Altmann W. Practical Process Control for Engineers and Technicians

Amsterdam: Elsevier, 2005. — 304 p. — ISBN-10 0750664002; ISBN-13 978-0750664004.Basic definitions and terms used in process control
Process modeling
Process dynamics and time constants
Types or modes of operation of process control systems
Closed loop controller and process gain calculations
Proportional, integral and derivative control modes
An introduction to cascade controlProcess measurement and transducers
The definition of transducers and sensors
Listing of common measured variables
The common characteristics of transducers
Sensor dynamics
Selection of sensing devices
Temperature sensors
Pressure transmitters
Flow meters
Level transmitters
The spectrum of user models in measuring transducers
Instrumentation and transducer considerations
Selection criteria and considerations
Introduction to the smart transmitterBasic principles of control valves and actuators
An overview of eight of the most basic types of control valves
Control valve gain, characteristics, distortion and rangeability
Control valve actuators
Control valve positioners
Valve sizingFundamentals of control systems
On–off control
Modulating control
Open loop control
Closed loop control
Deadtime processes
Process responses
Dead zoneStability and control modes of closed loops
The industrial process in practice
Dynamic behavior of the feed heater
Major disturbances of the feed heater
Stability
Proportional control
Integral control
Derivative control
Proportional, integral and derivative modes
I.S.A. vs ‘Allen Bradley’
P, I and D relationships and related interactions
Applications of process control modes
Typical PID controller outputsDigital control principles
Digital vs analog: a revision of their definitions
Action in digital control loops
Identifying functions in the frequency domain
The need for digital control
Scanned calculations
Proportional control
Integral control
Derivative control
Lead function as derivative control
Example of incremental form (Siemens S5-100 V)Real and ideal PID controllers
Comparative descriptions of real and ideal controllers
Description of the ideal or the non-interactive PID controller
Description of the real (Interactive) PID controller
Lead function – derivative control with filter
Derivative action and effects of noise
Example of the KENT K90 controllers PID algorithmsTuning of PID controllers in both open and closed loop control systems
Objectives of tuning
Reaction curve method (Ziegler–Nichols)
Ziegler–Nichols open loop tuning method ()
Ziegler–Nichols open loop method () using POI
Loop time constant (LTC) method
Hysteresis problems that may be encountered in open loop tuning
Continuous cycling method (Ziegler–Nichols)
Damped cycling tuning method
Tuning for no overshoot on start-up (Pessen)
Tuning for some overshoot on start-up (Pessen)
Summary of important closed loop tuning algorithms
PID equations: dependent and independent gainsController output modes, operating equations and cascade control
Controller output
Multiple controller outputs
Saturation and non-saturation of output limits
Cascade control
Initialization of a cascade system
Equations relating to controller configurations
Application notes on the use of equation types
Tuning of a cascade control loop
Cascade control with multiple secondariesConcepts and applications of feedforward control
Application and definition of feedforward control
Manual feedforward control
Automatic feedforward control
Examples of feedforward controllers
Time matching as feedforward controlCombined feedback and feedforward control
The feedforward concept
The feedback concept
Combining feedback and feedforward control
Feedback–feedforward summer
Initialization of a combined feedback and feedforward control system
Tuning aspectsLong process deadtime in closed loop control and the Smith Predictor
Process deadtime
An example of process deadtime
The Smith Predictor model
The Smith Predictor in theoretical use
The Smith Predictor in reality
An exercise in deadtime compensationBasic principles of fuzzy logic and neural networks
Introduction to fuzzy logic
What is fuzzy logic?
What does fuzzy logic do?
The rules of fuzzy logic
Fuzzy logic example using five rules and patches
The Achilles heel of fuzzy logic
Neural networks
Neural back propagation networking
Training a neuron network
Conclusions and then the next stepSelf-tuning intelligent control and statistical process control
Self-tuning controllers
Gain scheduling controller
Implementation requirements for self-tuning controllers
Statistical process control (SPC)
Two ways to improve a production process
Obtaining the information required for SPC
Calculating control limits
The logic behind control charts