Étude de l’influence des paramètres de combustion sur la formation de SO<sub>2</sub>, de NO et de CO lors de la dégradation thermique de produits phytosanitaires d’usage courant en Afrique de l’Ouest
Bibliographic record
Abstract
As we are faced with a constantly increasing quantity of industrial waste (namely, phytosanitary products), incineration is perceived as an interesting method for their disposal. Nevertheless, incineration can generate pollutants such as nitrogen oxides (NOx), sulfur oxides (SOx), carbon monoxide (CO), and other toxic gases. Therefore, it is important to optimize the combustion process to reduce these emissions. Studies performed during incineration of phytosanitary wastes show strong correlations between the generation of these pollutants and combustion parameters such as oxygen concentration, temperature, and residence time in the reactor core. In the present study, we focused more particularly on determining the influence of these parameters on the production of nitrogen monoxide (NO), CO, and sulfur dioxide (SO2) during the incineration of Cyperthion D and Cyperthion O, the two main phytosanitary products currently used in West Africa. The results showed that NO and SO2 emissions decrease with an increase in residence time, but increase with higher local oxygen concentrations and higher combustion temperatures in the reactor core. Inversely, CO emissions increase with an increase in residence time, but decrease with higher temperatures and higher local oxygen concentrations. This small-scale study allows us to derive the experimental conditions to pursue large-scale assays, in a rotatory incinerator, for the thermal processing of expired phytosanitary products.
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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) | 0.000 | 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) | 0.001 | 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".