This practical course covers all the essentials of process control and tools to optimize the operation of your plant and process, and regards the process, from the primary measuring device, through the controller, right down to the final control element as a chain with important links. Controllers need to be carefully matched to the process to work optimally; this matching procedure is called tuning. Controllers that are not correctly configured and tuned will not perform optimally and will not reduce variability in the process as they should. It is aimed at engineers and technicians who wish to have a clear, practical understanding of the essentials of instrumentation and final control elements typically found in common loops. It incorporates loop tuning, as well as how to optimize the operation of their particular plant or process. Mathematical theory has been kept to a minimum with the emphasis throughout on practical applications and useful information.

But it does not stop there. Advanced Process Control (APC) is an essential part of the modern plant. Small differences in process parameters can have large effects on profitability; get it right and profits continue to grow; get it wrong and there are major losses. Many applications of APC have pay back times well below one year. APC does require a detailed knowledge of the plant to design a working system and continual follow up along the life of the plant to ensure it is working optimally. Cascade Control, Feedforward control, control with long dead times, IMC and MPC are all considered, with respect to different applications. At the end of this course you will have the skills to troubleshoot / tune / deal with / understand a wide variety of process loops.

WHAT YOU WILL LEARN:

  • Basic control concepts
  • Introduction to sensors and transmitters
  • Different types of processes you may encounter
  • Different types of control
  • Optimum amount of filtering or dampening to apply to the measurement
  • Impact of control valves on control loop performance
  • PID controller behaviours
  • Troubleshoot and identify problems
  • When to use derivative control for the best tuned loop
  • Differences between ideal/real/ interacting/ non-interacting controllers
  • Combination of control modes to use
  • Cascade control
  • Feed forward control
  • Significance of dead time and transfer lags
  • Expert systems
  • Justification for advanced control
  • Internal Model Control (IMC)
  • Model Predictive Control (MPC)
  • MPC representation, identification and observation

Registrations for the March 16th, 2014 intake are now open – contact us now

 

The Programs

  1. Calculating the process gain
  2. Dealing with P, I and D, both individually as well as in combinations, in various loops
  3. Stability aspects
  4. Ziegler Nichols open loop tuning
  5. Ziegler Nichols closed loop tuning
  6. Cohen-Coon tuning
  7. Pessen tuning for some / no overshoot
  8. Trial and error tuning
  9. Saturated and non-saturated output limits
  10. Cascade control
  11. Cascade control with one primary and two secondaries
  12. Ratio control
  13. Feedforward control
  14. Dead time compensation
  15. Gain scheduling
  16. Model predictive controller


COURSE OUTLINE

MODULE 1: PROCESS CONTROL INTRODUCTION, BASIC TERMS AND DEFINITIONS

  • Definitions of process variable, controlled variable and manipulated variable
  • Process gain, dead time and time constants
  • Speed, stability and robustness
  • Process noise

MODULE 2: BASIC CONTROL CONCEPTS

  • Typical manual control
  • Processes, controllers and tuning
  • First, second and third order processes
  • Resistive, capacitive and inertia aspects of a process

MODULE 3: LOOP TUNING PRINCIPLES: BASIC PRINCIPLES OF CONTROL SYSTEMS

  • Open loop control
  • Feedback control
  • On and off control
  • Modulation control

MODULE 4: STABILITY AND CONTROL MODES OF CLOSED LOOPS

  • Cause of instability in control loops
  • Change of stability through PID control modes
  • Methods to improve stability
  • Principles of closed loop control tuning
  • Different rules compared
  • Rules of thumb in tuning

MODULE 5: INTRODUCTION TO SENSORS AND TRANSMITTERS

  • Selection and specification of devices
  • Pressure transmitters
  • Flow meters
  • Level transmitter
  • Temperature sensors

MODULE 6: INTRODUCTION TO CONTROL VALVES

  • Basic principles
  • Rotary and linear control valves
  • Control valve characteristics and specifications
  • Hysteresis
  • Stiction

MODULE 7: SPECIALIZED CONTROLLER SETTINGS AND GOOD PRACTICE: IDEAL PID VS REAL PID

  • Non-field-interactive or ideal PID
  • Field-interactive or real PID
  • Selection of ideal or real PID
  • Choice of saturated vs non-saturated output limits

MODULE 8: GOOD PRACTICE FOR TUNING OF CLOSED LOOP CONTROL

  • Good practice for common loop problems
  • Flow control loop characteristics
  • Level control loop characteristics
  • Temperature control loop characteristics
  • Pressure control loop characteristics
  • Other less common loops

MODULE 9: LOOP TUNING PRINCIPLES AND STABILITY: CASCADE CONTROL

  • Equation types for cascade control
  • Initialisation and PV-tracking
  • Use of multiple outputs in cascade control
  • Tuning procedure for cascade control

MODULE 10: FEEDFORWARD CONTROL

  • Feedforward balance - a control concept
  • Ratio control
  • Combined feedforward and feedback control
  • The problem of long dead-time in closed loops

MODULE 11: EXPERT SYSTEMS AND MODEL BASED SELF TUNING CONTROLLERS

  • Self tuning loops
  • Adaptive control
  • Fuzzy logic control
  • Gain scheduling
  • JUSTIFICATION OF ADVANCED CONTROL
  • Advanced vs classical control
  • Advanced on-line control vs statistical process control
  • Comparison of pay back time on real examples
  • INTERNAL MODEL CONTROL (IMC)
  • Open loop model in parallel with the process
  • Control system in two blocks
  • Equivalence with a classical controller
  • Disturbances rejection and control
  • IMC and delays and feedforward

MODULE 12: MODEL PREDICTIVE CONTROL (MPC)

  • Single input/output vs multivariable control
  • Example on a binary column causality graph
  • Constraints and planning ahead
  • Different models

 

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How can an e-learning course be interactive?

Boredom can be a real danger, however, we use an interactive approach to our e-Learning – with live sessions instead of recordings.  The webinar software allows everyone to interact and involves participants in group work; including hands-on exercises with simulation software and remote laboratories where possible.  You can communicate with text messages, or live VoIP speech, or can even draw on the whiteboard during the sessions.

 

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