Dynamic <scp>NOx</scp> emission prediction in coal‐fired power plants based on joint multi‐head attention <scp>CNN</scp> ‐ <scp>GRU</scp> hybrid model
Bibliographic record
Abstract
Abstract The flexible operation of coal‐fired power plants under deep peak‐shaving conditions imposes significant challenges on accurate NOx prediction for SCR systems. Given the inherent complexities of the SCR denitrification process, characterized by dynamic nonlinearity, temporal variability, and multivariable coupling in nitrogen oxide emissions, this study proposes a convolutional neural network‐gated recurrent unit (CNN‐GRU) hybrid model integrated with multi‐head attention (MA) mechanisms to address these system‐specific characteristics for precise NOx prediction. The model combines the local feature extraction capability of CNNs, the long‐term temporal dependency modelling strength of GRUs, and the adaptive feature weighting functionality of MA mechanisms, achieving dynamic weight allocation across feature channels and temporal scales to enhance robustness and feature representation. Furthermore, a sparrow search algorithm (SSA) is introduced to optimize model parameters adaptively, improving prediction accuracy and generalization performance. Experimental validation using real operational data demonstrates the model's superior performance, with mean absolute error (MAE) below 0.5 mg/m 3 and mean absolute percentage error (MAPE) below 2%. Ablation experiments confirm the effectiveness of the proposed architecture, showing over 28% prediction accuracy improvement compared to Transformer‐based models while maintaining enhanced generalization capability.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".