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Applied Process Control

Book Description

The basic working knowledge for the practicing control engineer in industry, offered here as a handy deluxe edition comprising two volumes each devoted to methods and practical problems.
Focusing on the practical implementation, the methods volume provides readers with rapid access to process modelling and control, while including the theoretical background necessary. Throughout, the essential knowledge is built up from chapter to chapter, starting with laying the foundations in plant instrumentation and control. Modelling abilities are then developed by starting from simple time-loop algorithms and passing on to discrete methods, Laplace transforms, automata and fuzzy logic. In the end, readers have the means to design simple controllers on the basis of their own models, and to use more detailed models to test them. With its clarity and simplicity of presentation, and illustrated by more than 200 diagrams, the volume supports self-study and teaches readers how to apply the appropriate method for the application required, and how to handle problems in process control.
Bridging theory and practice, the second volume contains over 200 practical exercises and their solutions to develop the problem-solving abilities of process engineers. The problems were developed by the author during his many years of teaching at university and are kept brief, taken from the fields of instrumentation, modeling, plant control, control strategy design and stability of control. The algorithm flows and codes, which are mostly based on MATLAB?, are given in many cases and allow for easy translation into applications.
With a clarity and simplicity of presentation, the two volumes are similarly structured for easy orientation.

Table of Contents

  1. Cover
  2. Related Titles
  3. Title Page
  4. Copyright
  5. Preface
  6. Acknowledgements
  7. Abbreviations
  8. Frontispiece
  9. Chapter 1: Introduction
    1. 1.1 The Idea of Control
    2. 1.2 Importance of Control in Chemical Processing
    3. 1.3 Organisation of This Book
    4. 1.4 Semantics
    5. References
  10. Chapter 2: Instrumentation
    1. 2.1 Piping and Instrumentation Diagram Notation
    2. 2.2 Plant Signal Ranges and Conversions
    3. 2.3 A Special Note on Differential Pressure Cells
    4. 2.4 Measurement Instrumentation
    5. 2.5 Current-to-Pneumatic Transducer
    6. 2.6 Final Control Elements (Actuators)
    7. 2.7 Controllers
    8. 2.8 Relays, Trips and Interlocks
    9. 2.9 Instrument Reliability
    10. References
  11. Chapter 3: Modelling
    1. 3.1 General Modelling Strategy
    2. 3.2 Modelling of Distributed Systems
    3. 3.3 Modelling Example for a Lumped System: Chlorination Reservoirs
    4. 3.4 Modelling Example for a Distributed System: Reactor Cooler
    5. 3.5 Ordinary Differential Equations and System Order
    6. 3.6 Linearity
    7. 3.7 Linearisation of the Equations Describing a System
    8. 3.8 Simple Linearisation ‘Δ’ Concept
    9. 3.9 Solutions for a System Response Using Simpler Equations
    10. 3.10 Use of Random Variables in Modelling
    11. 3.11 Modelling of Closed Loops
    12. References
  12. Chapter 4: Basic Elements Used in Plant Control Schemes
    1. 4.1 Signal Filtering/Conditioning
    2. 4.2 Basic SISO Controllers
    3. 4.3 Cascade Arrangement of Controllers
    4. 4.4 Ratio Control
    5. 4.5 Split Range Control
    6. 4.6 Control of a Calculated Variable
    7. 4.7 Use of High Selector or Low Selector on Measurement Signals
    8. 4.8 Overrides: Use of High Selector or Low Selector on Control Action Signals
    9. 4.9 Clipping, Interlocks, Trips and Latching
    10. 4.10 Valve Position Control
    11. 4.11 Advanced Level Control
    12. 4.12 Calculation of Closed-Loop Responses: Process Model with Control Element
    13. References
  13. Chapter 5: Control Strategy Design for Processing Plants
    1. 5.1 General Guidelines to the Specification of an Overall Plant Control Scheme
    2. 5.2 Systematic Approaches to the Specification of an Overall Plant Control Scheme
    3. 5.3 Control Schemes Involving More Complex Interconnections of Basic Elements
    4. References
  14. Chapter 6: Estimation of Variables and Model Parameters from Plant Data
    1. 6.1 Estimation of Signal Properties
    2. 6.2 Real-Time Estimation of Variables for Which a Delayed Measurement Is Available for Correction
    3. 6.3 Plant Data Reconciliation
    4. 6.4 Recursive State Estimation
    5. 6.5 Identification of the Parameters of a Process Model
    6. 6.6 Combined State and Parameter Observation Based on a System of Differential and Algebraic Equations
    7. 6.7 Nonparametric Identification
    8. References
  15. Chapter 7: Advanced Control Algorithms
    1. 7.1 Discrete z-Domain Minimal Prototype Controllers
    2. 7.2 Continuous s-Domain MIMO Controller Decoupling Design by Inverse Nyquist Array
    3. 7.3 Continuous s-Domain MIMO Controller Design Based on Characteristic Loci
    4. 7.4 Continuous s-Domain MIMO Controller Design Based on Largest Modulus
    5. 7.5 MIMO Controller Design Based on Pole Placement
    6. 7.6 State-Space MIMO Controller Design
    7. 7.7 Concept of Internal Model Control
    8. 7.8 Predictive Control
    9. 7.9 Control of Time-Delay Systems
    10. 7.10 A Note on Adaptive Control and Gain Scheduling
    11. 7.11 Control Using Artificial Neural Networks
    12. 7.12 Control Based on Fuzzy Logic
    13. 7.13 Predictive Control Using Evolutionary Strategies
    14. 7.14 Control of Hybrid Systems
    15. 7.15 Decentralised Control
    16. References
  16. Chapter 8: Stability and Quality of Control
    1. 8.1 Introduction
    2. 8.2 View of a Continuous SISO System in the s-Domain
    3. 8.3 View of a Continuous MIMO System in the s-Domain
    4. 8.4 View of Continuous SISO and MIMO Systems in Linear State Space
    5. 8.5 View of Discrete Linear SISO and MIMO Systems
    6. 8.6 Frequency Response
    7. 8.7 Control Quality Criteria
    8. 8.8 Robust Control
    9. References
  17. Chapter 9: Optimisation
    1. 9.1 Introduction
    2. 9.2 Aspects of Optimisation Problems
    3. 9.3 Linear Programming
    4. 9.4 Integer Programming and Mixed Integer Programming (MIP)
    5. 9.5 Gradient Searches
    6. 9.6 Nonlinear Programming and Global Optimisation
    7. 9.7 Combinatorial Optimisation by Simulated Annealing
    8. 9.8 Optimisation by Evolutionary Strategies
    9. 9.9 Mixed Integer Nonlinear Programming
    10. 9.10 The GAMS® Modelling Environment
    11. 9.11 Real-Time Optimisation of Whole Plants
    12. References
  18. Index
  19. End User License Agreement