Determination of the Synergism/Antagonism Parameters during Co-gasification of Potassium-Rich Biomass with Non-biomass Feedstock
Bibliographic record
Abstract
This study focuses on quantifying the synergistic/antagonistic behavior occurring during the co-gasification of non-biomass feedstock (ash-free coal and fluid coke) with potassium-rich switchgrass. The results showed that the gasification rate of switchgrass in the mixture decreased as a certain amount of its potassium was transferred to the non-biomass feed, leading to a multifold increase in the non-biomass gasification rate. The aim of this study was to quantify this behavior through kinetic modeling. It was assumed that each constituent in the mixture follows the random pore model with their corresponding kinetic parameters. Furthermore, synergism/antagonism parameters were included, which were either a constant or a function of the switchgrass conversion (linear or square root), representing the effect of interparticle potassium mobility. The acceleration of the gasification rate of the non-biomass feedstock followed a linear function of the switchgrass conversion. The inhibition of the switchgrass conversion did not show a clear trend because it depends upon the non-biomass feedstock and temperature. The presence of potassium in switchgrass, which acts as a catalyst, is clearly observed from the modeling, where the gasification rates for the fluid coke or ash-free coal in the mixture significantly increased for all temperatures studied. Modeling of the gasification reactions with the estimated best fit synergism/antagonism models showed a very good agreement with the observed values. The obtained results of this study can be useful in designing co-gasification systems and estimating the best ratio of biomass/non-biomass feeds.
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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 | 0.000 | 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 | 0.001 | 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".