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Modeling, Control, Simulation and Diagnosis of Complex Industrial and Energy Systems [Pehme köide]

  • Formaat: Paperback / softback, kaal: 570 g, illustrations
  • Ilmumisaeg: 28-Feb-2009
  • Kirjastus: ISA
  • ISBN-10: 1934394904
  • ISBN-13: 9781934394908
Teised raamatud teemal:
Modeling, Control, Simulation and Diagnosis of Complex Industrial and Energy SystemsSuurem pilt
  • Formaat: Paperback / softback, kaal: 570 g, illustrations
  • Ilmumisaeg: 28-Feb-2009
  • Kirjastus: ISA
  • ISBN-10: 1934394904
  • ISBN-13: 9781934394908
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781934394908.html
Märksõnad:
Focusing on the modeling, control, simulation, and diagnosis of complex industrial systems, this book contains a collection of papers that have been developed under the aegis of ANIPLA (the national automation association of Italy), which celebrated its 50th anniversary in 2006. Emphasis is placed on the real-time monitoring and control of process plants and energy systems and on the application of innovative approaches ranging from the predictive control of a gasoline engine, through fuzzy inference applied to quality control in the paper industry and up to innovative load shedding and demand management in national electrical grids. Part of the ISA/O3neida series, this book will be of interest to practitioners within the automation field, particularly those focused on process control and energy systems. It will also be of interest to academics and students seeking an overview of current approaches in this field or looking for detailed treatment of any of the issues covered by the individual chapters. More than forty authors from countries around the world have contributed to the production of this unique book and O3neida thanks them, one and all, for their strong collaboration in producing this excellent compendium and for their continuing contribution to the advancement of process control and optimization.
List of Figures
xvii
List of Tables
xxv
Remote Supervision Center for Enel Combined Cycle palnts
1(34)
Introduction
1(1)
Location of the center
2(1)
Architecture
2(1)
Functions
2(4)
Performance Control
6(1)
Heat rate evaluation
6(1)
Maximum power forecast
7(3)
Plant status and status monitor
10(1)
Plant start-up: technical and economical evaluation
11(3)
Power unbalance calculation
14(1)
Diagnostics
15(5)
Automatic reporting
20(1)
Heat rate losses
20(2)
Start-up evaluation
22(3)
Energy unbalance
25(2)
Gas turbine output temperatures and humming and acceleration phenomena
27(1)
Gas turbine compressor filters status
28(3)
Computerized events register
31(1)
Acknowledgmens
32(1)
References
32(3)
Pickling Line Modeling for Advanced Process Monitoring and Automation
35(18)
Introduction
35(1)
Pickling of carbon steel
35(1)
Pickling of stainless steel
36(2)
Management and control of pickling processes
38(1)
Advances in pickling line automation
39(1)
Architecture of control software
39(2)
Pickiling lines components and configuration
41(1)
Main components of pickling lines
42(1)
Pickling lines configuration
43(1)
Electrolytic pickling lines
43(1)
Pickling line model
44(1)
Equations describing the recirculation tank
44(2)
Equations describing the working tank
46(2)
The pickling model
48(1)
Electrolytic pickling model
48(1)
Additional notes on the pickling line model
49(1)
Model implementation
49(1)
Conclusion
49(2)
Acknoledgments
51(1)
References
52(1)
Modeling, Simulation and Predictive Control of a Gasoline Engine
53(22)
Introduction
53(2)
Mean value engine model
55(1)
Air supply system
55(3)
Engine
58(2)
Vehicle model
60(1)
Validation
61(1)
Control design
61(3)
Design of a static regulator
64(1)
Model of the driver
65(1)
Design of a dynamic controller with MPC
65(2)
Simulation results
67(1)
Conclusion
68(4)
Acknowledgments
72(1)
References
72(3)
Dynamic Principal Component Analysis Applied to the Monitoring of a diesel Hydrotreating Unit
75(22)
Introduction
75(1)
Hydrotreating Unit Model
76(1)
Hydrotreating (HDT) unit
76(1)
HDT unit modeling
77(5)
Principal Components Analysis (PCA)
82(2)
Monitoring system: development and results
84(1)
Operational conditions
84(3)
DPCA: definition of the number of delays
87(4)
DPCA: validation and test
91(2)
Conclusion
93(1)
Acknowledgments
94(1)
References
94(3)
A Simulation Study of the Flue Gas Path Control System in a Coal-Fired Power Plant
97(18)
Introduction
97(1)
The plant model
98(1)
Structure of the Plant
98(1)
Unit modeling
99(5)
The control system model
104(1)
General remarks
104(1)
