Process Control Fundamentals & AI Applications

After completion of this course, the participants will acquire knowledge of different aspects of Process Control.

The course includes all the areas of Process Control:

• PID controller tuning, including use of a simulation to demonstrate some of the concepts.
• Base layer control.
  • PID control is the most common base layer control algorithm.
  • Complex control schemes
• Quality estimators.
• Multivariable Predictive Control
• Dynamic process simulation with live demonstration in Unisim Dynamics
• Troubleshooting of control systems through monitoring. Interaction with users and analysis
• Use of AI in Process Control (on client’s request)

DAY 1 – Basics of Process Control

• Introduction
  • Presenter & Participants
  • What is Process Control?
  • Course outline
• PID Controller tuning
  • Importance of PID tuning to overall plant performance.
  • PID control description, including the elements of the PID control loop, First Order plus Deadtime process model
  • Tuning parameters
  • Tuning a controller, including examples on a controller simulation, and covering the differences in control of the main process parameters of flow, temperature, pressure and level.
• Base Layer Control Review

DAY 2 – Basics of Process Control (continued)

• Base Layer Control
  • PID control
  • Cascade control
  • Split Range
  • Override control
  • Static decoupling
  • Furnace coil balancing
  • Air/fuel ratio control
  • Compressor control
• Quality Estimators
  • Why use them?
  • Simple linear regression or calculation
  • Commercially available products.

DAY 3

• Multivariable Predictive Control (MPC)
  • Why use it?
  • General MPC characteristics.
  • APC project outline.
• Dynamic Process Simulation (Unisim Dynamics)
  • Why use it?
  • General structure
• Control system troubleshooting
  • What goes wrong?
  • Control system monitoring through automation and interaction with users.
  • Analysis and resolution
• Final Q & A