University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Prediction and Uncertainty Propagation for Completion Time of Batch Processes based on Data-driven Modeling

Zhou, Le, Chuang, Yao-Chen, Hsu, Shao-Heng, Yao, Yuan and Chen, Tao (2020) Prediction and Uncertainty Propagation for Completion Time of Batch Processes based on Data-driven Modeling Industrial and Engineering Chemistry Research.

[img] Text
Revised Paper-0523.docx - Accepted version Manuscript
Restricted to Repository staff only until 16 July 2021.

Download (1MB)

Abstract

Batch processes have been playing a crucial role for the flexibility in producing low-volume and high-value-added products. Due to the fluctuations of raw materials and operation conditions, the batch duration often varies. Prediction of batch completion time is important for the purpose of process scheduling and optimization. Existing studies of this subject have been focused on the prediction accuracy, while the importance of the prediction uncertainty has been under-explored. When the key variable defining the completion time changes slowly towards the end of a batch, the prediction uncertainty tends to be large. Under such situations, we argue that the uncertainty should always be considered along with the mean prediction for practical use. To this end, two data-driven prediction methods using probabilistic principal component analysis (PPCA) and bootstrapping case-based reasoning (Bootstrapping CBR) are developed, followed by the uncertainty quantification in the probabilistic framework. Finally, two batch processes are used to demonstrate the importance of prediction uncertainty and the efficiency of the proposed schemes.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Chemical and Process Engineering
Authors :
NameEmailORCID
Zhou, Le
Chuang, Yao-Chen
Hsu, Shao-Heng
Yao, Yuan
Chen, TaoT.Chen@surrey.ac.uk
Date : 15 July 2020
Funders : NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization, National Natural Science Foundation of China, Ministry of Science and Technology, ROC, Zhejiang Provincial Natural Science Foundation of China, BBSRC
Grant Title : NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization
Uncontrolled Keywords : Completion time prediction; Uncertainty propagation; Batch process; Probabilistic principal component analysis; Bootstrapping case-based reasoning; Prediction uncertainty
Additional Information : Embargo OK Metadata Pending
Depositing User : James Marshall
Date Deposited : 16 Jul 2020 10:07
Last Modified : 16 Jul 2020 10:21
URI: http://epubs.surrey.ac.uk/id/eprint/858223

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800