Operating performance assessment based on stacked performance‐relevant enhanced denoising auto‐encoder for industrial processes
Bibliographic record
Abstract
Abstract As an effective way to ensure the economic benefits of enterprises, process operating performance assessment has attracted more and more attention from industry and academia in recent years. In this paper, a stacked performance‐relevant enhanced denoising autoencoder (SPEDAE) network is designed for the operating performance assessment of industrial processes. Compared to the original denoising auto‐encoder (DAE), each performance‐relevant enhanced denoising auto‐encoder (PEDAE) not only reconstructs the input features in the output layer, but also strives to reconstruct the original input data and the performance grade labels simultaneously. Then the SPEDAE is formed by stacking multiple PEDAEs layer by layer. Through this improved training strategy, SPEDAE can avoid accumulated information loss during the deep feature extraction process, improve the robustness of the network, and extract features closely related to the operating performance, thereby better completing the assessment task. The effectiveness of the proposed assessment method is validated on the case of gold cyanide leaching process. Compared with several methods, the proposed SPEDAE has the highest accuracy and reaches 99.85%, which demonstrates its superiority in operating performance assessment.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".