Numerical Analysis of the Combustion Characteristics for the Power Improvement and Additional SOFA System in a Pulverized-Coal Boiler
Bibliographic record
Abstract
Tangentially fired pulverized coal boilers are one of the most widely used boilers in power plants because of their good flame distribution and uniform wall heat flux to the furnace walls and have been used to generate power in Korea [1].In this study, numerical investigation on the power improvement and air staging technology in a 500MWe tangentially fired pulverized-coal boiler has been performed to understand the effect of the characteristics of the combustion, temperature, NOx emission and unburnt carbon residual.The pulverized-coal boiler is simulated within CFD models implemented in the ANSYS FLUENT V17.1 software and the fluid flow and coal particle combustion process are modeled using the Euler-Lagrange approach.The governing equations for the conservations of energy, mass, momentum, and species are solved [2].The boiler was designed to burn low-rank coal (5,600 kcal/kg).And the existing 500MWe PC (Pulverized Coal) boiler reduces the NOx production by multi-stage combustion of OFA (Over Fire Air) and PM (Pollution Minimum) burner [3][4].However, as domestic NOx emission standards become more stringent, additional NOx reduction technologies are needed.Here, the traditional air staging technology has been unable to meet the reduction of pollutants, so we append the SOFA (Separated Over-Fire Air) system, this can be a good way to reduce the NOx generation [5].So we introduce the concept of that SOFA (Separated Over-Fire Air) system with a pulverized-coal boiler.The CFD analysis represents a useful technology to provide the flow and temperature fields.And we expect this emission control technologies to reduce environmental pollution and the impact on human health.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".