Forecast of NOx Emissions for a 660MW Coal-Fired Boiler with Multilayered Gradient Boosting Decision Tree Considering Multiple Operating Modes
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Nitrogen oxides (NOx) are among the primary pollutants emitted by coal-fired power plants. Accurate prediction of NOx concentrations at the boiler outlet is crucial for optimizing unit control and reducing emissions. This study introduces a data-driven NOx emissions prediction methodology based on a multilayered Gradient Boosting Decision Tree (mGBDT) framework. Initially, Kernel Independent Component Analysis (KICA) is employed to eliminate nonlinear correlation among collected auxiliary variables. Subsequently, high-quality variables, grounded in physical mechanisms, are integrated with the extracted independent features. The robust Gaussian Mixture Model (RGMM) is then applied to capture the intrinsic multimode operational characteristics from these integrated features. Finally, local mGBDT-based NOx emissions prediction models are developed for each identified mode. The optimal hyperparameters for each local model are determined using Particle Swarm Optimization (PSO) and 10-fold cross-validation techniques. Utilizing historical measurements from the studied boiler, the proposed framework achieved a square of correlation coefficient ( R 2 ) of 0.947, a root-mean-square error (RMSE) of 6.09 mg/m 3, and a mean absolute error (MAE) of 4.009 mg/m 3, outperforming five comparison models.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".