Co-gasification of Biomass and Non-biomass Feedstocks: Synergistic and Inhibition Effects of Switchgrass Mixed with Sub-bituminous Coal and Fluid Coke During CO<sub>2</sub> Gasification
Bibliographic record
Abstract
Co-gasification of biomass, namely, switchgrass, with coal and fluid coke was performed to investigate the availability of the gasification catalysts to the mixed feedstock, especially alkali and alkaline earth elements, naturally present on switchgrass. Rates of CO 2 gasification of the single and mixed materials were measured at temperatures between 750 and 950 °C and atmospheric pressure by thermogravimetry. High interparticle mobility of the catalysts is indicated by a prompt and lasting effect on the mixed feed gasification rate when compared with the separate rates. The switchgrass–coal mixtures show a deactivation (antagonism), attributed to sequestration of the mobile alkali elements by reaction with aluminosilicate minerals in coal to form inactive alkali aluminosilicates, such as KAlSi 3 O 8 and KAlSiO 4 . Remaining catalytic activity is evident when excess alkali is present in the feed mixture to satisfy the stoichiometric requirements of these deactivation reactions. In co-gasification of switchgrass with fluid coke, which has little interfering inorganic matter, a synergism is noted in the gasification of the mixed feed. The results document the effects of fuel mixture, composition of the coal or coke ash, and the gasification temperature on this behavior.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".