Kinetic analysis of cyclic carbonation of carbide slag during chemical reaction‐controlled stage under fluidization conditions
Bibliographic record
Abstract
Carbide slag, as a kind of typical industrial waste, was proposed as a CO2 sorbent in the calcium looping process. The CO2 capture performance of carbide slag was investigated in a bubbling fluidized bed reactor (BFBR) under fluidization conditions. A surface reaction‐controlled kinetic model was employed to describe the carbonation kinetics of carbide slag during the chemical reaction‐controlled stage. The results show that the values of k, tcrcs, and Xu, which respectively denote the reaction rate constant, duration time, and final carbonation conversion in the chemical reaction‐controlled stage, decrease with the cycle number, due to the sintering of the sorbents. The microstructure of calcined carbide slag leads to higher CO2 diffusion resistance in carbide slag than that in limestone, causing lower k and longer tcrcs compared with limestone. The larger BET areas and pore areas of calcined carbide slag provide additional surface for the reaction of CaO and CO2. Therefore, the Xu values of carbide slag are higher than those of limestone after 5 cycles. Reaction conditions have a significant effect on the carbonation process of carbide slag. 850–900 °C is the optimum temperature range for the calcination of carbide slag. The gas‐solid transfer is strengthened at a higher fluidization number, enhancing the CO2 capture of carbide slag. The pores of the larger particles are more easily blocked due to the formation of CaCO3 layer, and the Xu of the carbide slag with a larger particle size is lower.
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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".