CaO-Based Pellets with Oxygen Carriers and Catalysts
Bibliographic record
Abstract
This paper presents a new breakthrough in research on CaO-based pellets supported by calcium aluminate cements and other novel binders. These materials were originally designed for calcium looping cycles (CaL) for CO 2 capture. However, their preparation procedure allows for the addition of other materials such as oxygen carriers and/or catalysts. These scenarios were investigated in this work with a focus on the integration of postcombustion CO 2 capture with chemical looping combustion (CLC) and sorption-enhanced reforming (SER) for hydrogen production with simultaneous CO 2 capture. The main criterion for an oxygen carrier to be a component of the pellets is its exothermic reduction, which should provide heat for CO 2 carrier regeneration (CaCO 3 /CaO). There are several transition metal oxides that, in principle, can be used. The CuO/Cu pair and manganese oxides were tested in this study. NiO is selected as a catalyst, which is doped in CaO/CuO-based pellets to enhance SER. The activity tests were conducted using a thermogravimetric analyzer (TGA) apparatus. It has been shown that CuO/Cu is the most promising oxygen carrier, which, during its reduction by both CH 4 and syngas (CO + H 2 ), can provide heat for the calcination of CaCO 3 in the composite material. The manganese oxides are less promising because of a relatively poor oxygen carrying capacity and a low heat released during their reduction, which is also the case with other potential oxygen carriers we have considered. It is demonstrated that the catalytic activity of the CaO/CuO-based composites is significantly enhanced after their doping by NiO. Moreover, this study demonstrates that the CaO/Al 2 O 3 matrix for pellets is a suitable support for both oxygen carriers and catalysts and that bauxite can potentially be used instead of aluminate cements. Finally, it is concluded that CaO-based pellets with oxygen carriers and catalysts have promising properties to be employed in new classes of industrial processes such as CaL integrated with CLC (CaL–CLC) and SER integrated with CLC (SER–CLC). An important advantage of these pellets is their strength, which means that they can also be used in fluidized bed combustion (FBC) systems, which are desirable in these cases.
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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".