Professional Certificate of Competency in Process Control Incorporating Loop Tuning, and Advanced Control Strategies

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

OVERVIEW

This practical course covers all the essentials of process control and tools to optimise 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 optimise 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.

PRACTICAL EXERCISES

Calculating the process gain
Dealing with P, I and D, both individually as well as in combinations, in various loops
Stability aspects
Ziegler Nichols open loop tuning
Ziegler Nichols closed loop tuning
Cohen-Coon tuning
Pessen tuning for some / no overshoot
Trial and error tuning
Saturated and non-saturated output limits
Cascade control
Cascade control with one primary and two secondaries
Ratio control
Feedforward control
Dead time compensation
Gain scheduling
Model predictive controller

THE PROGRAM

MODULE 1: PROCESS CONTROL INTRODUCT ION& CONCEPTS

Topic 1.1: INTRODUCT ION, BAS IC 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

Topic 1.2: BASIC CONTROL CONCEPTS
Typical manual control
Processes, controllers and tuning
First, second and third order processes
Resistive, capacitive and inertia aspects of a process
Practical Demonstration: PID controllers - P, I and D modes of operation

MODULE 2: LOOP TUNING PRINC IPLES AND STAB ILITY

Topic 2.1: BASIC PRINCIPLES OF CONTROL SYSTEMS
Open loop control
Feedback control
On and off control
Modulation control

Topic 2.2: 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
Practical Demonstration: Ziegler-Nichols Open Loop Tuning Method

MODULE 3: SENSORS, TRANS MITTERS AND CONTROL VALVES

Topic 3.1: INTRODUCT ION TO SENSORS AND TRANSMITTERS
Selection and specification of devices
Pressure transmitters
Flow meters
Level transmitter
Temperature sensors

Topic 3.2: INTRODUCTION TO CONTROL VALVES
Basic principles
Rotary and linear control valves
Control valve characteristics and specifications
Hysteresis
Stiction
Practical Demonstration: Ziegler-Nichols Closed Loop Tuning

MODULE 4: SPECIALIZED CONTROLLER SETTINGS AND GOOD PRACTICE

Topic 4.1: 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

Topic 4.2: 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
Practical Demonstration: Pessen - Some and No Overshoot

MODULE 2: LOOP TUNING PR INCIPLES AND STABILITY

Topic 5.1: CASCADE CONTROL
Equation types for cascade control
Initialisation and PV-tracking
Use of multiple outputs in cascade control
Tuning procedure for cascade control

Topic 5.2: FEEDFORWARD CONTROL
Feed forward balance - a control concept
Ratio control
Combined feedforward and feedback Control
The problem of long dead-time in closed loops
Practical Demonstration: Tuning a loop with long lead-times

MODULE 6: EXPERT SYSTEMS AND ADVANCED CONTROL WITH IMC & MPC

Topic 6.1: 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 feed forward

Topic 6.2 MODEL PREDICT IVE CONTROL (MPC)
Single input/output vs multivariable control
Example on a binary column causality graph
Constraints and planning ahead
Different models
Practical Demonstration: Model Predictive Control

NB: The course description of all EIT "Certificate" courses has been changed to "Professional Certificate of Competency". Some course brochures are not yet updated. The actual certificate received by successful students will include the new title.