Multiobjective particles swarm optimization and multicriteria decision making of improved cumene production process including economic, environmental, and safety criteria
Bibliographic record
Abstract
Abstract Cumene is one of the five chemicals with the highest production in the world. In this work, the design by Flegiel was improved to increase the production rate of the cumene process by adding a trans‐alkylation reactor, then multi‐objective optimization (MOO) using the particles swarm optimization (PSO) algorithm is used to improve the process design. Furthermore, seven multicriteria decision‐making (MCDM) methods for selecting an optimal solution from the Pareto‐optimal front related to two MOO problems were performed. In this optimization, conflicting objectives such as total capital cost (TCC), energy cost, wastage rate, and safety target are simultaneously minimized in the format of trade‐offs. Finally, the results of this work were compared with those reported designs. The optimal solution chosen by MCDM methods is at TCC = 5589, damage index (DI) = 0.044, and material loss = 0.0005.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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".