Table Of ContentChemometric Monitoring:
Product Quality Assessment,
Process Fault Detection,
and Applications
Chemometric Monitoring:
Product Quality Assessment,
Process Fault Detection,
and Applications
By
Madhusree Kundu, Palash Kumar Kundu,
and Seshu Kumar Damarla
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Library of Congress Cataloging-in-Publication Data
Names: Kundu, Madhusree, author. | Kundu, Palash Kumar, author. | Damarla,
Seshu K., author.
Title: Chemometric monitoring : product quality assessment, process fault
detection and applications / Madhusree Kundu, Palash Kumar Kundu, Seshu K.
Damarla.
Description: Boca Raton : CRC Press, 2018. | Includes bibliographical
references and index.
Identifiers: LCCN 2017015762| ISBN 9781498780070 (hardback : acid-free paper)
| ISBN 9781315155135 (ebook)
Subjects: LCSH: Chemometrics.
Classification: LCC QD75.4.C45 K86 2018 | DDC 543.01/5195–dc23
LC record available at https://lccn.loc.gov/2017015762
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Dedication
Dedicated in the fond memory of our beloved
brother, Late Pradyumna Majumer
Dr. Madhusree Kundu
Dr. Palash Kundu
Dedicated to my father, Late Damarla Venugopalarao
Seshu Kumar Damarla
Contents
Preface ...........................................................................................................................................xiii
Acknowledgments .....................................................................................................................xvii
About the Authors ......................................................................................................................xix
Introduction .................................................................................................................................xxi
Chapter 1 Data generation, collection, analysis, and preprocessing ................................1
1.1 Data: Different data types and presentation of data .......................................................1
1.2 Data generation: Design of experiments ...........................................................................4
1.2.1 Factorial design and illustration ...........................................................................5
1.2.1.1 The effect of Zn loading ........................................................................6
1.2.1.2 Two-factor interaction effects ...............................................................8
1.2.1.3 Three-factor interaction effects ............................................................8
1.3 Computer-based data acquisition ....................................................................................11
1.3.1 Sensor/transducer ................................................................................................11
1.3.2 Analog-to-digital (A/D) converter .....................................................................12
1.3.3 Digital-to-analog (D/A) converter ......................................................................12
1.4 Basic statistical measures and regression .......................................................................12
1.4.1 Mean, median, mode ............................................................................................12
1.4.2 Variance and standard deviation .......................................................................13
1.4.3 Covariance and correlation coefficient ..............................................................13
1.4.4 Frequency................................................................................................................14
1.4.5 Distribution ...........................................................................................................15
1.4.6 Uncertainty ............................................................................................................18
1.4.7 Confidence interval ..............................................................................................19
1.4.8 Hypothesis Testing ...............................................................................................21
1.4.9 Correlation .............................................................................................................27
1.4.10 Regression ..............................................................................................................27
1.4.11 Chi-squared test ....................................................................................................28
1.5 Stochastic and stationary processes .................................................................................29
1.6 Data preprocessing .............................................................................................................32
1.6.1 Outlier detection ...................................................................................................32
1.6.2 Data reconciliation ................................................................................................35
1.6.3 Data smoothing and filtering ..............................................................................37
1.6.3.1 Smoothing signal .................................................................................37
1.6.3.2 Filtering signal .....................................................................................40
1.6.4 Transform and transformation ...........................................................................42
References .....................................................................................................................................49
vii
viii Contents
Chapter 2 Chemometric techniques: Theoretical postulations .......................................51
2.1 Chemometrics .....................................................................................................................51
2.2 Principal component analysis (PCA) ...............................................................................52
2.2.1 PCA decomposition of data.................................................................................53
2.2.2 Principle of nearest neighborhood ....................................................................54
2.2.3 Hotelling T2 and Q statistics ...............................................................................55
2.3 Similarity .............................................................................................................................56
2.3.1 PCA similarity ......................................................................................................57
2.3.2 Distance-based similarity ....................................................................................59
2.3.3 Combined similarity factor .................................................................................60
2.3.4 Dissimilarity and Karhunen-Loeve (KL) expansion .......................................61
2.3.5 Moving window-based pattern matching using similarity/
dissimilarity factors .............................................................................................63
2.4 Clustering ............................................................................................................................65
2.4.1 Hierarchical clustering ........................................................................................65
2.4.2 Nonhierarchical clustering..................................................................................65
2.4.3 Modified K-means clustering using similarity factors ...................................66
2.5 Partial least squares (PLS) .................................................................................................69
