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Now available - three courses for the advanced control engineer on alarm engineering, model identification, and controller performance assessment (more...)

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Control Arts offers the following courses for Big Data and Plant Control engineers who need to understand the theory, application, benefits, and limitations data mining and current control technology. Each course lasts one day and is presented by lecturers who have extensive theoretical and practical experience applying the technologies. Attendees are encouraged to bring data from their own plants for inclusion in the course tutorials.


Engineering of Alarm Systems

Alarm system engineering encompasses a wide variety of theoretical fields, from signal processing to applied statistics to data mining. This course covers the application of these fields to alarm systems, using real plant data to illustrate how to configure the alarm system for optimal performance.

Topics covered include:

  • Statistical quality control of alarm system performance
  • Determination of optimal alarm trip points
  • Alarm deadband design
  • Filters for chattering alarms
  • Algorithms for determining redundant, sequential, and grouped alarms
  • Evaluating controller performance using alarms

Model Identification for Process Control

There is a wealth of theory in model identification, but most identification packages either "hide" these techniques from the user or ignore them entirely. The practical aspects of model identification theory will be discussed in this course, with emphasis on modelling for both basic and advanced mutlivariable controllers.

Topics covered include:

  • Planning of plant tests
  • Different plant models (continuous, discrete, step-weight, etc.)
  • Disturbance models
  • Estimation routines
  • Closed-loop plant testing
  • Model robustness
  • Analysis of open-loop systems
  • Filter design

Assessment of Controller Performance

Controller performance indices are often a comparison to the performance which would have been seen if an "optimal" controller had been applied to the system. But what is this "optimal" controller? How is its performance estimated without plant tests? What information can be obtained from these indices? This course will look at performance indices, and discuss their uses, assumptions on which they are based, and advantages and limitations.

Topics covered include:

  • Controller performance indices
  • Data requirements for controller performance measurement
  • Stiction and hysteresis indices
  • Time series and frequency responses
  • Cycling detection algorithms

For more information on the above courses, contact Control Arts.

Control Arts Inc. 2014