Catalytic NO<sub><i>x</i></sub> Reduction in a Novel i-CFB Reactor: II. Modeling and Simulation of i-CFB Reactors
Bibliographic record
Abstract
A mathematical model has been developed for the i-CFB deNO x reactor, which is designed for the hydrocarbon-based selective catalytic reduction (HC-SCR) process. The i-CFB model consists of three submodels: hydrodynamics, NO x adsorption, and reaction kinetics. The modeling results show good agreement with the experimental data. It is observed from the simulation that the performance of the i-CFB reactor is very sensitive to gas bypass from the draft tube to the annulus ( R d-a ) but is less sensitive to the converse ( R a-d ). The solids circulation rate ( G s ) has little effect on the overall deNO x efficiency. The analysis of NO x adsorption and reduction at both the annulus and the draft tube zones revealed that solids residence time in the reduction zone is too short for the NO x reduction reaction for the current i-CFB reactor design. A large reduction zone could significantly enhance the overall deNO x efficiency. The optimum reduction zone area ratio ( A R / A total ) should be ∼0.65–0.7. Further increases in the A R / A total ratio will decrease NO x conversion. It is also observed that the performance of the i-CFB at higher A R / A total ratios is less sensitive to gas bypass from the reduction zone to the adsorption zone. The deNO x efficiency of i-CFB reactor becomes more sensitive to NO x adsorption capacity at higher A R / A total ratios.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".