2.5.1 Linear PLS ..............................................................................................................69
2.5.2 Dynamic PLS .........................................................................................................73
2.6 Cross-correlation coefficient .............................................................................................75
2.7 Sammon’s nonlinear mapping ..........................................................................................77
2.8 Moving window-based PCA .............................................................................................81
2.8.1 Mathematical postulates of recursive PCA.......................................................82
2.9 Discriminant function/hyperplane .................................................................................86
2.9.1 Linear discriminant analysis (LDA) ..................................................................87
2.9.2 Support vector machine (SVM) ..........................................................................90
2.9.2.1 Determination of decision function in SVM ....................................90
2.9.2.2 Determination of optimal separating hyperplane in SVM ...........92
2.10 Multiclass decision function .............................................................................................94
2.10.1 One against the rest approach ............................................................................94
2.10.2 One against one approach ...................................................................................94
2.10.3 Decision directed acyclic graph (DDAG)-based approach .............................95
2.10.3.1 DDAG algorithm ..................................................................................95
References .....................................................................................................................................97
Chapter 3 Classification among various process operating conditions .......................101
3.1 Yeast fermentation bioreactor process ...........................................................................102
3.1.1 Modeling and dynamic simulation of yeast fermentation bioreactor ........102
3.1.1.1 The process description ....................................................................102
3.1.1.2 Mathematical model ..........................................................................102
3.1.1.3 Analysis of dynamic behavior of yeast fermentation
bioreactor ........................................................................................105
3.1.2 Generation of process historical database for yeast
fermentation process ......................................................................................111
3.1.3 Application of modified K-means clustering algorithm on historical
database for yeast fermentation process .........................................................112
3.2 Commercial double-effect evaporator ...........................................................................112
3.2.1 Modeling and dynamic simulation of double effect evaporator .................112
Contents ix
3.2.1.1 The process description ....................................................................112
3.2.1.2 Mathematical model of double-effect evaporator .........................120
3.2.1.3 Analysis of dynamic behavior of double-effect evaporator
model ...................................................................................................122
3.2.2 Generation of historical database for double-effect evaporation process ..127
3.2.3 Application of modified K-means clustering algorithm on double-
effect evaporation process database .................................................................127
3.3 Continous crystallization process ..................................................................................128
3.3.1 Modeling and dynamic simulation of continuous
crystallization process ...................................................................................128
3.3.1.1 The process description ....................................................................131
3.3.1.2 Mathematical model of continuous crystallization process ........131
3.3.2 Generation of historical database for continuous crystallization process .133
3.3.3 Application of modified K-means clustering algorithm in
continuous crystallizer ......................................................................................134
References ...................................................................................................................................137
Chapter 4 Detection of abnormal operating conditions in processes using
moving window-based pattern matching ......................................................139
4.1 Detection of abnormal operating conditions in a fluid catalytic cracking unit ......140
4.1.1 Introduction to the fluid catalytic cracking (FCC) process ...........................140
4.1.2 FCC process description ....................................................................................141
4.1.3 Generation of historical database for FCC process ........................................142
4.1.4 Application of moving window-based pattern-matching algorithm on
historical database of FCC process ...................................................................143
4.2 Detection of abnormal operating conditions in continuous stirred tank heater ....151
4.2.1 Continuous stirred tank heater ........................................................................151
4.2.2 Generation of historical database for CSTH ...................................................154
4.2.3 Application of combined similarity factor and dissimilarity factor-
based pattern-matching algorithm on a CSTH historical database ............154
4.3 Detection of abnormal operating conditions in simulated industrial gas-phase
polyethylene reactor .........................................................................................................157
4.3.1 Simulated industrial gas-phase polyethylene reactor ...................................157
4.3.2 Generation of historical database for simulated industrial
polyethylene reactor ...........................................................................................160
4.3.3 Application of combined similarity factor and dissimilarity factor-
based pattern-matching algorithm on polyethylene historical database ...160
References .....................................................................................................................................166
Chapter 5 Design of an automated tea grader ...................................................................169
5.1 Electronic tongue: A biomimetic device .......................................................................169
5.2 Experimentation ...............................................................................................................173
5.2.1 E-tongue-based instrumentation and principles ...........................................173
5.2.2 E-tongue signature generation using various commercial brands of tea ..175
5.3 Tea data preprocessing ....................................................................................................176
5.4 Dissimilarity-based tea grader .......................................................................................178
5.4.1 Authentication/classification algorithm ..........................................................178
5.4.2 Performance evaluation of the designed dissimilarity-based
tea classifier .....................................................................................................185
