Catalytic NO<sub><i>x</i></sub> Reduction in a Novel i-CFB Reactor: I. Kinetics Development and Modeling of Reduction Zone
Bibliographic record
Abstract
A model was developed for the hydrocarbon selective catalytic reduction (HC-SCR) of NO x in a fluidized bed reactor. For the kinetics, the reaction was divided into two submodels: (i) NO oxidation and adsorption; (ii) hydrocarbon oxidation and NO x reduction. The kinetic parameters were obtained from fitting a fixed bed model to fixed bed experimental data. Mass transfer between solids and gas phases was also considered in this fixed bed model with the reaction term being embedded into the solids phase mass balance equation. Comparing with fixed bed experimental data, the model showed an average error of about 7% for NO x conversion and about 5% for HC conversion. The fitted kinetics was then incorporated into a fluidized bed reactor model, which included gas flow in the bubble phase, gas flow in the dense phase and solids flow in the dense phase. The fluidized bed reactor model was compared to the fluidized bed experimental data. The model showed acceptable agreement with measured NO x conversion, but poor agreement with observed HC conversion data. Suggestions were proposed to further improve the model and experimental data. The fluidized bed model was then applied to simulate NO x reduction in the reduction zone of the internal circulating fluidized bed (i-CFB) reactor. The simulation results showed that NO x conversion could be improved if NO x is fed into the reactor via the solids phase, the same as what happens in the reduction zone of an i-CFB reactor. Also, for the reduction zone of the i-CFB reactor, a higher solids circulation rate is preferred if NO x feed rate is kept at a constant. These simulations could provide good suggestions for the i-CFB design and operation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".