Soft sensor modelling of acrolein conversion based on hidden Markov model of principle component analysis and fireworks algorithm
Bibliographic record
Abstract
Based on the existing propylene oxidation process, it is important to measure acrolein conversion for the production of acrylic acid. The gas chromatographic analyzer is generally used to analyze the acrolein conversion as an off‐line method. In this paper, a soft sensor modelling method of acrolein conversion based on the hidden Markov model with principle component analysis (PCA) and the fireworks algorithm (FWA) is proposed. Firstly, PCA is used to decrease the input variables of hidden Markov model. Then, FWA is applied to optimize the initial parameters of the hidden Markov model. Finally, the hidden Markov model based on PCA and the FWA is employed to predict the acrolein conversion. The proposed method is compared with the support vector machine (SVM), the artificial neural network (ANN), and the hidden Markov method (HMM) to show its superior performance.
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How this classification was reachedexpand
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 | 0.000 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".