Reinforcement learning for batch process control: Review and perspectives

Yoo, Haeun; Byun, Ha-Eun; Han, Dongho; Lee, Jay H.

doi:10.1016/j.arcontrol.2021.10.006

Cited by 41 publications

(20 citation statements)

References 55 publications

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“…While PID and model predictive (MPC) controllers dominate industrial practice, reinforcement learning is an attractive alternative, 29,176 as it has the potential to outperform existing techniques in a variety of applications, such as online optimization and control of batch processes. 177 We only discuss model-free reinforcement learning here, as model-based reinforcement learning is very closely related to data-driven MPC for chemical process applications, and a full discussion on this topic is out of the scope of this section.…”

Section: Reaction Chemistry and Engineering Reviewmentioning

confidence: 99%

Industrial data science – a review of machine learning applications for chemical and process industries

et al. 2022

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Section: Reaction Chemistry and Engineering Reviewmentioning

confidence: 99%

Industrial data science – a review of machine learning applications for chemical and process industries

et al. 2022

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“…Although RL has been demonstrated for sequential decision making in a number of case studies [21,22,23,24], its application to physical production systems has been relatively limited. For example, [25] applied deep Q networks to optimize a flexible jobshop (i.e.…”

Section: Online Production Scheduling and Reinforcement Learningmentioning

confidence: 99%

Distributional Reinforcement Learning for Scheduling of Chemical Production Processes

Mowbray¹,

Zhang²,

Chanona³

2022

Preprint

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Reinforcement Learning (RL) has recently received significant attention from the process systems engineering and control communities. Recent works have investigated the application of RL to identify optimal scheduling decision in the presence of uncertainty. In this work, we present a RL methodology tailored to efficiently address production scheduling problems in the presence of uncertainty. We consider commonly imposed restrictions on these problems such as precedence and disjunctive constraints which are not naturally considered by RL in other contexts. Additionally, this work naturally enables the optimization of risk-sensitive formulations such as the conditional value-at-risk (CVaR), which are essential in realistic scheduling processes. The proposed strategy is investigated thoroughly in a parallel batch production environment, and benchmarked against mixed integer linear programming (MILP) strategies. We show that the policy identified by our approach is able to account for plant uncertainties in online decision-making, with expected performance comparable to existing MILP methods. Additionally, the framework gains the benefits of optimizing for risk-sensitive measures, and identifies online decisions orders of magnitude faster than the most efficient optimization approaches. This promises to mitigate practical issues and ease in handling realizations of process uncertainty in the paradigm of online production scheduling.

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“…14–16 Conventional techniques cannot meet the requirements of large-scale production due to batch-to-batch variability. 17–20…”

Section: Introductionmentioning

confidence: 99%

Continuous and large-scale synthesis of Ni–Co PBA nanoparticles with a tunable particle size in a microreactor

Wang

Liu

et al. 2023

New J. Chem.

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Reinforcement learning for batch process control: Review and perspectives

Cited by 41 publications

References 55 publications

Industrial data science – a review of machine learning applications for chemical and process industries

Industrial data science – a review of machine learning applications for chemical and process industries

Distributional Reinforcement Learning for Scheduling of Chemical Production Processes

Continuous and large-scale synthesis of Ni–Co PBA nanoparticles with a tunable particle size in a microreactor

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