Influence of Porosity on Performance of Freeze-granulated Fe2O3/Al2O3 Oxygen Carriers Used for Chemical Looping Combustion
Bibliographic record
Abstract
Chemical looping combustion (CLC) is a promising solution for the next coal-fired power generation technology with inherent CO2 separation capability. One of the critical aspects for the development of the CLC process is to develop suitable oxygen carrier (OC) particles to transfer oxygen to the fuel in the absence of air. Relevant studies have focused on active material screening, thermodynamic analysis and operational tests. This investigation was conducted on the microstructural property of OCs, to be specific, the particle porosity effect on the performance of iron-based OCs. Fe2O3, supported on Al2O3 was used as the oxygen carrier. The effect of water content of the spray slurry used to produce the OC was varied to determine the influence of OC porosity on reactivity, oxygen transfer capacity and mechanical durability. A preliminary test was done to establish the minimum and maximum water percentage needed to make slurry. A process that included freeze granulation (FG), freeze drying, and calcination was used to prepare four samples of iron oxide/alumina with various water-to-solid phase ratios. A scanning electron microscope (SEM) was used to characterize the porosity of FG Fe2O3/Al2O3 particles. A direct relationship was observed. A Shimpo FGE-10X force gauge was used to measure the crushing strength of selected samples. A thermogravimetric analyzer (TGA) coupled with a mass spectrometer (MS) was used to study the change in reaction rates through multiple reduction-oxidation cycles of the samples. Crystallinity of the OCs in reduced and oxidized forms were confirmed by XRD analysis.
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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 | 0.001 |
| 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".