Industrial Tomography

Industrial Tomography

Systems and Applications

2nd Edition - May 6, 2022

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  • Editor: M Wang
  • eBook ISBN: 9780128233078
  • Paperback ISBN: 9780128230152

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Description

Industrial Tomography: Systems and Applications, Second Edition thoroughly explores the important techniques of industrial tomography, also discusses image reconstruction, systems, and applications. This book presents complex processes, including the way three-dimensional imaging is used to create multiple cross-sections, and how computer software helps monitor flows, filtering, mixing, drying processes, and chemical reactions inside vessels and pipelines. This book is suitable for materials scientists and engineers and applied physicists working in the photonics and optoelectronics industry or in the applications industries.

Key Features

  • Provides a comprehensive discussion on the different formats of tomography, including advances in visualization and data fusion
  • Includes an excellent overview of image reconstruction using a wide range of applications
  • Presents a comprehensive discussion of tomography systems and their applications in a wide variety of industrial processes

Readership

This book is suitable for materials scientists and engineers and applied physicists working in the photonics and optoelectronics industry or in the applications industries.

Table of Contents

  • Cover image
  • Title page
  • Table of Contents
  • Copyright
  • List of contributors
  • Preface
  • Introduction
  • Understanding the basics of sensor design and reconstruction
  • Optimizing data collection and analysis for industrial information
  • Part One. Tomographic modalities
  • 1. Electrical Capacitance Tomography
  • 1.1. Introduction
  • 1.2. Principle of operation
  • 1.3. Image reconstruction algorithms
  • 1.4. Data acquisition system
  • 1.5. Electrical capacitance volume tomography
  • 1.6. Illustrative examples and discussion
  • 1.7. Flow velocimetry with ECVT
  • 1.8. Three-phase flow decomposition by exploiting Maxwell–Wagner–Sillars effect
  • 1.9. Displacement–current phase tomography (DCPT) and water-dominated flow velocimetry
  • 1.10. Recent progress with AECVT
  • 1.11. RVM for the determination of uncertainty in reconstruction
  • 1.12. Conclusion
  • 1.13. Future trends
  • 1.14. Source of further information
  • 2. Electrical impedance tomography
  • 2.1. Introduction
  • 2.2. Fundamentals of measurement
  • 2.3. Principle of electrical impedance tomography sensing
  • 2.4. Data acquisition
  • 2.5. Image reconstruction
  • 2.6. Imaging capability
  • 2.7. EIT data for process application
  • 2.8. Future trends
  • 2.9. Sources of further information
  • 3. Electromagnetic induction tomography
  • 3.1. Introduction
  • 3.2. Principle of operation and governing equations
  • 3.3. Solution to the forward problem
  • 3.4. Solution to the inverse problem
  • 3.5. System hardware
  • 3.6. Applications
  • 3.7. Conclusions and outlook
  • 4. Magnetic resonance imaging
  • 4.1. Introduction to MRI and NMR
  • 4.2. MRI: basic imaging principles
  • 4.3. Methods: basic imaging techniques
  • 4.4. Advanced data acquisition: fast imaging approaches
  • 4.5. Applications in engineering
  • 4.6. Future trends
  • 4.7. Conclusions
  • 4.8. Sources of further information and advice
  • 5. Chemical Species Tomography
  • 5.1. Introduction
  • 5.2. Absorption spectroscopy for Chemical Species Tomography
  • 5.3. Image reconstruction for low beam count systems
  • 5.4. Beam array design and optimization
  • 5.5. Design of Chemical Species Tomography systems
  • 5.6. Case studies
  • 5.7. Future trends
  • 6. X-ray computed tomography
  • 6.1. Introduction
  • 6.2. Variants of X-ray computed tomography for process applications
  • 6.3. X-ray sources for process tomography
  • 6.4. X-ray detectors
  • 6.5. Attenuation measurement with X-rays
  • 6.6. Beam hardening and radiation scattering
  • 6.7. Cone-beam X-ray computed tomography for gas holdup measurements
