Sbárbaro Advanced Control and Supervision of Mineral Processing Plants

1. Auflage 2010
ISBN: 978-1-84996-106-6
Verlag: Springer-Verlag
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 312 Seiten

Advanced Control and Supervision of Mineral Processing Plants describes the use of dynamic models of mineral processing equipment in the design of control, data reconciliation and soft-sensing schemes; through examples, it illustrates tools integrating simulation and control system design for comminuting circuits and flotation columns. Coverage is given to the design of soft sensors based on either single-point measurements or more complex measurements like images. Issues concerning data reconciliation and its employment in the creation of instrument architecture and fault diagnosis are surveyed. In consideration of the widespread use of distributed control and information management systems in mineral processing, the book describes the platforms and toolkits available for implementing such systems.Applications of the techniques described in real plants are used to highlight their benefits; information for all of the examples, together with supporting MATLAB® code can be found at www.springer.com/978-1-84996-105-9.

Autoren/Hrsg.

Sbárbaro, Daniel

Inhaltsverzeichnis

1;Series Editors’ Foreword;8
2;Preface;11
3;Contents;13
4;List of Contributors;18
5;1 Introduction;19
5.1;1.1 Introduction;19
5.2;1.2 A Concentration Plant;21
5.2.1;1.2.1 Crushing Circuits;21
5.2.2;1.2.2 Grinding Circuits;22
5.2.3;1.2.3 Separation Circuits;22
5.2.4;1.2.4 Dewatering Circuits;23
5.3;1.3 Main Functions of Automation Systems in Mineral Processing Plants;25
5.3.1;1.3.1 Basic Functions;25
5.3.2;1.3.2 Advanced Functions;26
5.4;1.4 Benefits;28
5.5;1.5 Historical Perspective;28
5.6;1.6 Synopsis;30
5.7;References;31
6;2 Process Observers and Data Reconciliation Using Mass and Energy Balance Equations;33
6.1;2.1 Introduction;33
6.2;2.2 Process Variables and Operating Regimes;38
6.3;2.3 Models and Constraints;41
6.3.1;2.3.1 The Dynamic Linear Mass Balance Equation;41
6.3.2;2.3.2 The Linear Stationary and Steady-state Cases;42
6.3.3;2.3.3 The Bilinear Case;43
6.3.4;2.3.4 Multi-linear Constraints;44
6.3.5;2.3.5 Additional Constraints;45
6.3.6;2.3.6 Summary of Stationary Conservation Equations;46
6.4;2.4 Sensors, Measurement Errors and Observation Equations;46
6.4.1;2.4.1 Statistical Properties of Measurements and Measurement Errors;47
6.4.2;2.4.2 Measurement Errors for Particulate Materials;48
6.5;2.5 Observation Equations;50
6.6;2.6 General Principles of Stationary and Steady-state Data Reconciliation Methods;51
6.6.1;2.6.1 Observability and Redundancy;53
6.6.2;2.6.2 General Principles for State Estimate Calculation;56
6.7;2.7 The Linear Cases: Steady-state, Stationary and Node Imbalance Data Reconciliation Methods;58
6.7.1;2.7.1 The Steady-state Case;58
6.7.2;2.7.2 The Stationary Case;61
6.7.3;2.7.3 The Node Imbalance and Two-step Methods for Bilinear Systems;63
6.8;2.8 The Non-linear Cases;65
6.8.1;2.8.1 An Example of Substitution Methods: Mass and Heat Balance of a Thermal Exchanger;65
