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From Plant Data to Process Control: Ideas for Process Identification and PID Design [Kõva köide]

  • Formaat: Hardback, 240 pages, kõrgus x laius: 246x174 mm, kaal: 600 g
  • Ilmumisaeg: 31-Aug-2000
  • Kirjastus: Taylor & Francis Ltd
  • ISBN-10: 0748407014
  • ISBN-13: 9780748407019
Teised raamatud teemal:
From Plant Data to Process Control: Ideas for Process Identification and PID DesignSuurem pilt
  • Formaat: Hardback, 240 pages, kõrgus x laius: 246x174 mm, kaal: 600 g
  • Ilmumisaeg: 31-Aug-2000
  • Kirjastus: Taylor & Francis Ltd
  • ISBN-10: 0748407014
  • ISBN-13: 9780748407019
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780748407019.html
Märksõnad:
Process engineering spans industrial applications in the manufacturing sector from petrochemical to polymer to mineral production. From Plant Data to Process Control covers the most up-to-date techniques and algorithms in the area of process identification (PID) and process control, two key components of process engineering, essential for optimizing production systems. It examines both the theoretical advances in process design and control theory, and a wide variety of implementations. A wide variety of approaches are presented for building models of dynamical systems based on observed data (process identification) and for making the output of a system behave in a desired fashion by properly selecting the process input (process control).

Arvustused

"The book provides a useful reference for engineers and researchers working in the fields of system identification and control." The International Journal of Advanced Manufacturing Technology

Series Introduction xi
Acknowledgements xiii
Introduction
1(8)
The Laguerre Model: Process Identification from Step Response Data
1(2)
Use of Press for Model Structure Selection in Process Identification
3(1)
Frequency Sampling Filters: An Improved Model Structure for Process Identification
3(2)
Pid Controller Design: A New Frequency Domain Approach
5(2)
Relay Feedback Experiments for Process Identification
7(2)
Modelling from Noisy Step Response Data Using Laguerre Functions
9(50)
Introduction
9(1)
Process Representation Using Laguerre Models
10(7)
Approximation of the process impulse response
10(2)
Approximation of the process transfer function
12(2)
Laguerre model in state space form
14(1)
Generating the Laguerre functions
15(2)
Choice of the Time Scaling Factor
17(11)
Modelling errors with respect to choice of p
18(1)
Optimal choice of p
19(5)
Optimal time scaling factor for first order plus delay systems
24(4)
Estimation of Laguerre Coefficients from Step Response Data
28(7)
Statistical Properties of the Estimated Coefficients
35(7)
Bias and variance analysis
35(2)
Some special cases of disturbances
37(5)
A Strategy for Improving the Laguerre Model
42(8)
Modelling of a Polymer Reactor
50(5)
Appendix
55(4)
Least Squares and the PRESS Statistic using Orthogonal Decomposition
59(16)
Introduction
59(1)
Least Squares and Orthogonal Decomposition
60(3)
Least squares for dynamic models
60(1)
Orthogonal decomposition algorithm
61(2)
The Press Statistic
63(1)
Computation of the Press Statistic
64(2)
Use of Press for Process Model Selection
66(3)
Use of Press for Disturbance Model Selection
69(6)
Frequency Sampling Filters in Process Identification
75(24)
Introduction
75(1)
The Frequency Sampling Filter Model
76(3)
Properties of the FSF Model with Fast Sampling
79(4)
Reduced Order FSF Model
83(4)
Parameter Estimation for the FSF Model
87(2)
Nature of the Correlation Matrix
89(10)
From FSF Models to Step Response Models
99(32)
Introduction
99(1)
Obtaining a Step Response Model from the FSF Model
100(6)
Smoothing the Step Response Using the FSF Model
106(5)
Error Analysis
111(4)
Confidence Bounds for Frequency Response and Step Response Estimates
115(4)
Generalized Least Squares Algorithm
119(1)
Industrial Application: Identification of a Refinery Distillation Train
120(11)
Process description
120(3)
Dynamic response testing
123(2)
Results
125(1)
Use of PRESS for model structure selection
126(1)
Use of noise models to remove feedback effects
127(1)
Use of confidence bounds for judging model quality
128(3)
New Frequency Domain PID Controller Design Method
131(40)
Introduction
131(1)
Control Signal Specification
132(10)
Specification for stable processes
134(4)
Specification for integrating processes
138(4)
PID Parameters: Least Squares Approach
142(6)
Illustrative example
144(4)
PID Parameters: Use of only Two Frequencies
148(4)
Choice of Frequency Points
152(3)
Ensuring a Positive Integral Time Constant
155(2)
Simulation Studies
157(14)
Tuning Rules for PID Controllers
171(30)
Introduction
171(1)
First Order Plus Delay Case
171(10)
Evaluation of the New Tuning Rules: Simulation Results
181(6)
Experiments with a Stirred Tank Heater
187(5)
Integrating Plus Delay Case
192(9)
Recursive Estimation from Relay Feedback Experiments
201(16)
Introduction
201(1)
Recursive Frequency Response Estimation
201(6)
Recursive Step Response Estimation
207(10)
Simulation case study
209(6)
Automated design of an identification experiment
215(2)
Bibliography 217(6)
Index 223


Wang, Liuping