Control system architecture
105(1)
Continuous-time controllers
106(1)
Logic controllers
106(1)
Improvement of the control strategy
106(3)
Improvement of the critical logic control behavior
109(1)
Selected simulation results
110(1)
Load dispatching
110(1)
Transition from FGD inserted to FGd bypassed
111(1)
Conclusion
112(2)
References
114(1)
Automatic Diagnosis of Valve Stiction by Means of a Qualitative Shape Analysis Technique
115(24)
Introduction
115(1)
Valve stiction
116(2)
Automatic detection of stiction
118(1)
Techniques based on PV-OP---brief review
118(1)
Techniques based on qualitative description formalism
119(2)
The Yamashita stiction detection technique
121(2)
Application on simulated data
123(2)
Noise-free data
125(1)
Adding noise
125(1)
Varying setpoints
126(3)
First conclusions about the technique
129(1)
Application to plant data
129(1)
Results
130(4)
Sampling time
134(1)
Observation window
134(1)
Noise level
135(1)
Other phenomena observed in the plant data
135(1)
Conclusion
135(1)
REferences
136(3)
Monitoring and Controlling Processes with Complex Dynamics Using Soft Sensors
139(24)
Introduction
139(1)
Freeze-drying of pharmaceuticals
140(2)
Detailed and simplified models
142(3)
Observers design
145(4)
Feedback temperature control
149(1)
Catalytic combustion of lean mixtures
150(4)
SCR unit for NOx
154(4)
Conclusions
158(1)
Acknowledgments
158(2)
Nomenclature
160(1)
References
160(3)
Estimation of a ternary Distillation Column via a Tailored Data Assimilation Mechanism
163(20)
Introduction
163(1)
Estimation problem
164(4)
Data assimilation mechanism
168(7)
Estimation design
175(1)
The Non-linear Geometric Estimator (ENE)
176(1)
The Extended Kalman Filter (EKF) with reduced data injuection
177(2)
Conclusion
179(1)
References
180(3)
A Prediction Error-Based Method for the Performance Monitoring of Model Predictive Controllers
183(22)
Introduction
183(2)
Problem Statement
185(1)
Process, model and state estimator
185(2)
Steady-state target calculation
187(2)
Dynamic optimization
189(1)
Method
190(1)
Prliminary definitions of predictin error
190(1)
Motivation example
190(1)
Prediction error-based diagnosis
191(5)
Case studies
196(1)
Extensive simulations
196(1)
Extensive simulations
196(2)
An industrial example
198(1)
Conclusion
198(3)
Acknowledgments
201(1)
References
201(4)
An Intelligent/ Smart Framework for Real-Time ProcessMonitoring and Supervision
205(20)
Introduction
205(2)
Integrated framework
207(1)
Trend aalysis and preprocessing
207(1)
Outlier detection
207(2)
Noise reduction
209(1)
Fault detection and identification
209(6)
Self-Organizing, Self-Clustering Network (SOSCN)
215(1)
Case study
216(5)
Conclusion
221(4)
Quality Monitoring Through a Dynamic Neural Software Sensor
225(14)
Introduction
225(1)
Background
226(1)
Problem statement
227(1)
The process
227(1)
Software sensor
228(1)
Software sensor design
228(1)
Software sensor design
228(1)
Basic structure
228(2)
Neural Software sensor formulation
230(1)
Industrial application
231(1)
Data acquisition
231(1)
Input selection
232(1)
Results ad discussion
233(2)
Conclusion
235(1)
References
236(3)
Wind Generation and Flexible Electric Load Management Issues for System Operation in Crete
239(14)
Introduction
239(2)
Green Electricity Availability Barometer Service (GEA BASE)
241(2)
A Control Center tool
243(1)
Generation nodes model
244(1)
Implementation
244(1)
Formulation of the knowledge base
245(1)
Formulation of the knowledge base
245(2)
Inference derivatin process
247(1)
Architecture of an expert system
248(2)
Incorporation of the GEA BASE tool into GIS and digital database for the Crete Power System
250(1)
Conclusion
251(1)
References
251(2)
A Fuzzy Inference System Applied to Quality Control in the Paper Industry
253(20)
Introduction
253(2)
Problem description
255(1)
Experimental setup
255(2)
The quality control system
257(1)
The image processing phase
258(4)
Defect detection through a clustering algorithm
262(2)
Defect evaluation through a fuzzy inferences system
264(4)
Numerical results
268(2)
Conclusin and future work
270(2)
References
272(1)
Innovative Load Shedding and Demand Side Management Enhancements to Improve the Security of a National Electrical System
273(12)
Introduction
273(1)
Demand side management and demand response services
274(3)
Automatic meter reading system and enhancement required by Demand Response (DR) services
277(1)
Potential vulnerability of communication technologies for demand control services
278(1)
Current activity in CESI RICERCA
279(3)
Conclusion
282(1)
Acknowledgments
283(1)
References
283(2)
Index 285