Gaseous emissions from agricultural biomass combustion: a prediction model
Bibliographic record
Abstract
Abstract. As the price of the fossil energy resources and the need to reduce the environmental impacts from energy use increase, biomass fuels have regained interest from Quebec’s agricultural sector. Producing and burning energy crops at the farm have become strategies to diversify incomes and decrease dependency to fossil fuels. However, the current absence of emission factors for solid fuel combustion does not allow a sustainable development for energy purposes. Besides, the variety of existing furnaces and biomasses complicates the establishment of such reference values. In order to quantify emissions (CO, CO2, NOx, SO2, CH4, NH3 and HCl) from on-farm combustion of different agricultural biomasses (short-rotation willow, switchgrass, reed canary grass, etc.), a prediction model was established based on the calculation of chemical equilibrium of reactive multicomponent systems. Under constant temperature and pressure, this technique has been judged as relevant for the prediction of product compositions considering inlet conditions in several operations and chemical processes, particularly gasification. The model was first established for wood gasification to be able to compare and validate its results with those of existing models from the literature using the same original data. The model was then adapted to biomass combustion and calibrated with recent results from combustion tests held in the province of Quebec. The preliminary results of the prediction model using data from past combustion experiments with wood, willow and switchgrass revealed good agreement between both measured and predicted values. Other simulation tests are required to increase accuracy of the 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".