A Deep Reinforcement Learning Approach to Improve the Learning Performance in Process Control-Reference-Cited by-同舟云学术

A Deep Reinforcement Learning Approach to Improve the Learning Performance in Process Control

Published:2021-04-06 Issue:15 Volume:60 Page:5504-5515
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Bao Yaoyao¹,Zhu Yuanming¹^ORCID,Qian Feng¹^ORCID

Affiliation:

1. Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

Funder

Ministry of Education of the People's Republic of China

National Natural Science Foundation of China

National Natural Science Fund for Distinguished Young Scholars

Programe of Introducing Talents of Discipline to Universities

Publisher

American Chemical Society (ACS)

Subject

Industrial and Manufacturing Engineering,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.0c05678

Reference38 articles.

1. Anodization control for barrier-oxide thinning and 3D interconnected pores and direct electrodeposition of nanowire networks on native aluminium substrates

2. Status of Flue Gas Desulphurisation (FGD) systems from coal-fired power plants: Overview of the physic-chemical control processes of wet limestone FGDs

3. Hierarchical decompositions for MPC of resource constrained control systems: applications to building energy management

4. Multistage NMPC with on-line generated scenario trees: Application to a semi-batch polymerization process

5. Fundamental Theories and Key Technologies for Smart and Optimal Manufacturing in the Process Industry

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