Entrained‐flow gasifier fuel blending studies at pilot scale
Bibliographic record
Abstract
Abstract For the foreseeable future, coal and petroleum‐based materials, such as petroleum Coke, residuals, and high‐sulphur fuel oil, are being adopted as the feedstocks of choice for gasification projects. Of particular interest from a Canadian perspective is Coke generated from the thermal cracking of the oil sands in Western Canada. Oil sand Coke contains high sulphur (5–6%), and also typically has a low volatile content, and lower reactivity than most coals. Experimental runs have recently been conducted on the pilot‐scale entrained‐flow gasifier at CETC‐Ottawa, blending oil sand Coke with sub‐bituminous and lignite coals, to try and enhance the gasification potential of these materials. Blending Genesee sub‐bituminous coal with the delayed oil sands Coke was found to alleviate problems encountered with slag plugging the reactor when running with Genesee coal alone. Blends of Genesee sub‐bituminous and Boundary Dam lignite coals with Coke achieved higher carbon conversions and cold gas efficiencies than runs completed with the Coke by itself. While using CO2 as the conveying gas into the gasifier was not found to significantly affect the conversion obtained, steam addition was found to have a marked effect on CO and H2 concentrations in the syngas.
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 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 | 0.001 | 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 | 0.000 |
| 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".