Process operating performance optimality assessment with coexistence of quantitative and qualitative information
Bibliographic record
Abstract
Abstract When the process is working normally, process operating performance optimality assessment makes a further judgment on how well the process is running currently. Sustained optimal process operating performance guarantees the greatest economic benefits. However, the operating performance may deteriorate from the optimal status due to the production condition shifts. It is crucial to accomplish operating performance optimality assessment strategies for a deeper understanding of the process status. A Dynamic Causality Diagram (DCD) explicitly and intuitively describes the causal correlations between variables. However, an actual process has both quantitative and qualitative measurements, which makes the conventional DCD imprecise or even invalid. To handle the above problems, a modified DCD is proposed to assess a complicated process with coexistence of quantitative and qualitative information. An operating performance optimality assessing strategy based on the proposed DCD is then established. The proposed method is successfully applied to a gold hydrometallurgy process, and the operating performance optimality assessment result is satisfied.
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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 | 0.000 | 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 | 0.001 |
| 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".