A deep graph convolutional network model of <scp>NOx</scp> emission prediction for coal‐fired boiler
Bibliographic record
Abstract
Abstract To deal with environmental problems caused by NOx production in thermal plants, it is imperative to establish a reliable model to predict NOx concentration in the combustion process. NOx formation in a coal‐fired boiler is complex, and many variables affect NOx emissions. The effective information fusion of these variables can improve the accuracy of NOx concentration prediction. However, the existing NOx prediction algorithms based on thermal parameters rarely consider the mechanical knowledge of the boiler operation, and it is not easy to incorporate the topological information of production into modelling. Therefore, a graph convolutional network is proposed for NOx emission prediction. First, the key variables affecting NOx generation are selected according to the knowledge and the random forest‐based variable importance. Then, the model structure is designed by exploring the topological information among thermal variables to capture the complex spatial dependence. The model inputs are constructed by coding different operation variables, and the adjacency matrix is generated according to the correlation information between variables, which can fuse data information and reduce redundancy. On this basis, the prediction model of NOx concentration is established. Historical data from a 660 MW coal‐fired boiler are used in the experiment. The prediction results show that the proposed model can effectively fuse the information of characteristic variables and fully exploit the non‐linear mapping relationship between process variables and NOx emission. When compared with three typical models in NOx modelling, the proposed model has better performance with a determination coefficient of 0.906.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".