Reacting Turbulent Flow Simulation to Improve the Mixing Process in an Oil Refinery Incinerator
Bibliographic record
Abstract
Combustion emission is one of the most important issues in the design of industries. Todays’ strict environmental standards have limited the productions of CO, NOx, SOx, and other hazardous pollutants from the related industries. In this work, we study a typical oil refinery incinerator, which is used to burn waste gases residue produced during bitumen production process. The waste gas mainly includes a mixture including N2, H2O-vapor, and O2 species. Additionally, there are significant amounts of CO species and CxHy droplets in the waste gas composition. The measurements show that the CO emission becomes so crucial in high flow rate of feeding waste gas to the incinerator. Here, we numerically simulate the combustion process in this incinerator by solving the full turbulent reacting flow equations. In this regard, we use the finite-volume method to solve the RANS equations. For turbulence modeling purposes, we use the two-equation k-ε model along with standard wall functions. The non-premixed combustion is simulated by solving the mixture fraction equations for both fuel and waste gas streams. The interaction between turbulence and combustion is properly considered in the current modeling. We use the P1 method to solve the radiation transfer equation in emitting and absorbing medium of combustion gasses. The WSGG model is used to consider the absorption coefficient variation. The set of governing equations are solved using a SIMPLE-based algorithm. The current solutions provide good knowledge about the mixing pattern of flue gas and air-fuel streams in the incinerator. The improper mixing in the incinerator suggests we present a new design to re-design the waste gas inlet to the incinerator. Our simulation shows that the new design would result in substantial improvement in mixing process of these two streams. We find that this new design would effectively reduce the CO ppm at the exit of incinerator’s stack.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".