Deep learning for quality prediction of nonlinear dynamic processes with variable attention‐based long short‐term memory network
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 Industrial processes are often characterized with high nonlinearities and dynamics. For soft sensor modelling, it is important to model the nonlinear and dynamic relationship between input and output data. Thus, long short‐term memory (LSTM) networks are suitable for quality prediction of soft sensor modelling. However, they do not consider the relevance of different input variables with the quality variable. To address this issue, a variable attention‐based long short‐term memory (VA‐LSTM) network is proposed for soft sensing in this paper. In VA‐LSTM, variable attention is designed to identify important input variables according to their relevance with quality prediction. After that, different attention weights are calculated and assigned to further obtain a weighted input sample at each time step. Finally, the LSTM network is exploited to capture the long‐term dependencies of the weighted input time series to predict the quality variable. The performance of the proposed modelling method is validated on an industrial debutanizer column and a hydrocracking process.
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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 | 0.000 |
| 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 it