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E-grāmata: Process Dynamics and Control

(University of Texas, Austin), (University of California, Santa Barbara), (University of California, Santa Barbara), (University of California, Santa Barbara)
  • Formāts: EPUB+DRM
  • Izdošanas datums: 20-Sep-2016
  • Izdevniecība: John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781119285953
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Process Dynamics and ControlPalielināt
  • Formāts: EPUB+DRM
  • Izdošanas datums: 20-Sep-2016
  • Izdevniecība: John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781119285953
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The new 4th edition of Seborg’s Process Dynamics Control provides full topical coverage for process control courses in the chemical engineering curriculum, emphasizing how process control and its related fields of process modeling and optimization are essential to the development of high-value products. A principal objective of this new edition is to describe modern techniques for control processes, with an emphasis on complex systems necessary to the development, design, and operation of modern processing plants. Control process instructors can cover the basic material while also having the flexibility to include advanced topics.
PART ONE INTRODUCTION TO PROCESS CONTROL
1 Introduction to Process Control
1(13)
1.1 Representative Process Control Problems
2(2)
1.2 Illustrative Example --- A Blending Process
4(1)
1.3 Classification of Process Control Strategies
5(2)
1.4 A More Complicated Example --- A Distillation Column
7(1)
1.5 The Hierarchy of Process Control Activities
8(2)
1.6 An Overview of Control System Design
10(4)
2 Theoretical Models of Chemical Processes
14(24)
2.1 The Rationale for Dynamic Process Models
14(2)
2.2 General Modeling Principles
16(3)
2.3 Degrees of Freedom Analysis
19(2)
2.4 Dynamic Models of Representative Processes
21(9)
2.5 Process Dynamics and Mathematical Models
30(8)
PART TWO DYNAMIC BEHAVIOR OF PROCESSES
3 Laplace Transforms
38(16)
3.1 Laplace Transforms of Representative Functions
39(3)
3.2 Solution of Differential Equations by Laplace Transform Techniques
42(1)
3.3 Partial Fraction Expansion
43(2)
3.4 Other Laplace Transform Properties
45(2)
3.5 A Transient Response Example
47(2)
3.6 Software for Solving Symbolic Mathematical Problems
49(5)
4 Transfer Function Models
54(14)
4.1 Introduction to Transfer Function Models
54(3)
4.2 Properties of Transfer Functions
57(4)
4.3 Linearization of Nonlinear Models
61(7)
5 Dynamic Behavior of First-Order and Second-Order Processes
68(18)
5.1 Standard Process Inputs
69(1)
5.2 Response of First-Order Processes
70(3)
5.3 Response of Integrating Processes
73(2)
5.4 Response of Second-Order Processes
75(11)
6 Dynamic Response Characteristics of More Complicated Processes
86(19)
6.1 Poles and Zeros and Their Effect on Process Response
86(3)
6.2 Processes with Time Delays
89(3)
6.3 Approximation of Higher-Order Transfer Functions
92(2)
6.4 Interacting and Noninteracting Processes
94(1)
6.5 State-Space and Transfer Function Matrix Models
95(3)
6.6 Multiple-Input, Multiple-Output (MIMO) Processes
98(7)
7 Development of Empirical Models from Process Data
105(18)
7.1 Model Development Using Linear or Nonlinear Regression
106(3)
7.2 Fitting First- and Second-Order Models Using Step Tests
109(4)
7.3 Neural Network Models
113(2)
7.4 Development of Discrete-Time Dynamic Models
115(1)
7.5 Identifying Discrete-Time Models from Experimental Data
116(7)
PART THREE FEEDBACK AND FEEDFORWARD CONTROL
8 Feedback Controllers
123(17)
8.1 Introduction
123(2)
8.2 Basic Control Modes
125(5)
8.3 Features of PID Controllers
130(3)
8.4 Digital Versions of PID Controllers
133(2)
8.5 Typical Responses of Feedback Control Systems
135(1)
8.6 On-Off Controllers
136(4)
9 Control System Instrumentation
140(20)
9.1 Sensors, Transmitters, and Transducers
141(7)
9.2 Final Control Elements
148(6)
9.3 Accuracy in Instrumentation
154(6)
10 Process Safety and Process Control
160(15)
10.1 Layers of Protection
161(4)
10.2 Alarm Management
165(4)
10.3 Abnormal Event Detection
169(1)
10.4 Risk Assessment
170(5)
