Multivariate statistical process monitoring of batch-to-batch startups

• Published in: AIChE journal Vol. 61; no. 11; pp. 3719 - 3727
• Main Authors: Yan, Zhengbing, Huang, Bi-Ling, Yao, Yuan
• Format: Journal Article
• Language: English
• Published: New York Blackwell Publishing Ltd 01.11.2015; American Institute of Chemical Engineers
• Subjects: batch process; Chemical engineers; Decomposition; ensemble empirical mode decomposition; Feasibility; Injection molding; instantaneous frequency; Mathematical analysis; Monitoring; Multivariate analysis; multivariate statistical process monitoring; Process engineering; signal decomposition; Signal processing; Trajectories
• ISSN: 0001-1541; 1547-5905
• DOI: 10.1002/aic.14939

In batch processes, multivariate statistical process monitoring (MSPM) plays an important role for ensuring process safety. However, despite many methods proposed, few of them can be applied to batch‐to‐batch startups. The reason is that, during the startup stage, process data are usually nonstationary and nonidentically distributed from batch to batch. In this article, the trajectory signal of each process variable is decomposed into a series of components corresponding to different frequencies, by adopting a nonparametric signal decomposition technique named ensemble empirical mode decomposition. Then, through instantaneous frequency calculation, these components can be divided into two groups. The first group reflects the long‐term trend between batches, which extracts the batch‐wise nonstationary drift information. The second group corresponds to the short‐term intrabatch variations. The variable trajectory signals reconstructed from the latter fulfills the requirements of conventional MSPM. The feasibility of the proposed method is illustrated using an injection molding process. © 2015 American Institute of Chemical Engineers AIChE J, 61: 3719–3727, 2015