate from slurry is less sensitive to changes in pH compared with previous models that used an exponential relationship between f and pH. Keywords: ammonia emission, slurry, TAN, pH, mass transfer, modelling. Le lisier des dalots est l'une des principales sources d'emis-
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".