11 Dynamic Behavior and Stability of Closed-Loop Control Systems
175(24)
11.1 Block Diagram Representation
176(2)
11.2 Closed-Loop Transfer Functions
178(3)
11.3 Closed-Loop Responses of Simple Control Systems
181(5)
11.4 Stability of Closed-Loop Control Systems
186(5)
11.5 Root Locus Diagrams
191(8)
12 PID Controller Design, Tuning, and Troubleshooting
199(30)
12.1 Performance Criteria for Closed-Loop Systems
200(1)
12.2 Model-Based Design Methods
201(5)
12.3 Controller Tuning Relations
206(7)
12.4 Controllers with Two Degrees of Freedom
213(1)
12.5 On-Line Controller Tuning
214(6)
12.6 Guidelines for Common Control Loops
220(2)
12.7 Troubleshooting Control Loops
222(7)
13 Control Strategies at the Process Unit Level
229(15)
13.1 Degrees of Freedom Analysis for Process Control
230(2)
13.2 Selection of Controlled, Manipulated, and Measured Variables
232(3)
13.3 Applications
235(9)
14 Frequency Response Analysis and Control System Design
244(18)
14.1 Sinusoidal Forcing of a First-Order Process
244(2)
14.2 Sinusoidal Forcing of an nth-Order Process
246(1)
14.3 Bode Diagrams
247(4)
14.4 Frequency Response Characteristics of Feedback Controllers
251(1)
14.5 Nyquist Diagrams
252(1)
14.6 Bode Stability Criterion
252(4)
14.7 Gain and Phase Margins
256(6)
15 Feedforward and Ratio Control
262(17)
15.1 Introduction to Feedforward Control
263(1)
15.2 Ratio Control
264(2)
15.3 Feedforward Controller Design Based on Steady-State Models
266(2)
15.4 Feedforward Controller Design Based on Dynamic Models
268(4)
15.5 The Relationship Between the Steady-State and Dynamic Design Methods
272(1)
15.6 Configurations for Feedforward-Feedback Control
272(1)
15.7 Tuning Feedforward Controllers
273(6)
PART FOUR ADVANCED PROCESS CONTROL
16 Enhanced Single-Loop Control Strategies
279(21)
16.1 Cascade Control
279(5)
16.2 Time-Delay Compensation
284(2)
16.3 Inferential Control
286(1)
16.4 Selective Control/Override Systems
287(2)
16.5 Nonlinear Control Systems
289(3)
16.6 Adaptive Control Systems
292(8)
17 Digital Sampling, Filtering, and Control
300(26)
17.1 Sampling and Signal Reconstruction
300(3)
17.2 Signal Processing and Data Filtering
303(4)
17.3 z-Transform Analysis for Digital Control
307(6)
17.4 Tuning of Digital PID Controllers
313(2)
17.5 Direct Synthesis for Design of Digital Controllers
315(4)
17.6 Minimum Variance Control
319(7)
18 Multiloop and Multivariable Control
326(24)
18.1 Process Interactions and Control Loop Interactions
327(4)
18.2 Pairing of Controlled and Manipulated Variables
331(7)
18.3 Singular Value Analysis
338(3)
18.4 Tuning of Multiloop PID Control Systems
341(1)
18.5 Decoupling and Multivariable Control Strategies
342(1)
18.6 Strategies for Reducing Control Loop Interactions
343(7)
19 Real-Time Optimization
350(18)
19.1 Basic Requirements in Real-Time Optimization
352(2)
19.2 The Formulation and Solution of RTO Problems
354(2)
19.3 Unconstrained and Constrained Optimization
356(3)
19.4 Linear Programming
359(3)
19.5 Quadratic and Nonlinear Programming
362(6)
20 Model Predictive Control
368(27)
20.1 Overview of Model Predictive Control
369(1)
20.2 Predictions for SISO Models
370(7)
20.3 Predictions for MIMO Models
377(2)
20.4 Model Predictive Control Calculations
379(3)
20.5 Set-Point Calculations
382(2)
20.6 Selection of Design and Tuning Parameters
384(5)
20.7 Implementation of MPC
389(6)
21 Process Monitoring
395(18)
21.1 Traditional Monitoring Techniques
397(1)
21.2 Quality Control Charts
398(6)
21.3 Extensions of Statistical Process Control
404(2)
21.4 Multivariate Statistical Techniques
406(2)
21.5 Control Performance Monitoring
408(5)
22 Batch Process Control
413(22)
22.1 Batch Control Systems
415(1)
22.2 Sequential and Logic Control
416(5)
22.3 Control During the Batch
421(5)
22.4 Run-to-Run Control
426(1)
22.5 Batch Production Management
427(8)
PART FIVE APPLICATIONS TO BIOLOGICAL SYSTEMS
23 Biosystems Control Design
435(16)
23.1 Process Modeling and Control in Pharmaceutical Operations
435(7)
23.2 Process Modeling and Control for Drug Delivery
442(9)
24 Dynamics and Control of Biological Systems
451(13)
24.1 Systems Biology
451(2)
24.2 Gene Regulatory Control
453(4)
24.3 Signal Transduction Networks
457(7)
