Name
Abstract | ||
|---|---|---|
Batch or semi-batch processing is becoming more prevalent in industrial chemical manufacturing but it has not benefited from advanced control technologies to a same degree as continuous processing. This is due to its several unique aspects which pose challenges to implementing model-based optimal control, such as its highly nonstationary operation and significant run-to-run variabilities. While existing advanced control methods like model predictive control (MPC) have been extended to address some of the challenges, they still suffer from certain limitations which have prevented their widespread industrial adoption. Reinforcement learning (RL) where the agent learns the optimal policy by interacting with the system offers an alternative to the existing model-based methods and has potential for bringing significant improvements to industrial batch process control practice. With such motivation, this paper examines the advantages that RL offers over the traditional model-based optimal control methods and how it can be tailored to better address the characteristics of industrial batch process control problems. After a brief review of the existing batch control methods, the basic concepts and algorithms of RL are introduced and issues for applying them to batch process control problems are discussed. The nascent literature on the use of RL in batch process control is briefly reviewed, both in recipe optimization and tracking control, and our perspectives on future research directions are shared. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.arcontrol.2021.10.006 | Annual Reviews in Control |
Keywords | DocType | Volume |
Reinforcement learning,Batch process,Batch optimization,Batch control | Journal | 52 |
ISSN | Citations | PageRank |
1367-5788 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haeun Yoo | 1 | 0 | 0.34 |
| Ha-Eun Byun | 2 | 0 | 0.34 |
| Dongho Han | 3 | 0 | 0.34 |
| Jay H. Lee | 4 | 501 | 80.37 |