Hydrodynamics of bubbling fluidized bed for adsorption of CO<sub>2</sub> with KOH/K<sub>2</sub>CO<sub>3</sub>
Bibliographic record
Abstract
Abstract The aim of this study is to assess the performance of carbon dioxide (CO2) capture in a bubbling fluidized bed using a proper adsorbent. A mixture of potassium hydroxide (KOH) and potassium carbonate (K2CO3) adsorbents was used as bed materials, which provide proper kinetic and fluidization behaviours. The adsorbents consisted of two different mean particle sizes: size 1 is composed of K2CO3 with mean particle size of 335 μm and KOH with mean particle size of 197 μm; whereas, size 2 contained the K2CO3 with mean particle size of 605 μm and KOH with mean particle size of 197 μm. The weight fraction of KOH in both sizes was 0.3 g/g (30 mass%). The pressure fluctuations of the bed were measured and characterized in a time domain. The effects of several hydrodynamic parameters (i.e., superficial gas velocity, aspect ratio of bed, and particle size distribution of the adsorbent mixture) on CO2 adsorption were investigated. The results showed that the larger bubbles caused an improvement in solid mixing in the bed and consequently enhanced the CO2 capture capacity. Fluidization of the adsorbents mixture with narrower distribution (size 1) led to the formation of larger bubbles and an improvement of mixing in the bed. Therefore, size 1 adsorbent exhibited a higher CO2 capture capacity compared to a wider distribution of (size 2) adsorbent. Furthermore, the bubble size is increased with an increase in the aspect ratio of the bed leading to a better mixing in the bed.
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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".