Practical Grey-box Process Identification

Theory and Applications

By: Torsten P. Bohlin

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Hardcover | 22 May 2006

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376 Pages

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16.6 x 24.1 x 2.7

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In process modelling, knowledge of the process under consideration is typically partial with significant disturbances to the model. Disturbances militate against the desirable trait of model reproducibility. "Grey-box" identification takes advantage of two sources of process information that may be available: any invariant prior knowledge and response data from experiments.

"Practical Grey-box Process Identification" is in three parts: The first part is a short review of the theoretical fundamentals of grey-box identification, focussing particularly on the theory necessary for the software presented in the second part. Part II puts the spotlight on MoCaVa, a MATLAB^(R)-compatible software tool, downloadable from springeronline.com, for facilitating the procedure of effective grey-box identification. Part III demonstrates the application of MoCaVa using two case studies drawn from the paper and steel industries. More advanced theory is laid out in an appendix and the MoCaVa source code enables readers to expand on its capabilities to their own ends.

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Theory of Grey-box Process Identification
Prospects and Problems	p. 3
Introduction	p. 3
White, Black, and Grey Boxes	p. 4
White-box Identification	p. 5
Black-box Identification	p. 6
Grey-box Identification	p. 10
Basic Questions	p. 13
Calibration	p. 14
How to Specify a Model Set	p. 15
...and a Way to Get Answers	p. 17
Tools for Grey-box Identification	p. 18
Available Tools	p. 18
Tools that Need to Be Developed	p. 21
The MoCaVa Solution	p. 23
The Model Set	p. 23
Time Variables and Sampling	p. 24
Process, Environment, and Data Interfaces	p. 25
Multi-component Models	p. 27
Expanding a Model Class	p. 29
The Modelling Shell	p. 31
Argument Relations and Attributes	p. 34
Graphic Representations	p. 37
Prior Knowledge	p. 41
Hypotheses	p. 42
Credibility Ranking	p. 43
Model Classes with Inherent Conservation Law	p. 43
Modelling 'Actuators'	p. 44
Modelling 'Input Noise'	p. 46
Standard I/O Interface Models	p. 49
Fitting and Falsification	p. 51
The Loss Function	p. 52
Nesting and Fair Tests	p. 54
Evaluating Loss and its Derivatives	p. 55
Predictor	p. 56
Equivalent Discrete-time Model	p. 56
Performance Optimization	p. 57
Controlling the Updating of Sensitivity Matrices	p. 58
Exploiting the Sparsity of Sensitivity Matrices	p. 59
Using Performance Optimization	p. 60
Search Routine	p. 62
Applicability	p. 65
Applications	p. 65
A Method for Grey-box Model Design	p. 67
What is Expected from the User?	p. 68
Limitations of MoCaVa	p. 69
Diagnostic Tools	p. 69
What Can Go Wrong?	p. 71
Tutorial on MoCaVa
Preparations	p. 77
Getting Started	p. 77
System Requirements	p. 77
Downloading	p. 77
Installation	p. 77
Starting MoCaVa	p. 78
The HTML User's Manual	p. 78
The 'Raw' Data File	p. 78
Making a Data File for MoCaVa	p. 78
Calibration	p. 83
Creating a New Project	p. 83
The User's Guide and the Pilot Window	p. 85
Specifying the Data Sample	p. 86
The Time Range Window	p. 86
Creating a Model Component	p. 88
Handling the Component Library	p. 89
Entering Component Statements	p. 90
Classifying Arguments	p. 92
Specifying I/O Interfaces	p. 95
Specifying Argument Attributes	p. 98
Specifying Implicit Attributes	p. 100
Assigning Data	p. 100
Specifying Model Class	p. 101
Simulating	p. 103
Setting the Origin of the Free Parameter Space	p. 103
Selecting Variables to be Plotted	p. 104
Appraising Model Class	p. 105
Handling Data Input	p. 106
Fitting a Tentative Model Structure	p. 107
Search Parameters	p. 108
Appraising the Search Result	p. 111
Testing a Tentative Model Structure	p. 113
