Two‐level multi‐block operating performance optimality assessment for plant‐wide processes
Bibliographic record
Abstract
Abstract A process operating performance optimality assessment (POPOA) consists of an optimal degree online assessment and non‐optimal cause identification, which contribute to maintaining a high comprehensive economic index (CEI) of the production. However, two main problems limit the application of the traditional POPOA methods, i.e., the plant‐wide process characteristics and the coexistence of both the quantitative and qualitative variables. To overcome the two problems for POPOA, a novel two‐level multi‐block assessment method based on the fuzzy probabilistic rough set (FPRS) is proposed in this research. The operating performance grade of both the global and sub‐block level are properly defined, where the sub‐block assessment indices, which are difficult to obtain, are not required. Different from traditional multi‐block methods due to the novel offline modelling method, an explicit global model is unnecessary. The global performance grade is directly determined by the sub‐block performance grades. When the process is operating at a non‐optimal performance grade, the responsible sub‐block can be rapidly identified through online assessment. The proposed non‐optimal cause identification technique is carried out in the non‐optimal sub‐blocks, based on a newly‐defined matching degree function. The identified non‐optimal causes also contribute to the actual production adjustment to obtain the optimal performance. Finally, the proposed POPOA method is successfully applied to a gold hydrometallurgy process, which is a typical plant‐wide process with hybrid types of variables.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".