  • 6.8. Static mixer studies with ultrafast electron beam X-ray tomography
  • 6.9. Future trends
  • 6.10. Sources of further information and advice
  • 7. Radioisotope tracer techniques
  • 7.1. Nuclear medicine imaging
  • 7.2. Industrial applications
  • 7.3. Particle tracking
  • 8. Ultrasound tomography
  • 8.1. Introduction
  • 8.2. Ultrasound theory
  • 8.3. Equipment and techniques
  • 8.4. Industrial applications
  • 8.5. Summary
  • 8.6. Future trends
  • 8.7. Source of further information and advice
  • 9. Spectro-tomography
  • 9.1. Introduction
  • 9.2. Multidimensional process sensing
  • 9.3. Spectroscopic sensing
  • 9.4. Spectro-tomography principles
  • 9.5. Spectro-tomography system implementation
  • 9.6. Trial demonstrations
  • 9.7. Future trends for spectro-tomography
  • 10. Electron tomography
  • 10.1. Introduction
  • 10.2. Tomography in the electron microscope
  • 10.3. Practical electron tomography
  • 10.4. Advanced electron tomography
  • 10.5. Off-line electron tomography
  • 10.6. Electron tomography of dynamic processes
  • 10.7. Future trends
  • 10.8. Sources of further information
  • Part Two. Tomographic image reconstruction and data fusion
  • 11. Mathematical concepts for image reconstruction in tomography
  • 11.1. Introduction
  • 11.2. Transmission tomography
  • 11.3. Electrical tomography
  • 11.4. Diffraction tomography
  • 11.5. Future trends
  • 11.6. Source of further information
  • 12. Direct image reconstruction in electrical tomography and its applications
  • 12.1. Introduction
  • 12.2. Invariant property of the governing equation via conformal transformation
  • 12.3. Typical direct algorithms for electrical tomography
  • 12.4. Dirichlet-to-Neumann/Neumann-to-Dirichlet maps
  • 12.5. Calculation of the scattering transforms
  • 12.6. Applications in image reconstruction
  • 12.7. Dynamic flame monitoring
  • 12.8. Future trends
  • 12.9. Further information
  • 13. Machine learning process information from tomography data
  • 13.1. Introduction
  • 13.2. Machine learning methods for information needs
  • 13.3. Artificial neural networks for IPT applications
  • 13.4. Case study A—estimating multiphase flow parameters
  • 13.5. Case study B—estimating inline rheology properties of product flow
  • 13.6. Future trends
  • 14. Advanced electrical tomography visualisation
  • 14.1. Introduction
  • 14.2. Background
  • 14.3. Advanced visualization of multiphase flow
  • 14.4. Multidimensional data fusion
  • 14.5. Future trends
  • Part Three. Tomography applications
  • 15. Applications of electrical resistance tomography to chemical engineering
  • 15.1. Introduction
  • 15.2. Applications of ERT
  • 15.3. Conclusions
  • 16. From process understanding to process control—Applications in industry
  • 16.1. Introduction
  • 16.2. Applications to improve process understanding
  • 16.3. Process modeling and optimization
  • 16.4. Process analytics
  • 16.5. Process monitoring for process control
  • 16.6. Conclusions and future trends
  • 17. Applications of tomography in oil–gas industry—Part 1
  • 17.1. Introduction
  • 17.2. Seismic tomography in hydrocarbon exploration and reservoir characterization
  • 17.3. Multicomponent seismic data for reservoir characterization
  • 17.4. Simultaneous inversion of time-lapse seismic surveys for reservoir monitoring
  • 17.5. Borehole seismic surveys
  • 17.6. Future trends
  • 17.7. Source of further information and advice
  • 18. Applications of tomography in oil–gas industry—Part 2
  • 18.1. Introduction
  • 18.2. Cross-well electromagnetic tomography in hydrocarbon reservoir monitoring
  • 18.3. Potential of tomography in hydrocarbon production monitoring
  • 18.4. Future trends
  • 18.5. Source of further information and advice
  • 19. Applications of tomography in multiphase transportation
  • 19.1. Introduction
  • 19.2. Flow pattern and flow pattern identification with IPT
  • 19.3. Multiphase transportation process measurement and monitoring with IPTs
  • 19.4. IPT in multiphase flow measurement with multisensor fusion
