Robust Optimization for Petrochemical Network Design under Uncertainty
Bibliographic record
Abstract
This paper addresses the strategic planning, design, and optimization of a network of petrochemical processes under uncertainty and risk considerations. In this work, we extend the deterministic model proposed by Al-Sharrah et al. [ Ind. Eng. Chem. Res. 2001, 40, 2103; Chem. Eng. Res. Des. 2006, 84, 1019] to account for parameter uncertainty in process yield, raw material cost, product prices, and lower product market demand. The problem was formulated as a two-stage stochastic mixed-integer nonlinear programming model (MINLP). Risk was accounted for in terms of deviation in both projected benefits in the first stage variables and process yield and forecasted demand in terms of the recourse variables. For each term, a different scaling factor was used to analyze the sensitivity of the petrochemical network due to variations of each component. The study showed that the final petrochemical network bears more sensitivity to variations in product demand and process yields for scaling parameters values that maintain the final petrochemical structure obtained form the stochastic model. The concept of expected value of perfect information (EVPI) and value of the stochastic solution (VSS) are also investigated to numerically illustrate the value of including the randomness of the different model parameters. Modeling uncertainty in the process parameters provided a more robust analysis and practical perspective of this type of problem in the chemical industry.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".