Entrained-Flow Gasification of Oil Sand Coke with Coal: Assessment of Operating Variables and Blending Ratio via Response Surface Methodology
Bibliographic record
Abstract
Co-gasification of oil sand fluid coke with sub-bituminous coal was performed in an entrained-flow gasifier. The underlying objective of this work was to assess the combined effects of the operating variables (i.e., temperature and oxygen and steam concentrations) and coal/coke blending ratio in an entrained-flow gasification process, where the focus was to quantify the relationships between the response variables and vital operating factors. With a view to the shortcomings of the classical “one-factor-at-a-time” method in identification of the effect of experimental factors and their interactions, a statistical design of the experiment based on response surface methodology (RSM) was used. The response variables used in this work were H 2, CO, H 2 /CO ratio, gasification efficiency, and carbon conversion. Experiments were conducted over a temperature range of 1000–1400 °C, using steam and oxygen to carbon weight ratios of 0.9–4.3 and 0–0.4, respectively, equivalent to 15–50 vol % steam and 0–3 vol % oxygen in N 2 carrier gas. All of the response variables were successfully fitted to either a two-factor interaction or quadratic model. Using RSM, the effects of individual operating factors and their interactions were categorically determined, which were not otherwise possible by the classical design of experiment methodology. Using the resultant response variable correlations, H 2 production was optimized as a function of the temperature, steam and oxygen concentration, and different blending ratios. The full potentiality of Canadian oil sand coke for entrained-flow gasification was successfully investigated via RSM. The results of this work, however, are only applicable to entrained-flow gasification systems.
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 | 0.001 | 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) | 0.000 | 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".