Appraising a Tentative Model	p. 116
Nesting	p. 118
Interpreting the Test Results	p. 119
Refining a Tentative Model Structure	p. 121
Multiple Alternative Structures	p. 122
Augmenting a Disturbance Model	p. 124
Checking the Final Model	p. 132
Terminals and 'Stubs'	p. 134
Copying Components	p. 135
Effects of Incorrect Disturbance Structure	p. 138
Exporting/Importing Parameters	p. 140
Suspending and Exiting	p. 141
The Score Table	p. 142
Resuming a Suspended Session	p. 143
Checking Integration Accuracy	p. 143
Some Modelling Support	p. 147
Modelling Feedback	p. 147
The Model Class	p. 148
User's Functions and Library	p. 153
Rescaling	p. 154
Importing External Models	p. 159
Using Dymola^2+TM as Modelling Tool for MoCaVa	p. 160
Detecting Over-parametrization	p. 166
Assigning Variable Input to Imported Models	p. 170
Selective Connection of Arguments to Dymola^2+TM Models	p. 173
Case Studies
Case 1: Rinsing of the Steel Strip in a Rolling Mill	p. 185
Background	p. 185
Step 1: A Phenomenological Description	p. 185
The Process Proper	p. 185
The Measurement Gauges	p. 188
The Input	p. 189
Step 2: Variables and Causality	p. 189
The variables	p. 189
Cause and effect	p. 190
Data Preparation	p. 191
Relations to Measured Variables	p. 192
Step 3: Modelling	p. 194
Basic Mass Balances	p. 194
Strip Input	p. 201
Step 4: Calibration	p. 203
Refining the Model Class	p. 206
The Squeezer Rolls	p. 206
The Entry Rolls	p. 211
Continuing Calibration	p. 213
Refining the Model Class Again	p. 215
Ventilation	p. 215
More Hypothetical Improvements	p. 217
Effective Mixing Volumes	p. 217
Avoiding the pitfall of 'Data Description'	p. 219
Modelling Disturbances	p. 222
Pickling	p. 222
State Noise	p. 223
Determining the Simplest Environment Model	p. 225
Variable Input Acid Concentration	p. 225
Unexplained Variation in Residual Acid Concentration	p. 225
Checking for Possible Over-fitting	p. 229
Appraising Roller Conditions	p. 233
Conclusions from the Calibration Session	p. 233
Case 2: Quality Prediction in a Cardboard Making Process	p. 235
Background	p. 235
Step 1: A Phenomenological Description	p. 235
Data Preparation	p. 237
Step 2: Variables and Causality	p. 244
Relations to Measured Variables	p. 247
Step 3: Modelling	p. 248
The Bending Stiffness	p. 248
The Paper Machine	p. 253
The Pulp Feed	p. 260
Control Input	p. 262
The Pulp Mixing	p. 265
Pulp Input	p. 267
The Pulp Constituents	p. 269
Step 4: Calibration	p. 271
Expanding the Tentative Model Class	p. 279
The Pulp Refining	p. 279
The Mixing-tank Dynamics	p. 284
The Machine Chests	p. 287
Filtering the "Kappa" Input	p. 289
Checking for Over-fitting: The SBE Rule	p. 290
Ending a Calibration Session	p. 293
'Black-box' vs 'White-box' Extensions	p. 293
Determination vs Randomness	p. 294
Modelling Disturbances	p. 295
Calibrating Models with Stochastic Input	p. 296
Determination vs Randomness Revisited	p. 299
A Local Minimum	p. 304
Conclusions from the Calibration Session	p. 306
Mathematics and Algorithms	p. 313
The Model Classes	p. 313
The Loss Derivatives	p. 316
The ODE Solver	p. 317
The Reference Trajectory	p. 317
The State Deviation	p. 318
The Equivalent Discrete-time Sensitivity Matrices	p. 318
The Predictor	p. 321
The Equivalent Discrete-time Model	p. 322
Mixed Algebraic and Differential Equations	p. 322
Performance Optimization	p. 326
The Sensitivity Update Control Function	p. 327
Memoization	p. 330
The Search Routine	p. 330
Library Routines	p. 331
Output Conversion	p. 331
Input Interpolators	p. 331
Input Filters	p. 334
Disturbance Models	p. 335
The Advanced Specification Window	p. 337
Optimization for Speed	p. 337
User's Checkpoints	p. 338
Internal Integration Interval	p. 338
Debugging	p. 339
Glossary	p. 341
References	p. 345
Index	p. 349
Table of Contents provided by Ingram. All Rights Reserved.

Practical Grey-box Process Identification

Theory and Applications

At a Glance

Hardcover

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