Guide to Process Control--Article Index

0. Overview/Article Index (Outline of Contents) [this page]

1. Introduction

• Goals
• Introduction
• Process control--Basic definitions and terms
• Process modeling
• Process dynamics and time constants
• Types / modes of operation: process control systems
• Closed loop controller and process gain calculations
• Proportional, integral and derivative (PID) control modes
• Cascade control--Introduction

2. Process measurement and transducers

• Goals
• transducers and sensors--definition
• Some common measured variables
• Transducers--common characteristics
• Sensor dynamics
• Selecting sensing devices
• Temperature sensors
• Pressure transmitters
• Flow meters
• Level transmitters
• Measuring transducers: User models
• Instrumentation and transducer considerations
• Selection criteria and considerations
• Introduction to smart transmitters

3. Control valves and actuators: Basic principles

• Goals
• Overview: basic control valves
• Control valve gain, characteristics, distortion, rangeability
• Control valve actuators
• Control valve positioners
• Valve sizing

4. Control systems: Fundamentals

• Goals
• On–off control
• Modulating control
• Open loop control
• Closed loop control
• Deadtime processes
• Process responses
• Dead zone

5. Closed loops--Stability and control modes

• Goals
• The industrial process in practice
• Feed heater--Dynamic behavior
• Feed heater--Major disturbances
• Stability
• Proportional control
• Integral control
• Derivative control
• Proportional, integral and derivative modes
• ISA vs Allen Bradley
• PID relationships and related interactions
• Process control modes: Applications
• PID controller outputs: Examples

6. Principles of Digital Process Control

7. Real and ideal PID controllers

8. How to tune PID controllers: open- / closed-loop control systems

• Goals
• Goals 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 possible in open loop tuning
• Continuous cycling method (Ziegler–Nichols)
• Damped cycling tuning method
• Tuning --no overshoot on start-up (Pessen)
• Tuning --overshoot on start-up (Pessen)
• Summary--important closed loop tuning algorithms
• PID equations: dependent and independent gains

9. Controller output modes, operating equations, cascade control

• Goals
• Controller output
• Multiple controller outputs
• Saturation and non-saturation of output limits
• Cascade control
• Cascade system--Initialization
• Controller configurations--Equations
• Using equation types and application
• Cascade control loop--Tuning
• Cascade control with multiple secondaries

10. Feedforward control--concepts + applications

• Goals
• Application + definition of feedforward control
• Manual feedforward control
• Automatic feedforward control
• Feedforward controllers--Examples
• Time matching as feedforward control

11. Combined feedback + feedforward control

• Goals
• The feedforward concept
• The feedback concept
• Combining feedback and feedforward control
• Feedback–feedforward summer
• Combined feedback and feedforward control system--Initialization
• Tuning aspects

12. Long process deadtime in closed-loop control--Smith Predictor

• Goals
• Process deadtime
• Process deadtime--example
• The Smith Predictor model
• The Smith Predictor--theoretical use
• The Smith Predictor--reality
• Deadtime compensation

13. Fuzzy logic, neural networks--Basic principles

• Goals
• Introduction to fuzzy logic
• What is fuzzy logic?
• What's fuzzy logic for?
• The rules of fuzzy logic
• Fuzzy logic example using 5 rules and patches
• The Achilles heel of fuzzy logic
• Neural networks
• Neural back propagation networking
• Training a neuron network
• Conclusions + next steps

14. Self-tuning intelligent control + statistical process control

• Goals
• Self-tuning controllers
• Gain scheduling controller
• Implementation requirements for self-tuning controllers
• Statistical process control (SPC)
• Two ways to improve production process
• Getting SPC information
• Calculating control limits
• Control charts and logic

Experience shows that most graduate engineers have a decent knowledge of the mathematical aspects of process control. But, when it comes to the practical understanding of industrial process control, there's often a problem in converting theoretical knowledge into practical understanding of control concepts and problems. This Guide is intended to fill this gap.

It’s not intended to add another guide to the vast number of existing resources covering process-control theory. Instead, this guide provides a practical understanding of control concepts as well as enabling the reader to gain a correct understanding of control theory. The principles of industrial process control concepts and the associated pitfalls are explained in an easy to understand manner. Although the mathematical side is kept to a minimum, a basic grasp of engineering concepts and a general knowledge of algebra and calculus is required in order to fully understand this Guide.

There's a degree of emphasis on the internal calculation of control algorithms in digital computers.

The purpose of this is to provide a wider view of the use and modification of computer-calculated algorithms (incremental algorithms). The first automatic control system known was the Fly-ball governor installed on Watt's steam engine in 1775 to regulate the steam rate. It was nearly a century later that the first mathematical model of the Fly-ball governor was prepared by James Clerk Maxwell. This illustrates a common practice in the development of process control, using a system before fully understanding exactly why and how it does the job. The spreading use of steam boilers resulted in the introduction of other automatic control systems, such as steam pressure regulators and the first multiple element boiler feedwater systems. Again, the applications came before the theory. The first general theory of automatic control, written by Nyquist, only appeared in 1932.

Today, automatic process control is an increasingly important part of the capital outlay in industry. The primary difficulty encountered with process control is in applying well-defined mathematical theories to day-to-day industrial applications, and translating ideal models to the frequently far-from ideal real world scenario. Process control has a number of significant advantages. As always, the primary factor in any operation is cost. The use of process control in a system enables the maximum profitability to be derived. Other advantages are that automatic control results in increased plant flexibility, reduced maintenance, and in stable and safe operation of the plant. It also allows operators to more closely approach optimum operation of the process. As the degree of automatic control is increased, so do the related advantages which too become more significant.

Further improvements in process control are attained by model-based control and ultimately by optimization.

Optimization applications can be installed when the plant is stable, operated safely and has tight quality control. The benefits of optimization are improvement in product yield and quality, reduction in energy consumption, and a move to optimum operation of the process. It’s possible to track optimum operation to maintain the maximum profitability of the process.

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