清华化工论坛第五十七讲将于9月10日14:00在英士楼201会议室举行🖕🏻🏋🏻‍♂️，欢迎各位老师参加！

报告题目：Process Data Analytics for Troubleshooting of Feedback Controlled Manufacturing Plants

报告人： Prof. S. Joe Qin University of Southern California

报告时间：9月10日14:00-15:00

报告地点：英士楼201会议室

PROCESS DATA ANALYTICS FOR TROUBLESHOOTING

OF FEEDBACK CONTROLLED MANUFACTURING PLANTS

S Joe Qin

The Mork Family Department of Chemical Engineering and Materials Science

Ming Hsieh Department of Electrical Engineering

University of Southern California

Los Angeles, CA 90089 USA

sqin@usc.edu

Abstract

Although manufacturing process systems collect and store massive amount of data from routine operations with computer control systems, most control theory and practice research to date have focused on either system identification where the data are collected with carefully designed experiments or on fault detection where the normal process models are assumed to be available. It is also clear that many processes have poor control performance and often exhibit dynamic oscillations, albeit over a century of stability theory exists. We make a proposition that these undesirable performances are due to uncertainty and abnormal situations that develop during routing operations and go beyond the capability of normal models. We further assert that routing data contain up to date situational knowledge about the process performance and abnormal situational knowledge, that can be effectively mined by properly analyzing operation data. Since the massive operation data are usually dynamic but are far from being fully excited, theory and methods are needed to analyze these data where the dynamics exist only in a subspace of the high dimensional measurement space.

In this talk we first provide a historical perspective on the process data analytics based on latent variables modeling methods and machine learning, and the objectives to distill desirable components or features from measured data under routine operations. These methods are then extended to modeling high dimensional dynamic time series data to extract the most dynamic latent variables. We show with an industrial case study how real process data are efficiently and effectively modeled using these dynamic methods to extract features for process operations and control, leading to new perspectives on how process data are indispensable for manufacturing process troubleshooting, diagnosis, and effective control.

Bio -- S. Joe Qin

Dr. S. Joe Qin obtained his B.S. and M.S. degrees in Automatic Control from Tsinghua University in Beijing, China, in 1984 and 1987, respectively, and his Ph.D. degree in Chemical Engineering from University of Maryland at College Park in 1992. He is Director of the Center for Machine and Process Intelligence and Professor at the Viterbi School of Engineering of the University of Southern California and Guest Chair Professor at the Chinese University of Hong Kong, Shenzhen.

Dr. Qin is a Fellow of AIChE, Fellow of IEEE,and Fellow of the International Federation of Automatic Control (IFAC). He is a recipient of the National Science Foundation CAREER Award, the 2011Northrop Grumman Best Teaching award at Viterbi School of Engineering, theDuPont Young Professor Award, Halliburton/Brown & Root Young Faculty Excellence Award, NSF-China Outstanding Young Investigator Award, and recipient of the IFAC Best Paper Prize for a model predictive control survey paper published inControl Engineering Practice.He is currently a Subject Editor forJournal of Process Controland a Member of the Editorial Board forJournal of Chemometrics. He has published over 140 papers in SCI journals or book chapters. He has over10,900 Web of Science citations with an associated h-index of50 and over 30,000 Google Scholar citations. He delivered over 40 invited plenary or keynote speeches and over 100 invited technical seminars worldwide. Dr. Qin’s research interests include process data analytics, machine learning, process monitoring and fault diagnosis, model predictive control, system identification,building energy optimization,multi-step batch process control, and control performance monitoring.