Effect of the Composition of Additive Ash on the Thermal Behavior of Petroleum Coke Ash during Gasification
Bibliographic record
Abstract
The behavior of ash fusion at high temperatures plays a key role in the stable operation of gasifiers. This study investigated the effect of synthetic mineral compounds on the transformation and ash fusion temperatures (AFTs) of petroleum coke (petcoke) ash in a steam atmosphere with the variation of the SiO 2 /Al 2 O 3 (Si/Al), Fe 2 O 3 /CaO (Fe/Ca), and V 2 O 5 /NiO (V/Ni) ratios and temperature. Thermodynamic equilibrium calculation was also applied to simulate the ash-melting process in petcoke gasification. The results show that the dominant crystalline phases in petcoke ash at high temperature are CaAl 2 Si 2 O 8, FeAl 2 O 4, and FeV 2 O 4 . The increase of temperature is conducive to the formation of low-melting minerals, such as, CaAl 2 Si 2 O 8 and FeAl 2 O 4 . High Si/Al is beneficial for the reduction of AFTs because high melting point minerals partly convert into slag and the low melting point mineral phase of CaAl 2 Si 2 O 8 transforms largely into slag. AFTs could be reduced at an Fe/Ca ratio of 0.5 when the content of slag in the ash reaches maximum and there are plenty of the low-melting minerals (CaAl 2 Si 2 O 8 ). A high V/Ni ratio was not conducive to the suppression of AFTs. The effect of Si/Al and Fe/Ca could effectively improve the AFTs of petcoke ash. A high ratio of Si/Al and the ratio of Fe/Ca at 0.5 were beneficial to the ash fusibility. AFTs under gasification atmosphere conditions have been predicted by FactSage.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".