Modeling and simulation of a novel internal circulating fluidized bed reactor for selective catalytic reduction of nitrogen oxides
Bibliographic record
Abstract
The internal circulating fluidized bed (i-CFB) reactor exhibits an ability to overcome the negative impact of excessive O₂ present in the flue gas on selective catalytic reduction of NOx with hydrocarbons as the reductant (HC-SCR) by decoupling NOx adsorption and reaction into two separate zones. A mathematical model has been developed in this study, which includes three sub-models: hydrodynamics, adsorption and reaction kinetics. Each sub-model was developed separately and validated by experimental data before they were integrated into the i-CFB model. For the hydrodynamics of i-CFB, solids circulation rates, which were later used for model parameter fitting, were measured using optical fibre probe. The hydrodynamics model was then developed based mass and pressure balance. Adsorption isotherm and deNOx reaction kinetics were developed based on a series of fixed bed experimental data: O₂ adsorption, NOx adsorption and NOx reaction. The kinetic model was further evaluated by fluidized bed adsorption and reaction experiments. The simulation results of the integrated i-CFB model showed good agreement with the experimental data. It is observed from the model that the performance of the current laboratory scale i-CFB reactor was dominated by the catalyst reactivity, rather than the catalyst adsorption rate, because of too short a solids residence time in the reduction zone for the deNOx reaction. Simulation results for i-CFBs with different cross sectional areas of the adsorption and reduction zones showed that a large reduction zone could significantly enhance the overall deNOx efficiency, and there existed an optimal reduction zone to adsorption zone area ratio at which NOx conversion is maximized at a given operating condition. It was also observed that the performance of i-CFB reactors with a larger reduction zone is less sensitive to gas bypass from reduction zone to adsorption zone. Overall, the i-CFB model developed in this study can be used as a tool to assist reactor design and scale up, and to provide guidance on how to further improve the NOx reduction efficiency. The simulation results showed that it is possible to achieve a higher deNOx efficiency higher while avoiding the negative effects of flue gas O₂.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".