6.8.2;2.8.2 An Example of Hierarchical Methods: BILMAT™ Algorithm;68
6.9;2.9 Performance of Data Reconciliation Methods;70
6.10;2.10 An Overview of Dynamic Reconciliation Methods;73
6.10.1;2.10.1 Phenomenological Causal Models;75
6.10.2;2.10.2 Empirical Causal Model;76
6.10.3;2.10.3 Sub-model;78
6.10.4;2.10.4 Reconciliation Methods;79
6.10.5;2.10.5 An Example of Dynamic Reconciliation for a Simulated Flotation Circuit;82
6.11;2.11 Instrumentation Strategy Design;86
6.12;2.12 Fault Diagnosis;87
6.13;2.13 Coupling Data Reconciliation with Process Control and Optimization;90
6.14;2.14 Conclusion;91
6.15;References;96
7;3 Multivariate Image Analysis in Mineral Processing;103
7.1;3.1 Introduction;103
7.2;3.2 Background on Latent Variable Methods;106
7.2.1;3.2.1 Principal Component Analysis;106
7.2.2;3.2.2 Projection to Latent Structures (PLS);107
7.2.3;3.2.3 Statistics and Diagnostic Tools Used With Latent Variable Models;108
7.2.3.1;3.2.3.1 Scaling of Data Matrices;109
7.2.3.2;3.2.3.2 Number of Components;109
7.2.3.3;3.2.3.3 Distance to the Model Statistic;110
7.3;3.3 Nature of Multivariate Digital Images;110
7.4;3.4 Machine Vision Framework and Methods;112
7.4.1;3.4.1 Feature Extraction Methods;113
7.4.1.1;3.4.1.1 Multivariate Image Analysis;113
7.4.1.2;3.4.1.2 Multiresolution Analysis for Texture Analysis;119
7.4.1.3;3.4.1.3 Multiresolution Multivariate Image Analysis (MR-MIA);124
7.4.2;3.4.2 Feature Reduction and Analysis;125
7.5;3.5 Case Studies;127
7.5.1;3.5.1 Prediction of Flotation Froth Grade Using MIA;128
7.5.2;3.5.2 Flotation Froth Health Monitoring using MR-MIA;134
7.5.2.1;3.5.2.1 Wavelet Size Signatures;135
7.5.2.2;3.5.2.2 Froth Color Features;138
7.5.2.3;3.5.2.3 Froth Health Monitoring;139
7.5.3;3.5.3 Estimation of Run-of-Mine Ore Composition on Conveyor Belts;143
7.5.3.1;3.5.3.1 Mineral Characteristics and Vision System Hardware;145
7.5.3.2;3.5.3.2 The Mineral Recognition Problem;146
7.5.3.3;3.5.3.3 The Machine Vision Approach;147
7.5.3.4;3.5.3.4 Training and Validation of the Mineral Type Classification Model;149
7.5.3.5;3.5.3.5 Mineral Type Recognition on a Conveyor Belt;152
7.6;3.6 Conclusions;155
7.7;References;157
8;4 Soft Sensing;161
8.1;4.1 Introduction;161
8.2;4.2 Soft Sensor Models;173
8.2.1;4.2.1 Optimality Criterion;174
8.2.2;4.2.2 A General Class of Black Box Models;176
8.2.2.1;4.2.2.1 Optimal Parameter Vector;182
8.2.2.2;4.2.2.2 GrayModels;184
8.2.3;4.2.3 The Use of the Model as a Soft Sensor;184
8.2.4;4.2.4 Development of Soft Sensor Models;186
8.2.4.1;4.2.4.1 Model Structure;186
8.2.4.2;4.2.4.2 Clustering;187
8.2.4.3;4.2.4.3 Stepwise Regression;188
8.2.4.4;4.2.4.4 Soft Sensors Based on the Kalman Filter;190
8.2.4.5;4.2.4.5 Principal Components;191
8.2.4.6;4.2.4.6 Neural Networks;191
8.2.4.7;4.2.4.7 Support Vector Machines (SVM);192
8.2.5;4.2.5 On-line Parameter Estimation;192
8.2.5.1;4.2.5.1 Region of Validity;193
8.2.5.2;4.2.5.2 Soft Sensor Models and Plant models;194
8.2.6;4.2.6 Soft Sensors Designed From Measurement Features;194
8.2.7;4.2.7 Soft Sensors in Control Loops;194
8.3;4.3 Geometrical View of Modeling for Linear in the Parameter and Nonlinear Models;196
