Trends and perspectives in deterministic MINLP optimization for integrated planning, scheduling, control, and design of chemical processes
Bibliographic record
Abstract
Mixed integer nonlinear programming (MINLP) in chemical engineering originated as a tool for solving optimal process synthesis and design problems. Since then, the application of MINLP has expanded to encompass control and operational decisions that are in line with the arising challenges faced by the industry, e.g., sustainability, competitive markets, and volatile supply chain environments. Nowadays, process plants are transitioning from traditional manufacturing practices to automated solutions able to integrate decision-making within manufacturing enterprises. This paradigm shift aims to increase profits, optimize resource utilization efficiency, promote long-term sustainability, minimize waste, and enhance responsiveness under uncertainties and perturbations. Accordingly, the development of reliable, computationally efficient, and robust MINLP algorithms capable of simultaneously handling process design, planning, scheduling, or control decisions are crucial to achieving Industry 4.0 integration goals. This work explores potential research opportunities and recent advances toward the development of integrated decision-making frameworks, focusing on their underlying state-of-the-art optimization tools. We provide an overview of emerging deterministic MINLP optimization algorithms for simultaneous decision-making problems. Furthermore, we constructively discuss the versatility and limitations of these optimization tools. We also highlight how novel optimization theories, both within and outside the chemical engineering domain, can be incorporated into advanced MINLP frameworks suitable for process integration.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".