Influence of coal properties on the CO<sub>2</sub> adsorption capacity of coal gasification residues
Bibliographic record
Abstract
Abstract Post‐underground coal gasification (UCG) sites hold attractive prospects for geological storage of carbon dioxide. For the successful commercial implementation of UCG with carbon capture and storage (CCS), site‐selection is crucial, and a careful techno‐economic feasibility analysis is essential to systematically assess the site related parameters aside from evaluating the environmental risk. This study is related to one of the important aspects of site selection‐ the coal type. Specifically, this work investigates the influence of coal properties and gasification conditions on the adsorption capacities of CO2 on gasified coal chars. For this purpose, four coals of diverse ranks varying from lignite to bituminous were selected and subjected to CO2 gasification at atmospheric pressure for 10 min at 800, 900, and 1000°C under a low heating rate of 5°C/min. Subsequently, the gasified chars, as well as the raw coals, were tested for their adsorption capacity in a purpose built volumetric adsorption apparatus at 45.5°C and pressures up to 90 bar. Also, complementary coal and char analysis were carried out for determining the surface area, pore size distribution, and surface morphology. The CO2 storage capacity was observed to be a strong function of the coal properties and gasification conditions. Among the samples examined, the highest adsorption capacity was observed for chars of the sub‐bituminous coals. The CO2 adsorption capacity at 80 bar and 45.5°C on the sub‐bituminous char samples was 2.08, 2.43, and 1.95 mmole/g that were prepared at 800, 900, and 1000°C, respectively. The experimental adsorption isotherms were fitted to the Dubinin‐Radushkevic (DR) and the Dubinin‐Astakhov (DA) models.
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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".