Robust optimization of chemical process networks based on Louvain‐ <scp>KBICD</scp> community division rewiring algorithm
Bibliographic record
Abstract
Abstract Previous work using the rewiring algorithm for robust optimization of chemical process networks did not take into account the existence of community structures between networks, thereby reducing the extent of robust optimization. Therefore, this paper proposes a robust optimization of chemical process networks based on the Louvain‐KBICD community division rewiring algorithm. This algorithm firstly employs the K‐shell‐based algorithm with improved comprehensive degree (KBICD) to identify the key nodes of the network; it then proposes the community division of the network based on the Louvain‐KBICD algorithm; and finally, it performs a robust optimization, respectively, by using the rewiring algorithm that reserves the node degree within the communities and the intelligent rewiring algorithm based on the average degree improvement between the communities. The case study proves that the key nodes identification algorithm proposed in this paper solves the problems of low resolution and insufficient identification accuracy of the previous algorithms, and the resolution is improved by 0.6607 and 0.8139 compared with the benchmark algorithm, respectively; the community division algorithm improves the quality of the network community division, and reduces the complexity of the community division, improving the quality of the community division by 11.20% and 14.58%, respectively; and the robust optimization algorithm effectively improves the extent of robust optimization of chemical process networks and preserves the initial community structure of the network while optimizing, meaning the robust optimization extent can reach 62.19%, 80.97% and 64.94%, 76.39% under two attacks, respectively.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".