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E-raamat: Soft Sensors for Monitoring and Control of Industrial Processes

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Soft sensors are inferential estimators, drawing conclusions from process observations when hardware sensors are unavailable or unsuitable; they have an important auxiliary role in sensor validation when performance declines through senescence or fault accumulation.The non-linear behaviour exhibited by many industrial processes can be usefully modelled with the techniques of computational intelligence: neural networks; fuzzy systems and nonlinear partial least squares.Soft Sensors for Monitoring and Control of Industrial Processes underlines the real usefulness of each approach and the sensitivity of the individual steps in soft-sensor design to the choice of one or the other. Design paths are suggested and readers shown how to evaluate the effects of their choices. All the case studies reported, resulting from collaborations between the authors and a number of industrial partners, raised challenging soft-sensor-design problems. The applications of soft sensors presented in this volume are designed to cope with the whole range from measuring system backup and what-if analysis through real-time prediction for plant control to sensor diagnosis and validation. Some of the soft sensors developed here are implemented on-line at industrial plants.Features:soft-sensor design;advice on data selection and choice of model structure;model validation;strategies for the improvement of soft-sensor performance;uses of soft sensors in fault detection and sensor validation;soft sensors in use in industrial applications such as a debutanizer column and a sulfur recovery unit.This monograph guides interested readers - researchers, graduate students and industrial process technologists - through the design of their own soft sensors. It is self-contained with full references and appraisal of existing literature and data sets for some of the case studies can be downloaded from springer.com.

This book reviews current design paths for soft sensors, and guides readers in evaluating different choices. The book presents case studies resulting from collaborations between the authors and industrial partners. The solutions presented, some of which are implemented on-line in industrial plants, are designed to cope with a wide range of applications from measuring system backup and what-if analysis through real-time prediction for plant control to sensor diagnosis and validation.

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From the reviews:









"The monograph is devoted to design procedure of soft sensors and their applications for solving some industrial problems. The monograph highlights the importance of using knowledge from industrial experts and from the existing industrial process literature. It is full of valuable information about the veracity of different methods. The monograph will be appreciated by the industrial control engineer for its practical insights and by the academic staff for its case-study applications."(Tadeusz Kaczorek, Zentralblatt MATH, Vol. 1136 (14), 2008)

