A modified fuzzy clustering framework for catalyst activity monitoring in the <scp>selective catalytic reduction</scp> system
Bibliographic record
Abstract
Abstract The catalyst activity monitoring in the selective catalytic reduction (SCR) system is of great importance for safety and economic operation in the power plant. To address the problem, a framework based on clustering considering time delay has been proposed. A compound parameter, , is put forward in this paper as a strategy to remove the influences from gas volume (power output), inlet NO x concentration, and outlet NO x concentration to the ammonia amount. A modified entropy‐based fuzzy clustering (EFC) method is proposed by a threshold varying model and then tested for its efficiency by four datasets from the University of California, Irvine (UCI) machine learning repository. With the maximum mutual information entropy coefficient (MIC) method for detecting time delay and the modified EFC method, process data from three working levels are handled for automatically obtained clustering centres. The proposed activity value, , is then calculated based on 1440 process data before and after the catalyst replacement shown in boxplot figures. The results of the framework are analyzed to be in accordance with the real working conditions, with values and fluctuation ranges starting to fall near first from the 721st sample in the 24th box.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".