Deep Learning of Complex Batch Process Data and Its Application on Quality Prediction
Bibliographic record
Abstract
Batch process quality prediction is an important application in manufacturing and chemical industries. The complexity of batch processes is characterized by multiphase, nonlinearity, dynamics, and uneven durations so that modeling of these batch processes is rather difficult. Moreover, there are other challenges in the face of quality prediction. Specifically, the process trajectories over the whole running duration potentially make specific contributions to the final targets so that the prediction issue embraces tremendously high-dimensional inputs but very low-dimensional outputs. This means that the prediction suffers from a severe dimensional imbalance between inputs and outputs. Motivated by these difficulties, this paper proposes a new deep learning-based framework for complex feature representative and quality prediction. Long short-term memory (LSTM) is used to extract comprehensive quality-relevant hidden features from a long-time sequence in each phase, significantly reducing the predictor dimensions. And these features from different phases are further integrated and compressed by a stacked auto-encoder (SAE). A practical industrial example testifies to the efficacy of the proposed framework.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".