Conversion of Petroleum Coke in a High-Pressure Entrained-Flow Gasifier: Comparison of Computational Fluid Dynamics Model and Experiment
Bibliographic record
Abstract
High-pressure entrained-flow gasifier technology is used to convert solid carbonaceous feedstocks into synthesis gas, which can be used in an integrated gasification combined cycle power plant or as a feedstock for chemical or synthetic fuel production. Computational fluid dynamics (CFD) models, once validated, can be used to help design full-scale reactors. Model validation entails the comparison of model predictions to lab-scale or pilot-scale measurements. However, experimental measurements of high-pressure pilot-scale gasifiers usually consist only of wall temperatures and outlet gas temperature and composition, which are of limited use for model validation when the gasifier is operating well, providing information only about operating temperature, heat loss, and equilibrium gas composition. These do not provide a strong validation of the CFD model, whose main purpose is to make predictions of the flame size and shape and its ability to convert solid fuel to gas efficiently in a small volume. This paper presents a model validation based on data generated using CanmetENERGY’s 1 MW th high-pressure entrained-flow gasifier. To provide a stronger validation, the approach taken here is to compare the model predictions to the pilot-scale measurements over a range of operating conditions comprising higher (approximately 90%) carbon conversion and lower (approximately 80% or lower) carbon conversion. In effect, the comparison includes operating conditions for which gasification reactions are extended or delayed toward the outlet in order to capture key effects. It is found that the present CFD model is able to track the performance of the gasifier over the range of operating conditions and provides insight into the causes for limited carbon conversion.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".