Potassium‐catalyzed steam gasification of ash‐free lignite coal with CO<sub>2</sub> capture
Bibliographic record
Abstract
Abstract The purpose of this research was to study steam gasification of ash‐free coal integrated with CO2 capture in the presence of a K2O catalyst for enhancement of the key water‐gas shift reaction and promotion of hydrogen production. To achieve this goal, gasification experiments on ash‐free coal (AFC) were carried out at varying temperatures (600, 650, 675, 700, and 750 °C) with a sorbent‐to‐carbon (CaO/C) ratio of 2 and a catalyst (K2O) loading of 0.2 g/g (20 weight percent (wt%)) in a fixed‐bed reactor equipped with a gas chromatography analyzer. The sorbent‐to‐carbon (CaO/C) ratio of 2 is based on dry and ash‐free basis. The CaO/C ratio and K2O wt% were chosen to maximize hydrogen production based on our previously determined optimal values. The AFC was originally extracted from raw lignite coal using organic solvents, which allowed the sorption‐enhanced gasification to be conducted with minimal ash‐catalyst interactions. The effect of temperature on the yield and the initial reaction rate were investigated. The optimal reaction temperature of 675 °C was determined. Carbon balance and final carbon conversions were calculated based on the residue analysis. Activation energy was also calculated using intrinsic kinetics of the reaction. In this study, using AFC offered the potential advantage of operating the gasification process with catalyst recycle.
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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".