Academy Offers

Get a 20% discount on eLearning course purchases made through July 31. Use code ACADEMY20 at check out.

Through June 30, AIChE members will receive complimentary, unlimited access to live and on-demand AIChE webinars by purchasing them with their newly increased number of credits. See more resources.

Process Data Analytics and Machine Learning Methods

Originally delivered Sep 19, 2018
Source: AIChE
  • Type:
    Archived Webinar
  • Level:
  • Duration:
    1 hour
  • PDHs:

Share This Post:

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 

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 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

Once the content has been viewed and you have attested to it, you will be able to download and print a certificate for PDH credits. If you have already viewed this content, please click here to login.



Do you already own this?



AIChE Member Credits 1
AIChE Members $69.00
AIChE Graduate Student Members Free
AIChE Undergraduate Student Members Free
Non-Members $99.00
Webinar content is available with the kind permission of the author(s) solely for the purpose of furthering AIChE’s mission to educate, inform and improve the practice of professional chemical engineering. All other uses are forbidden without the express consent of the author(s).