Process Data Analytics and Machine Learning Methods
- Type: Archived Webinar
- Level: Intermediate
- Duration: 1 hour
- PDHs: 1.00
Could recent advances in machine learning improve process data analytics in your facility? Take this webinar to learn more.
In 60 minutes, you’ll gain a historical perspective on the process data analytics based on machine learning and latent variable methods and the need to distill desirable features from measured data under routine operations. You’ll then examine several statistical machine learning methods that could have vast applications in process data analytics, including a new method that models high dimensional dynamic time series data to extract the most dynamic latent variables. Through an industrial case study, you’ll see how real process data are efficiently and effectively modeled using these dynamic methods to extract features for process operations and control. And, you’ll gain a new understanding of how process data are indispensable for manufacturing process troubleshooting, diagnosis and effective control.
Take a look at your agenda:
- A historical perspective on process data analytics
- New statistical machine learning methods with vast applications
- An industrial case study: Lessons learned
- How process data are indispensable to your manufacturing process
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 currently Director of the Center for Machine and Process Intelligence and Fluor Professor at the Viterbi School of Engineering of the University of Southern California.
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...Read more
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