  • 19.5. Conclusions and future trends
  • 20. Measurement and characterization of slurry flow using Electrical Resistance Tomography
  • 20.1. Introduction
  • 20.2. Physical mechanisms governing hydraulic transport of solid particles
  • 20.3. Slurry flow characterization with Electrical Resistance Tomography
  • 20.4. Limitations of ERT in slurry flow measurement and characterization
  • 20.5. Conclusions and future trends
  • 20.6. Sources of further information
  • 21. Application of tomography in microreactors
  • 21.1. Introduction
  • 21.2. X-ray and γ-ray tomography
  • 21.3. X-ray and γ-ray absorption/radiography tomography
  • 21.4. Nuclear magnetic resonance imaging
  • 21.5. Positron emission tomography
  • 21.6. Electrical impedance tomography
  • 21.7. Future trends
  • 22. X-ray tomography of fluidized beds
  • 22.1. Introduction
  • 22.2. Imaging of fluid beds
  • 22.3. Computational models and their experimental validation
  • 22.4. Experimental studies
  • 22.5. Data evaluation
  • 22.6. Validation experiments for narrow and wide particle size distribution
  • 22.7. Comparison between different validation approaches
  • 22.8. Validation for reactor scale-up
  • 22.9. Ultrafast X-ray computer tomography
  • 22.10. Future trends
  • 23. Applications of tomography in bubble column and fixed bed reactors
  • 23.1. Introduction
  • 23.2. Bubble column reactors
  • 23.3. Fixed bed reactors
  • 23.4. Future trends
  • 23.5. Sources of further information
  • 24. Applications of tomography in mixing process
  • 24.1. Introduction
  • 24.2. Review of tomographic techniques utilizing for different kinds of mixing processes
  • 24.3. How to extract information about mixing from tomographic images
  • 24.4. Application of one-plane tomography in mixing process
  • 24.5. Mixing process monitoring by twin-plane tomographic system
  • 24.6. Toward to improvement of process measurement
  • 24.7. Future trends
  • 25. Applications of electrical capacitance tomography in industrial systems
  • 25.1. Introduction
  • 25.2. Two-phase gas–solid systems
  • 25.3. Two-phase air–water systems
  • 25.4. Three-phase systems
  • 25.5. Future trends
  • 25.6. Source of further information
  • 26. Applications of AI and possibilities for process control
  • 26.1. Introduction
  • 26.2. Artificial intelligence
  • 26.3. Multiphase flow processes for testing AI techniques
  • 26.4. AI techniques relevant for process control
  • 26.5. Possibilities for AI-assisted control
  • 26.6. Future trends
  • 26.7. Sources of further information
  • 27. Diverse tomography applications
  • 27.1. Introduction
  • 27.2. Packed column monitoring with electrical tomography
  • 27.3. 3D Cell spheroid imaging by electrical impedance tomography
  • 27.4. Fabrics pressure mapping using electrical impedance tomography
  • 27.5. Hand gesture recognition using electrical impedance tomography
  • 27.6. Temperature monitoring in the stored grain using acoustic tomography
  • 27.7. Tree decay detection by acoustic tomography
  • 27.8. Concrete defect detection by acoustic tomography
  • 27.9. Temperature monitoring using single light field camera
  • 27.10. Conclusion
  • Index

Product details

  • No. of pages: 922
  • Language: English
  • Copyright: © Woodhead Publishing 2022
  • Published: May 6, 2022
  • Imprint: Woodhead Publishing
  • eBook ISBN: 9780128233078
  • Paperback ISBN: 9780128230152

About the Editor

M Wang

Mi Wang has been a Professor of Process Tomography and Sensing at the University of Leeds, UK, since 2007. He is also a guest editor of Measurement Science and Technology and Flow Measurement and Instrumentation, and the editor of Petroleum and Hydrodynamics. He has previously been a guest Professor at the Institute of Mechanics of the Chinese Academy of Sciences, China, and a visiting Professor at Nihon University, Japan, where he was awarded the Outstanding Contribution Medal in 2007.

Affiliations and Expertise

Professor of Process Tomography and Sensing, University of Leeds, UK

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