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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".