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Introduction

In recent years, driven by the rapid development of electronic, information and communication technologies, the great changes of industrial environment have been witnessed. Due tothe ever increasingdema nds o n product q uality a nd econo mic be nefit, no t onl y the intellige nt co mpo ne nts and devicesare implemented and networked, but also the real-time supervision and controlsystems are running parallel, and the automation degree in modernind ustrial syste ms,such as chemical process, aerospace, nuclear power plant, ship powersystem, transportation s ystem, is continuously growing. Modeling these processes accurately via first principles is becoming time-consuming and impractical, and the supervision and control systems designed based on the process model may only be efficient for a short period in the life-time of the industrial processes.

This challenges and pushes scientists and engineers to develop advanced process monitoring and control methodologies using offline stored or online process data to solve optimal process monitoring and control issues. Therefore, it would be very meaningful if the reliable and efficient process monitoring and control systems could be directly designed based on the i nfor ma tion extracted from the offline stored or online process data. Driven by this reason, the development of advanced data-driven process monitoring and control methodologies is significant in bothresearch and industrial application domains.

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Submission Topics

Topics of the Session:

  • Data-driven process monitoring approaches and applications

  • Model-free or data-driven control design approaches and applications

  • Data-driven performance evaluation, diagnosis, decisions and their applications

  • Data-driven optimization methods and applications

  • Real-ti me model-free learning methods and practical applications

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Important Date
  • Conference Date

    Oct 29

    2017

    to

    Nov 01

    2017

  • Nov 01 2017

    Registration deadline

Sponsored By
IEEE工业电子学会
Organized By
Southeast University
Institute of Automation, Chinese Academy of Sciences
School of Mathematics and Systems Science, Chinese Academy of Sciences