Accelerating process control and optimization via machine learning: a review
importance 4/10
ai assisted
Advanced Control Systems Optimization chemical engineering stage: research
Problem
Identifying and tuning the best solution algorithm for chemical process control and optimization is challenging and time-consuming.
Why it matters
Manual algorithm selection and tuning delays optimization decisions in chemical plants, costing engineering time and process efficiency.
What the AI did
Machine learning tools learn the behavior of numerical solvers from data to automate algorithm selection and configuration for monolithic and decomposition-based optimization methods.
Results
not stated in source
Impact
The review maps recent advances and lists open problems; no new measured impact is stated.
Record
| Venue | Reviews in Chemical Engineering |
|---|---|
| Published | 2025 |
| Work type | article |
| Authors | Ilias Mitrai, Pródromos Daoutidis |
| Citations | 18 (OpenAlex, at import time) |
| References | 156 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1515/revce-2024-0060 |
| Open access | hybrid |
| Keywords | machine learning, process control, optimization, algorithm selection, algorithm configuration, chemical engineering |
Importance rating
4
real but narrower advance
Review of ML for solver selection in chemical process optimization; saves engineering time but no scale or measured impact stated.
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