An ontology‐based procedure knowledge framework for the process industry
Bibliographic record
Abstract
Abstract Process industry enterprises rely heavily on expert experience in production, and expert knowledge is stored in multiple formats like, pictures, texts, videos, etc. Several isolated islands of information or knowledge are formed, and they are difficult to store, share, and expand upon. Therefore, the same procedure appears multiple times in different application scenarios, such as optimization scheduling, optimization operation, or fault diagnosis. If redundant knowledge is paid too much attention, decision‐makers will not be able to comprehensively consider business knowledge. Thus, a novel framework is proposed for the process industry to manage procedure knowledge. This paper first divides the detailed procedure of process plants into four layers, that is, raw materials, intermediate materials, operation, and products based on the P‐graph theory, which involves virtual hierarchies, nodes, and operations. Second, the domain ontology‐based procedure knowledge model is constructed. Finally, two experimental cases, the Tennessee Eastman (TE) and the ethylene production process, are studied to verify the procedure knowledge framework (PKF). The PKF provides a useful foundation for the subsequent construction of a superstructure model based on the P‐graph theory and is different from the previous industrial process industrial ontology model. The PKF has a theoretical basis for simulation calculation and makes future work based on PKF more accurate and interpretable.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".