Numerical Analysis for a 870MW Wall-fired Pulverized Coal Boiler: Comparison with Field Test and Simulation
Bibliographic record
Abstract
Pulverized coal-fired boilers have been used to generate power in Korea, contributing 39% of the total electricity in 2014. [1] Recently, the Korea South East Plant (KOSEP) constructed a wall-fired boiler with a capacity of 870MW on Dec. 2014. The boiler was designed to burn low rank coal (5,300 kcal/kg) and this uses a Low-NOx Burner and air staging system for reduction of NOx emission during coal combustion. This system can control emissions and combustion efficiency. For this reason, Kim et al. The test results are about change of NOx and CO concentration at exit from the boiler depending on the parameters. Computation fluid dynamics (CFD) have been widely used to predict boiler performance. [3-5]. It's because CFD can provide a detailed boiler analysis such as distribution of temperature, velocity and species concentration in the boiler. So the objective of this study is a numerical investigation of the field test in the 870MW wall-fired boiler This study is investigated the effects of burner swirl angle and OFA flow distribution on the combustion and emission characteristics such as gas movement, coal particles trajectory, temperature, thermal absorption at water wall around a burner zone etc. The CFD results compare to the field test and analyse the combustion characteristics in the boiler. The commercial CFD code ANSYS FLUENT v16.1 is employed to simulate the CFD simulations. The fluid flow and coal particle combustion process are modelled using the Euler-Lagrange approach. The governing equations for the conservations of energy, mass, momentum and species are solved
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".