(EBOOK PDF)Applied Process Control Essential Methods 1st Edition by Michael Mulholland 9783527801671 3527801677 full chapters

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• ISBN 10:3527801677 

• ISBN 13:9783527801671

• Author:Michael Mulholland 

  Applied Process Control: Essential Methods
Focusing on the practical implementation of the methods of process modelling and control, this book provides readers with rapid access to the methods described, 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, this book 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, over 200 exercises and solutions can be found in the accompanying “Applied Process Control: Efficient Problem Solving”, to develop the problem-solving abilities of process engineers.                                                                                                                                               

Applied Process Control Essential Methods 1st Table of contents:

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

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