Evaluation of ash‐free coal for chemical looping combustion ‐ part II: Thermogravimetric multi‐cycle performance
Bibliographic record
Abstract
In this paper, performance of AFC in CLC during multi‐cycle thermogravimetric (TG) experiments was investigated. The close to stoichiometric CuO/AFC ratio of 30 is selected for the consecutive reduction and re‐oxidation cyclic experiments. The reactivity of the first cycle was higher than the consecutive cycles due to the fresh CuO in the first cycle. The thermal behaviours, such as mass change in the consecutive cycles, were almost similar and there was no residual ash deposition after each cycle. Furthermore, reduction and oxidation processes were performed with different isothermal times. After 1 h the combustion was incomplete but a longer combustion period (3 h) leads to almost complete combustion. Moreover, fresh samples and solid residues were analyzed by several advanced analytical techniques including XRD, SEM, and BET. XRD analysis of the residue of CuO/AFC showed the presence of CuO at the end of the cycles; CuO and SiO2 in residues from CuO/BL raw coal (parent coal of AFC) were shown in XRD results. At the higher temperatures (900 °C) the sintering effect and increased agglomeration were observed after each cycle. Total pore volume and average pore radius of the material decreased, however, the CLC performances at 900 °C did not reduce. The multi‐cycle experiments showed AFC as a promising candidate for CLC.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".