Published: Nov 4, 2008 3:26:56 PM
Media Contact: , firstname.lastname@example.org, 334.844.3447
Joe Qin, Fluor Professor at the University of Southern California's Viterbi School of Engineering, will discuss data driven process control and operations on Wednesday, Nov. 5, at 3:30 p.m. in the McMillan Auditorium in 136 Ross Hall. His lecture is hosted by Auburn University's Department of Chemical Engineering.
Qin will discuss how automatic control is realized through real-time feedback of information to adjust a set of manipulated variables continuously. A focus of his seminar is the use of multivariate statistical methods for efficient data-driven control and process monitoring. Process monitoring provides supervision of process operations so that abnormal operating conditions can be detected and diagnosed, and proper adjustment can be implemented as needed. Qin will also present a sample-covariance benchmark proposed for control performance monitoring.
Aside from his appointment at USC, Qin is the Chang Jiang Professor affiliated with Tsinghua University by the Ministry of Education of China. Prior to joining the USC faculty, he held the Paul D. and Betty Robertson Meek and American Petrofina Foundation Centennial Professorship in Chemical Engineering at the University of Texas-Austin. He also previously worked as a principal engineer at Fisher-Rosemount.
Qin is a recipient of the National Science Foundation CAREER Award, DuPont Young Professor Award, Halliburton/Brown and Root Young Faculty Excellence Award, NSF-China Outstanding Young Investigator Award and an IFAC Best Paper Prize for the model predictive control survey paper published in Control Engineering Practice.
He obtained his bachelor's and master's degrees in automatic control from Tsinghua University in Beijing. He earned his doctoral degree in chemical engineering from the University of Maryland in 1992. His research interests include system identification; process monitoring and fault diagnosis; model predictive control; run-to-run control; semiconductor process control; and control performance monitoring.