Appendix A Digital Process Control Systems: Hardware and Software
464(14)
A.1 Distributed Digital Control Systems
465(1)
A.2 Analog and Digital Signals and Data Transfer
466(1)
A.3 Microprocessors and Digital Hardware in Process Control
467(3)
A.4 Software Organization
470(8)
Appendix B Review of Thermodynamic Concepts for Conservation Equations
478(2)
B.1 Single-Component Systems
478(1)
B.2 Multicomponent Systems
479(1)
Appendix C Control Simulation Software
480(7)
C.1 MATLAB Operations and Equation Solving
480(2)
C.2 Computer Simulation with Simulink
482(3)
C.3 Computer Simulation with Lab VIEW
485(2)
Appendix D Instrumentation Symbols
487(2)
Appendix E Process Control Modules
489(2)
E.1 Introduction
489(1)
E.2 Module Organization
489(1)
E.3 Hardware and Software Requirements
490(1)
E.4 Installation
490(1)
E.5 Running the Software
490(1)
Appendix F Review of Basic Concepts From Probability and Statistics
491(4)
F.1 Probability Concepts
491(1)
F.2 Means and Variances
492(1)
F.3 Standard Normal Distribution
493(1)
F.4 Error Analysis
493(2)
Appendix G Introduction to Plant wide Control (Available online at: www.wiley.com/college/seborg)
Appendix H Plantwide Control System Design (Available online at: www.wiley.com/college/seborg)
Appendix I Dynamic Models and Parameters Used for Plant wide Control
Chapters (Available online at: www.wiley.com/college/seborg)
Appendix J Additional Closed-Loop Frequency Response Material (Available online at: www.wiley.com/college/seborg)
Appendix K Contour Mapping and the Principle of the Argument (Available online at: www.wiley.com/college/seborg)
Appendix L Partial Fraction Expansions for Repeated and Complex Factors (Available online at: www.wiley.com/college/seborg)
Index 495
Dale E. Seborg is a Professor and Vice Chair of the Department of Chemical Engineering at the University of California, Santa Barbara. He received his B.S. degree from the University of Wisconsin and his Ph.D. degree from Princeton University. Dr. Seborg has published over 200 articles and co-edited three books on process control and related topics. Dr. Seborg has served on the Editorial Advisor Boards for control engineering journals and book series, and has been a co-organizer of several major conferences. He is an active industrial consultant who serves as an expert witness in legal proceedings.





Thomas F. Edgar holds the Abell Chair in chemical engineering at the University of Texas at Austin. He earned a B.S. degree in chemical engineering from the University of Kansas and a Ph.D. from Princeton University. He has published over 300 papers in the field of process control, optimization, and mathematical modeling of processes such as separations, combustion, and microelectronics processing. Dr. Edgar was president of AIChE in 1997 and President of the American Automatic Control Council in 198991.





Duncan A. Mellichamp is professor Emeritus and founding member of the faculty of the chemical engineering department at the University of California, Santa Barbara. He is editor of an early book on data acquisition and control computing and has published more than one hundred papers on process modeling, large scale/plantwide systems analysis, and computer control. He earned a B.S. degree from Georgia Tech and a Ph.D. from Purdue University with intermediate studies at the Technische Universität Stuttgart (Germany). He presently serves on the governing boards of several nonprofit organizations.





Francis J. Doyle III is the Associate Dean for Research in the College of Engineering at the University of California, Santa Barbara. He holds the Duncan and Suzanne Mellichamp Chair in Process Control in the Department of Chemical Engineering, as well as appointments in the Electrical Engineering Department, and the Biomolecular Science and Engineering Program. He received his B.S.E. from Princeton, C.P.G.S. from Cambridge, and Ph.D. from Caltech, all in Chemical Engineering. He is a Fellow of IEEE, IFAC, and AIMBE; he is also the recipient of multiple research awards (including the AIChE Computing in Chemical Engineering Award) as well as teaching awards (including the ASEE Ray Fahien Award).
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