Novel Fluidized Bed Reactor for Integrated NO<sub><i>x</i></sub>Adsorption−Reduction with Hydrocarbons
Bibliographic record
Abstract
In order to avoid the negative impact of excessive oxygen in the combustion flue gases on the selectivity of most hydrocarbon selective catalytic reduction (HC-SCR) catalysts, an integrated NO(x) adsorption-reduction process has been proposed in this study for the treatment of flue gases under lean burn conditions by decoupling the adsorption and reduction into two different zones. The hypothesis has been validated in a novel internal circulating fluidized bed (ICFB) reactor using Fe/ZSM-5 as the catalyst and propylene as the reducing agent. Effects of propylene to the NO(x) molar ratio, flue gas oxygen concentration, and gas velocity on NO(x) conversion were studied using simulated flue gases. The results showed that increasing the ratio of HC:NO improved the reduction performance of Fe/ZSM-5 in the ICFB reactor. NO(x) conversion decreased with an increasing flue gas flow velocity in the annulus U(A) but increased with an increasing reductant gas flow velocity in the draft tube U(D). The NO(x) adsorption ratio decreased with increasing U(A). In most cases, NO(x) conversion was higher than the adsorption ratio due to the relatively poor adsorption performance of the catalyst. Fe/ZSM-5 showed a promising reduction performance and a strong inhibiting ability on the negative impact of excessive O2 in the ICFB reactor, proving that such an ICFB reactor possessed the ability to overcome the negative impact of excessive O2 in the flue gas using Fe/ZSM-5 as the deNO(x) catalyst.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".