A data‐based optimal setting method for the coking flue gas denitration process
Bibliographic record
Abstract
Abstract This study focuses on developing an optimal setting method for the first integrated coking flue gas desulphurization and denitration device in China. Maintaining the denitration process in a state of optimal economic efficiency has become an issue in production optimization control. This paper proposes a data‐based two‐stage nonparametric optimization method to optimize the operation of the denitration process. A principal component regression (PCR)‐based multiple case fusion case‐based reasoning (CBR) method is proposed to obtain the initial optimization set points. To overcome the steady‐state modelling difficulties associated with the process, a local modelling method for the coking flue gas denitration process is developed using an improved just‐in‐time learning (JITL) algorithm. Taking the preset values obtained above as the initial value of an active set algorithm, the optimization problem can be solved in a timely and precise manner. The intelligent setting software has been developed for running industrial applications, and the results demonstrate the effectiveness of the proposed optimization approach.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".