Development of a novel parallel framework upon deep dual‐enhanced autoencoder and its applications for industrial soft sensing
Bibliographic record
Abstract
Abstract In recent years, data‐driven soft sensing technology has provided a cost‐effective support for industrial process monitoring, in which autoencoder plays an important role in extracting features for soft sensing technology. However, existing autoencoder models take a long time for modelling, and the extracted features have difficulty considering both key variable and process variables. To address these issues, this paper proposes a parallel gated deep input‐enhanced supervised autoencoder (PGDISAE) model. Different from the standard deep autoencoder model, the proposed model improves the feature extraction performance of deep autoencoder by embedding input variables into hidden layers through supervised learning in the pre‐training stage while embedding output variable into decoder layer. The gated strategy makes full use of the abstract representation of each hidden layer. Additionally, this model adopts a data parallel training strategy which can drastically reduce the training time. The experimental results on the sulphur recovery unit and primary reformer process verify the effectiveness and feasibility of the proposed model in senses of accuracy and time‐efficiency.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".