Parametric Analysis Using a Reactor Network Model for Petroleum Coke Gasification
Bibliographic record
Abstract
Usage of reduced order models (ROMs) and reactor networks are becoming widely accepted tools for the modeling of complex reactors, such as entrained-flow gasifiers. The approximations made in a ROM reduce the required computational costs compared to computational fluid dynamic (CFD) models; however; the capabilities of the model in predicting the outputs for a range of operating conditions in the gasification unit face challenges. The following contribution presents a comparison between a ROM and the corresponding CFD model of a short-residence-time gasifier under different operating conditions and kinetic parameters. Although the framework of the proposed ROM was fixed and developed on the basis of CFD simulations generated at a base-case condition, the results showed reasonable agreement between the two models in predicting syngas composition, carbon conversion, and temperature profile in the gasification system. Sensitivity analysis of the inputs of the ROM (including test condition and reactor network parameters) has also been performed. This analysis has shown that the recirculation ratio and oxygen flow rate have a greater effect on the outputs compared to model geometry and kinetic parameters.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".