A novel robust dynamic method for <scp>NOx</scp> emissions prediction in a thermal power plant
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract NOx is a harmful by‐product of coal‐fired boilers, and accurate prediction of NOx emissions in the outlet of a boiler is essential for environmental protection. In recent years, data‐driven models have been widely studied and applied in this area. However, dynamic characteristics are ignored by many existing models, leading to sub‐optimal performance. Besides, outliers that occur in the operation data have adverse effects on the efficacy of these prediction models. To address these issues, this paper presents a novel method for predicting NOx concentration via integrating a robust dynamic probabilistic approach and the long short‐term memory (LSTM). First, mutual information (MI) is applied to determine the input variables. Subsequently, a robust probabilistic method is proposed to extract dynamic latent features considering outliers. On this basis, the generated latent variables are further utilized to train the LSTM‐based model, with which the intrinsic relation between inputs and NOx values are obtained. Based on the application to a 660 MW thermal power plant, the superiority of the proposed method is demonstrated in terms of high prediction accuracy.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| 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 it