1st Edition
Process Imaging For Automatic Control
As industrial processes and their corresponding control models increase in complexity, the data provided by traditional point sensors is no longer adequate to ensure product quality and cost-effective operation. Process Imaging for Automatic Control demonstrates how in-process imaging technologies surpass the limitations of traditional monitoring systems by providing real-time multidimensional measurement and control data. Combined with suitable data extraction and control schemes, such systems can optimize the performance of a wide variety of industrial processes.
Contributed by leading international experts, Process Imaging for Automatic Control offers authoritative, comprehensive coverage of this new area of process control technology, including:
From theory to practical implementation, this book is the first to treat the entire range of imaging techniques and their application to process control. Supplying broad coverage with more than 270 illustrations and nearly 700 cited references, it presents an accessible introduction to this rapidly growing, interdisciplinary technology.
Preface
THE CHALLENGE
D.M. Scott and H. McCann
Motivation
Roadmap
Vista
PROCESS MODELING
P. Linke, A. Kokossis, J.U. Repke, and G. Wozny
Introduction
Simulation Versus Optimization
Process Models for Imaging and Analysis
Process Modeling for Design, Control, and Diagnostics
References
DIRECT IMAGING TECHNOLOGY
S. Someya and M. Takei
Introduction
Light Sources
Sensors
Optical Components
Applications
Machine Vision
References
PROCESS TOMOGRAPHY
B.S. Hoyle, H. McCann, and D.M. Scott
Introduction
Tomographic Sensor Modalities
Image Reconstruction
Current Tomography Systems
Applications
References
IMAGE PROCESSING AND FEATURE EXTRACTION
D. Zhao
Introduction
Image Enhancement
Image Restoration
Segmentation
Feature Representation
Morphological Image Processing and Analysis
References
STATE ESTIMATION
J. Kaipio, S. Duncan, E. Somersalo, A. Seppänen, and A. Voutilainen
Introduction
Real-Time Recursive Estimation: Kalman Predictors and Filters
On-Line and Transient Estimation: Smoothers
Nonlinear and Non-Gaussian State Estimation
Partially Unknown Models: Parameter Estimation
Further Topics
Observation and Evolution Models in Process Industry
Example: Convection-Diffusion Models
References
CONTROL SYSTEMS
S. Duncan, J. Kaipio, A. Ruuskanen, M. Malinen, and A. Seppänen
Introduction
Modeling the Process
Feedback Control
Control Design
Practicalities of Implementing Controllers
Conclusion
References
IMAGING DIAGNOSTICS FOR COMBUSTION CONTROL
V. Sick and H. McCann
Introduction
Combustor Types
Imaging in Combustors
Results from Combustion Imaging
Conclusions
References
MULTIPHASE FLOW MEASUREMENTS
T. Dyakowski and A.J. Jaworski
Introduction
Flow Pattern Recognition
Flow Pattern Imaging
Solids Mass Flow Measurements
References
APPLICATIONS IN THE CHEMICAL PROCESS INDUSTRY
D.M. Scott
Introduction
Applications Related to Process Control
Applications Related to Process/Product R&D
Conclusion
Reference
MINERAL AND MATERIAL PROCESSING
R.A. Williams
Motivation for Development of Image-Based Techniques
Design of Comminution Equipment
Granular Flow and Bulk Transportation
Particle Classification in Cyclones
Performance of Flotation Cells and Columns
Solids Settling and Water Recovery
Microscale Analysis of Granules, Flocs, and Sediments
Concluding Remarks
References
APPLICATIONS IN THE METALS PRODUCTION INDUSTRY
J.A. Coveney, N. Gray, and A.K. Kyllo
Introduction
Detection of Slag Entrainment
Measurement of Furnace Refractory Wear
Flow Measurement
Imaging of Solid State Phase Change
Conclusion
References
INDEX
Biography
David M. Scott, Hugh McCann