NDLI: Batch reinforcement learning based dynamic optimization for polyethylene grade transitions

Content Provider	IEEE Xplore Digital Library
Author	Yujie Wei Zhihua Xiong Yongheng Jiang Dexian Huang
Copyright Year	2014
Description	Author affiliation: Dept. of Autom., Tsinghua Univ., Beijing, China (Yujie Wei; Zhihua Xiong; Yongheng Jiang; Dexian Huang)
Abstract	It is necessary for polyethylene grade transitions to establish dynamic optimization in order to preserve competitiveness in the global polymer market. Although the typical technology of iterative learning control has been performed to track the reference trajectories, it is usually difficult to obtain the optimal reference trajectories. A transition from one specific grade to another specific grade can be considered as one batch, therefore, we propose a feasible scheme for dynamic optimization of polyethylene grade transitions based on the batch reinforcement learning, which can derive a best possible policy after a few batches. The scheme aims to integrate the offline reference optimization and online implementations, and explore a better control policy instead of tracking the predetermined trajectories. This designed scheme has three distinct phases, collecting observations from reference and the historical trajectories, learning a policy, and executions. The proposed method is verified by the simulated polyethylene grade transitions, and good performance has been obtained.
Starting Page	7612
Ending Page	7616
File Size	273785
Page Count	5
File Format	PDF
ISBN	9789881563873
ISSN	19341768
DOI	10.1109/ChiCC.2014.6896268
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-28
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	TCCT, CAA
Subject Keyword	polyethylene grade transitions Polyethylene Dynamic optimization observations Approximation algorithms batch reinforcement learning Trajectory Approximation methods Decision trees Optimization
Content Type	Text
Resource Type	Article

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