Numerical Modeling of Volatile Organic Compounds (VOC) Emissions during Preheating of Magnesia-Carbon Bricks in a Basic Oxygen Furnace
Bibliographic record
Abstract
The refractory preheating process in oxygen furnaces is a dynamic input of energy in a chemically complex system requiring special attention to chemical emissions relative to permissible release limits. This particular industrial and regulatory interest is the emission of volatile organic compounds (VOC), given their detrimental impacts on human health. In the present work, a mathematical model was developed to predict the emission rates of volatile organics during the preheating of a 260-ton basic oxygen furnace. A numerical heat transfer model was developed using finite difference techniques to obtain the thermal profile and then integrated with chemical thermodynamics using FactSage 7.0 (CRCT, Polytechnique Montreal Quebec Canada, H3C 3A7). The parameters that affected VOC emissions were preheating process times, burner gas composition, heating rate, and burner geometry. Two different preheating procedures were compared, and emission rates were predicted with extended use of a top burner providing the greatest degree of emissions control. The mathematical model was validated against plant data with respect to average emission rates of CO, CO2, SOX, and NOX.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".