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
Webinar content is available with the kind permission of the author(s) solely for the purpose of furthering AIChE’s & IChemE’s mission to educate, inform and improve the practice of professional chemical engineering. The content reflects the views, opinions, and recommendations of the presenters. AIChE does not warrant or represent, expressly or by implication, the correctness or accuracy of the content of the information presented. All other uses are forbidden without the express consent of the author(s). For permission to re-use, please contact chemepermissions@aiche.org. Attendee contact information, including email addresses, will be shared with AIChE and IChemE, with the option to unsubscribe from future communications.
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