Dry Petroleum Coke Gasification in a Pilot-Scale Entrained-Flow Gasifier and Inorganic Element Partitioning Model
Bibliographic record
Abstract
Entrained-flow gasification has several advantages over competing technologies for converting petroleum coke, a byproduct of oil refining. However, due to the high capital costs and limits of current commercial technology, the economics look favorable only with high natural gas and oil prices, and high CO 2 emission penalties. The objective of the current study is to accelerate the development of petroleum coke gasification technologies via dry-feed pressurized entrained-flow gasifier pilot-scale tests with petroleum coke. The results indicate carbon conversion generally increased with higher O:C ratios. Thermodynamic model predictions generally vary by less than 25% from the experimental outlet gas flow rates of the main species, CO and H 2 . The predicted flow rates for other gases vary much more from experimental values, while the predicted carbon conversion values are similar (±16 percentage points), and the predicted temperatures are mostly lower than experimental values. Mass balances and enrichment factors were calculated for inorganic elements due to their potential environmental and technological impact. In general, results from this study indicate similar or lower volatility for elements when compared to combustion systems. An inorganic element partitioning model is presented and compared to experimental values. Considerations for other types of petroleum coke are also provided.
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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 | 0.000 |
| 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 | 0.001 |
| 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".