Online operating performance evaluation for the plant‐wide industrial process based on a three‐level and multi‐block method
Bibliographic record
Abstract
Abstract Process operating performance evaluation is of great significance both in theoretical research and practical application. Process operating performance evaluation is used to determine whether or not a production process is running in excellent operating conditions. When the production process is running in a non‐optimal state, non‐optimal reason tracing will be performed to find the reasons causing the non‐optimal running state of the process so that the process can quickly return to the optimal operating state. In this article, multiple working modes and plant‐wide industrial process characteristics are considered. To solve the above problems, a novel multiple three‐level multi‐block hybrid model based online operating performance evaluation approach is proposed. Under each working condition, a three‐level multi‐block model is established. Under the corresponding working mode, each block at the bottom level is evaluated first. Second, the blocks in the middle level are evaluated according to the evaluation results of the bottom level blocks. Finally, according to the evaluation results of the middle level blocks, the operating performance of the top level can be achieved. When non‐optimal operating performance occurs, a variable contribution‐based cause identification technique is developed to locate the variables leading to non‐optimal performance and provide operation strategies to bring the process back to optimal performance. At the end of the paper, the developed evaluation and non‐optimal cause identification approaches are explored in a gold hydrometallurgy process.
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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 | 0.001 | 0.001 |
| 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".