Title: Monitoring of within batch and batch-to-batch dynamics using adaptive LASSO

Authors: Yan Wang; Yuan Yao; Ying Zheng; David Shan Hill Wong

Addresses: School of Electric and Information Engineering, Zhengzhou University of Light Industry, Henan, Zhengzhou 450002, China ' Department of Chemical Engineering, National Tsing-Hua University, Hsinchu 30013, Taiwan ' School of Automation, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China ' Department of Chemical Engineering, National Tsing-Hua University, Hsinchu 30013, Taiwan

Abstract: The output of a batch process under within batch operation control, and batch-to-batch feedback control can be represented by a two-dimensional auto-regressive moving average (2D ARMA) time series. However, it is not easy to identify this time series under close-loop conditions. In this paper, an adaptive least absolute shrinkage and selection operator (LASSO) method is used to identify the orders and coefficients of this 2D time series. The estimated coefficients of 2D time series model as inputs are to calculate the stability indexes by inner-matrix method. These indexes can be monitored by traditional control charts. On basis of this, the faults are detected by applying on these stability indices. The simulation results show that these stability indexes are sensitive to the faults in 2D batch processes, verifying the effectiveness of the proposed method. Furthermore, the detection capabilities are much superior when adaptive LASSO was used instead of other identification techniques.

Keywords: batch processes; 2D time series; process monitoring; adaptive LASSO; within batch dynamics; batch-to-batch dynamics; feedback control; control charts; SPC; statistical process control; simulation; identification.

DOI: 10.1504/IJSCIP.2015.075898

International Journal of System Control and Information Processing, 2015 Vol.1 No.4, pp.353 - 365

Received: 08 May 2021
Accepted: 12 May 2021

Published online: 12 Apr 2016 *

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