8.3.1;4.3.1 Modeling From the Inner Product Spaces Point of View;196
8.3.1.1;4.3.1.1 Inner Product Axioms;197
8.3.1.2;4.3.1.2 Projection on an Element;198
8.3.1.3;4.3.1.3 Projection on a Subspace;199
8.3.2;4.3.2 Estimation of Expected Values Through Time Averages;204
8.3.2.1;4.3.2.1 Random Functions or Stochastic Processes;205
8.3.2.2;4.3.2.2 Time Averages;207
8.3.2.3;4.3.2.3 Parameter Estimation;208
8.4;4.4 Soft Sensors in Industrial Concentrators;209
8.4.1;4.4.1 Soft Sensor Management Systems;209
8.4.2;4.4.2 Soft Sensors for Grinding Circuits;211
8.4.2.1;4.4.2.1 Particle Size Soft Sensors;211
8.4.2.2;4.4.2.2 Global OperationalWork Index Soft Sensor;214
8.4.2.3;4.4.2.3 Development of Soft Sensors for f80 and p80;217
8.4.2.4;4.4.2.4 Grindability Index Soft Sensor Using Feature Extraction;220
8.4.2.5;4.4.2.5 Templates;221
8.5;4.5 Final Remarks;226
8.6;References;227
9;5 Dynamic Simulation and Model-based Control System Design for Comminution Circuits;231
9.1;5.1 Introduction;231
9.2;5.2 Dynamic Simulation of Comminution Circuits Based on MATLAB®/Simulink®;233
9.3;5.3 Dynamic Modeling of Crushing Circuits;234
9.3.1;5.3.1 Dynamic Model of a Cone Crusher;234
9.3.2;5.3.2 Screens;236
9.3.3;5.3.3 Conveyor Belts;237
9.4;5.4 Dynamic Modeling of Wet Grinding Circuits;238
9.4.1;5.4.1 Ball and Rod Mills;238
9.4.2;5.4.2 SAG Mill;240
9.4.3;5.4.3 Hydrocyclone;242
9.4.4;5.4.4 Sump;243
9.5;5.5 Model-based Control Systems Design;244
9.5.1;5.5.1 Models for Control Design;245
9.5.2;5.5.2 Generalized Predictive Control;248
9.6;5.6 Model-based Control Strategies for Grinding Circuits;250
9.6.1;5.6.1 Regulatory Control of a Conventional Grinding Circuit;250
9.6.2;5.6.2 Supervisory Control for SAG Grinding Circuits;254
9.6.2.1;5.6.2.1 Open Loop Responses;254
9.6.2.2;5.6.2.2 A Two-level Optimizing Strategy;258
9.7;5.7 Conclusions;263
9.8;References;263
10;6 Automatic Control of Flotation Columns;267
10.1;6.1 Introduction;267
10.2;6.2 Process Description;269
10.2.1;6.2.1 Froth Depth;271
10.2.2;6.2.2 Bias Rate;271
10.2.3;6.2.3 Gas Hold-up;272
10.2.4;6.2.4 Bubble Size;272
10.3;6.3 Sensor Development and Applications;275
10.3.1;6.3.1 Interface Position Sensor;276
10.3.2;6.3.2 Bias Rate Sensor;278
10.3.3;6.3.3 Collection zone Gas Hold-up;281
10.3.4;6.3.4 Bubble Size Distribution (BSD) Sensor;286
10.3.5;6.3.5 Superficial Gas Velocity (Jg);288
10.4;6.4 Automatic Control;289
10.4.1;6.4.1 Control Hierarchy;289
10.4.2;6.4.2 Process Control;291
10.4.3;6.4.3 Application Examples;293
10.5;6.5 Future Industrial Applications;295
10.5.1;6.5.1 Sensor Development and Applications;296
10.5.2;6.5.2 Process Control and Optimization;298
10.6;References;300
11;7 Industrial Products for Advanced Control of Mineral Processing Plants;305
11.1;7.1 Introduction;305
11.2;7.2 Advanced Sensors;306
11.2.1;7.2.1 Size Distribution Sensors;306
11.2.2;7.2.2 Mill Operating Condition Sensors;307
11.2.3;7.2.3 Flotation Cell Froth Sensors;308
11.3;7.3 Concepts for Advanced Control;310
11.3.1;7.3.1 Intelligent Control;311
11.3.2;7.3.2 Model Predictive Control;312
11.4;7.4 Advanced Control Tools and Applications;315
11.5;7.5 Conclusions and Trends;323
11.6;References;324
12;Index;327