A mechanistic model for simulating methane emissions from unstirred liquid manure storages
Bibliographic record
Abstract
The objective of the study was to develop a mechanistic model of methane (CH4) producing processes in unstirred conditions with potential application for estimating CH4 emissions from anaerobic manure storage facilities. Although models for describing anaerobic digestion processes are available, they largely relate to anaerobic digesters, and do not directly apply to the prediction of CH4 emissions from liquid manure storage. Based on extant models, six biochemical steps were described: hydrolysis, acetogenesis, hydrogenogenesis, homoacetogenesis, hydrogenous methanogenesis and acetic methanogenesis, performed by five bacterial groups. The model contains six state variables, and mass flow is mostly generated and quantified using bacterial kinetics. The model was coded in acslX and a fourth-order Runge-Kutta method with an integration step size of 0.05 d was used for numerical integration. The time courses of CH4 production and volatile fatty acid (VFA) concentration of two laboratory-scale liquid manure storage tanks, both filled with liquid sow manures and running in unstirred and constant 25°C conditions, were well predicted, with correlation coefficients over 0.90. Discrepancies between predicted and measured CH4 production and VFA concentration were mainly due to random variation of observed data. The model was sensitive to parameters describing hydrolysis and the kinetics of acetogenic and acetate methanogenic bacteria. Simulations based on the Intergovernmental Panel on Climate Change model (Tier II) predicted 260 g CH4 kg-1 volatile solids (VS, assuming maximum CH4 producing capacity of 0.48 and methane conversion factor of 80%), whereas the measured value was 78.3 g CH4 kg-1 VS after 146 d and the mechanistic model predicted 74.8 g CH4 kg-1 VS. The model developed in this study appears to be better suited to batch manure storage than the IPCC model.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".