Modeling and Simulation of Multipollutant Dispersion from a Network of Refinery Stacks Using a Multiple Cell Approach
Bibliographic record
Abstract
Mathematical air pollution modeling represents an essential tool to control and predict atmospheric pollution. In this paper, a multiple cell model for the three-dimensional simulation of pollutants (SO2, CO, NOx, and TH) dispersion from a network of industrial stacks is presented. The model verification was conducted by checking the simulation results for a single stack against experimental data and also against the predictions of the Gaussian Dispersion Model. Simulation runs were also conducted in actual scale in order to illustrate the program on a network of actual refinery stacks. The results are compared with measured data and also with the results obtained from the Industrial Source Complex (ISC) model, and good agreements were obtained. The effects of meteorological parameters (i.e., wind velocity, air temperature, atmospheric stability, and surface roughness) on pollutants dispersion were also investigated, and a sensitivity analysis study was carried out in order to determine the effect of atmospheric conditions and other input parameters on pollutants dispersion. Sensitivity analysis shows that concentration is sensitive to exit concentration and flow rate in comparison with other input parameters. Finally, practical methods for reducing maximum ground level concentrations are recommended and simulated using the proposed 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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".