1 Soft Sensors in Industrial Applications
1
1.1 Introduction
1
1.2 State of the Art
4
1.2.1 Data Collection and Filtering
5
1.2.2 Variables and Model Structure Selection
6
1.2.3 Model Identification
9
1.2.4 Model Validation
10
1.2.5 Applications
10
2 Virtual Instruments and Soft Sensors
15
2.1 Virtual Instruments
15
2.2 Applications of Soft Sensors
22
2.2.1 Back-up of Measuring Devices
22
2.2.2 Reducing the Measuring Hardware Requirements
23
2.2.3 Real-time Estimation for Monitoring and Control
24
2.2.4 Sensor Validation, Fault Detection and Diagnosis
24
2.2.5 What-if Analysis
25
3 Soft Sensor Design
27
3.1 Introduction
27
3.2 The Identification Procedure
27
3.3 Data Selection and Filtering
30
3.4 Model Structures and Regressor Selection
34
3.5 Model Validation
46
4 Selecting Data from Plant Database
53
4.1 Detection of Outliers for a Debutanizer Column: A Comparison of Different Approaches
53
4.1.1 The 3σEdit Rule
54
4.1.2 Jolliffe Parameters with Principal Component Analysis
66
4.1.3 Jolliffe Parameters with Projection to Latent Structures
68
4.1.4 Residual Analysis of Linear Regression
71
4.2 Comparison of Methods for Outlier Detection
72
4.3 Conclusions
80
5 Choice of the Model Structure
81
5.1 Introduction
81
5.2 Static Models for the Prediction of NOx Emissions for a Refinery
82
5.3 Linear Dynamic Models for RON Value Estimation in Powerformed Gasoline
87
5.4 Soft Computing Identification Strategies for a Sulfur Recovery Unit
90
5.5 Comparing Different Methods for Inputs and Regressor Selection for a Debutanizer Column
97
5.5.1 Simple Correlation Method
98
5.5.2 Partial Correlation Method
100
5.5.3 Mallow's Coefficients with a Linear Model
101
5.5.4 Mallow's Coefficients with a Neural Model
102
5.5.5 PLS-based Methods
103
5.5.6 Comparison
108
5.6 Conclusions
114
6 Model Validation
115
6.1 Introduction
115
6.2 The Debutanizer Column
116
6.3 The Cascaded Structure for the Soft Sensor
117
6.4 The One-step-ahead Predictor Soft Sensor
127
6.4.1 Refinement of the One-step-ahead Soft Sensor
134
6.5 Conclusions
142
7 Strategies to Improve Soft Sensor Performance
143
7.1 Introduction
143
7.2 Stacked Neural Network Approach for a Sulfur Recovery Unit
144
7.3 Model Aggregation Using Fuzzy Logic for the Estimation of RON in Powerformed Gasoline
158
7.4 Conclusions
164
8 Adapting Soft Sensors to Applications
167
8.1 Introduction
167
8.2 A Virtual Instrument for the What-if Analysis of a Sulfur Recovery Unit
167
8.3 Estimation of Pollutants in a Large Geographical Area
174
8.4 Conclusions
181
9 Fault Detection, Sensor Validation and Diagnosis
183
9.1 Historical Background
183
9.2 An Overview of Fault Detection and Diagnosis
184
9.3 Model-based Fault Detection
187
9.3.1 Fault Models
188
9.3.2 Fault Detection Approaches
189
9.3.3 Improved Model-based Fault Detection Schemes
197
9.4 Symptom Analysis and Fault Diagnosis
199
9.5 Trends in Industrial Applications
201
9.6 Fault Detection and Diagnosis: A Hierarchical View
202
9.7 Sensor Validation and Soft Sensors
203
9.8 Hybrid Approaches to Industrial Fault Detection, Diagnosis and Sensor Validation
204
9.9 Validation of Mechanical Stress Measurements in the JET TOKAMAK
207
9.9.1 Heuristic Knowledge
208
9.9.2 Exploiting Partial Physical Redundancy
209
9.9.3 A Hybrid Approach to Fault Detection and Classification of Mechanical Stresses
211
9.10 Validation of Plasma Density Measurement at ENEA-FTU
217
9.10.1 Knowledge Acquisition
218
9.10.2 Symptom Definition
219
9.10.3 Design of the Detection Tool: Soft Sensor and Fuzzy Model Validator
219
9.10.4 The Main Fuzzy Validator
221
9.10.5 Performance Assessment
222
9.11 Basic Terminology in Fault Detection and Diagnosis
223
9.12 Conclusions
225
Appendix A Description of the Plants 227
A.1 Introduction
227
A.2 Chimneys of a Refinery
227
A.3 Debutanizer Column
229
A.4 Powerformer Unit
232
A.5 Sulfur Recovery Unit
233
A.6 Nuclear Fusion Process: Working Principles of Tokamaks
235
A.6.1 Nuclear Fusion
235
A.6.2 Tokamak Working Principles
238
A.7 Machine Diagnostic System at JET and the Monitoring of Mechanical Stresses Under Plasma Disruptions
241
A.7.1 The MDS Measurement System
241
A.7.2 Disruptions and Mechanical Stresses
242
A.8 Interferometry-based Measurement System for Plasma Density at FTU
243
Appendix B Structured References 245
B.1 Theoretical Contributions
245
B.1.1 Books
245
B.1.2 Data Collection and Filtering, Effect of Missing Data
246
B.1.3 Variables and Model Structure Selection
247
B.1.4 Model Identification
248
B.1.5 Model Validation
249
B.1.6 Fault Detection and Diagnosis, Sensor Validation
250
B.2 Applicative Contributions
252
References 257
Index 267


Luigi Fortuna has been Professor of System Theory at the University of Catania since 1994. He has published more than 280 technical papers and is co-author of seven books including, for Springer, Soft Computing (1-85233-308-1, 2001, 280 pp, SC) and Microelectronics and Microsystems (1-85233-499-1, 2001, 222 pp, HC). He holds several USA patents. His scientific interests include automatic control, nonlinear science and complexity, chaos, cellular neural networks with applications in bioengineering. He has been an IEEE Fellow since 2000.



Salvatore Graziani received a M.S. degree in electronic engineering and a Ph.D. in electrical engineering, both from the Università degli Studi di Catania, Italy, in 1990 and 1994, respectively. In 1990 he joined the Dipartimento di Ingegneria Elettrica, Elettrica e dei Sistemi, Università di Catania, where he is an Associate Professor of Electric and Electronic Measurement and Instrumentation. His primary research interests lie in the field of sensors and actuators, signal processing, multisensor data fusion, neural networks, and smart sensors. He has co-authored several scientific papers and one book.



Maria Gabriella Xibilia holds an M.S. degree in electronic engineering and a Ph.D. in electrical engineering, both from the Università degli Studi di Catania, Italy, in 1991 and 1995, respectively. In 1998 she joined the Dipartimento di Matematica of the Università degli Studi di Messina, where she is an Assistant Professor of Automatic Control. Her primary research interests lie in the field of process control, system identification, soft computing and fault detection. She has coauthored several scientific papers and three books.
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