Industrial Tomography: Systems and Applications

Industrial Tomography: Systems and Applications
Copyright
Introduction
Understanding the basics of sensor design and reconstruction
Optimizing data collection and analysis for industrial information
A compendium of applications examples
List of contributors
Preface
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
Acknowledgment
References
2. Electrical impedance tomography
2.1 Introduction
2.2 Fundamentals of measurement
2.2.1 Electrode–electrolyte interface modeling
2.2.2 Lead theorem
2.2.3 Reciprocity theorem
2.2.4 4-Electrode method
2.3 Principle of electrical impedance tomography sensing
2.3.1 Sensing strategies
2.3.2 Sensitivity
2.3.3 Graphic estimation
2.3.4 Typical sensor
2.4 Data acquisition
2.4.1 Signal sources
2.4.2 Sensor electronics and demodulation
2.4.3 Data acquisition systems
2.5 Image reconstruction
2.5.1 Inverse problem
2.5.2 Sensitivity coefficient back-projection method
2.5.3 Multistep methods
2.5.4 One-step methods
2.6 Imaging capability
2.7 EIT data for process application
2.7.1 Concentration distribution
2.7.2 Velocity distribution
2.8 Future trends
2.9 Sources of further information
References
3. Electromagnetic induction tomography
3.1 Introduction
3.2 Principle of operation and governing equations
3.3 Solution to the forward problem
3.3.1 Linear approximation
3.3.2 Analytical approach
3.3.3 Finite element method
3.3.4 Boundary element method
3.3.5 Full wave solutions
3.3.6 Impedance method
3.3.7 Dipole approximation
3.4 Solution to the inverse problem
3.4.1 Linear case
3.4.2 Iterative solutions
3.4.3 Sensitivity formulations
3.4.4 Neural inversion
3.5 System hardware
3.5.1 System configurations
3.5.2 Practical considerations
3.5.2.1 Coil design
3.5.2.2 Electrostatic screening
3.5.2.3 Resonance
3.6 Applications
3.6.1 Hot metal processing
3.6.2 Conductivity profiling
3.6.3 Biomedical
3.6.4 Security
3.7 Conclusions and outlook
References
4. Magnetic resonance imaging
4.1 Introduction to MRI and NMR
4.2 MRI: basic imaging principles
4.2.1 The precessing magnetization vector
4.2.2 Magnetic field gradients and imaging
4.2.3 Frequency and phase encoding
4.2.4 Simple 1D and 2D imaging sequences
4.2.5 Slice selection
4.2.6 Signal relaxation
4.2.6.1 The spin echo
4.2.7 Flow and diffusion
4.2.7.1 Time-of-flight velocity imaging
4.2.7.2 Phase-shift velocity imaging
4.2.7.3 Diffusion
4.3 Methods: basic imaging techniques
4.3.1 Gradient echo and spin echo imaging sequences
4.3.2 Signal relaxation measurement
4.3.3 Velocity imaging
4.4 Advanced data acquisition: fast imaging approaches
4.4.1 Full k-space fast sampling
4.4.1.1 Echo planar techniques
4.4.1.2 RARE
4.4.1.3 FLASH
4.4.2 Undersampled data reconstruction: compressed sensing, bayesian, and deep leaning methods
4.4.2.1 Compressed sensing
4.4.3 Practicalities and limitations
4.5 Applications in engineering
4.5.1 Nonspatially resolved measurement
4.5.2 MRI and time-of-flight
4.5.3 Phase-shift velocity imaging
4.6 Future trends
4.7 Conclusions
4.8 Sources of further information and advice
References
5. Chemical Species Tomography
5.1 Introduction
5.2 Absorption spectroscopy for Chemical Species Tomography
5.3 Image reconstruction for low beam count systems
5.3.1 Reconstruction as an ill-posed inverse problem
5.3.2 Reconstruction algorithms
5.3.3 Reconstruction accuracy and resolution
5.3.4 Spectroscopic aspects of reconstruction in Chemical Species Tomography
5.4 Beam array design and optimization
5.4.1 Case study: practical beam array design for target spatial resolution
5.5 Design of Chemical Species Tomography systems
5.5.1 Optical propagation
5.5.2 Optics
5.5.3 Optoelectronics and data acquisition
5.5.3.1 Light sources
5.5.3.2 Optical fiber components
5.5.3.3 Detection
5.5.3.4 Data acquisition
5.6 Case studies
5.6.1 Case study 1: automotive in-cylinder hydrocarbon imaging
5.6.2 Lab swirl flame
5.6.3 Wind tunnel flow
5.6.4 Turbine exhaust imaging
5.6.5 Pulverized coal combustion
5.7 Future trends
References
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
References
7. Radioisotope tracer techniques
7.1 Nuclear medicine imaging
7.1.1 Single photon emission computed tomography
7.1.2 Positron emission tomography
7.2 Industrial applications
7.2.1 Single photon imaging
7.2.2 Positron emission tomography
7.3 Particle tracking
7.3.1 Radioactive particle tracking
7.3.2 Positron emission particle tracking
References
8. Ultrasound tomography
8.1 Introduction
8.2 Ultrasound theory
8.2.1 Acoustic propagation wave theory
8.2.2 Acoustic impedance
8.2.3 Speed of sound
8.2.4 What can ultrasound measure in industrial processes?
8.2.4.1 Wave propagation in a single phase
8.2.4.2 What happens at an interface in a multiphase system
8.2.5 Limitations on ultrasound systems
8.3 Equipment and techniques
8.3.1 Equipment setups
8.3.1.1 Transducers
8.3.1.2 Pulser, receiver, and signal processing
8.3.2 Different types of systems
8.3.3 Image reconstruction
8.3.4 Ultrasound tomography linked with additional techniques
8.3.5 Calculating system resolution
8.3.5.1 Spatial resolution
8.3.5.2 Temporal resolution
8.4 Industrial applications
8.4.1 Characterization of single phase systems
8.4.2 Characterization of multiphase flows
8.5 Summary
8.6 Future trends
8.7 Source of further information and advice
References
9. Spectro-tomography
9.1 Introduction
9.2 Multidimensional process sensing
9.3 Spectroscopic sensing
9.4 Spectro-tomography principles
9.4.1 Simple process model
9.4.2 Process component identification
9.5 Spectro-tomography system implementation
9.5.1 System requirements
9.5.2 Process excitation
9.5.3 Response processing
9.5.4 Data fusion processing
9.5.5 Data capture and processing architecture
9.5.6 Implementation design
9.6 Trial demonstrations
9.6.1 Basic wideband excitation and response extraction
9.6.2 Energy frequency tomograph set processing
9.6.3 Overall processing with interpretation
9.6.4 General system design conclusions
9.7 Future trends for spectro-tomography
9.7.1 System development trends
9.7.2 Industrial application trends
References
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
References
11. Mathematical concepts for image reconstruction in tomography
11.1 Introduction
11.2 Transmission tomography
11.2.1 Mathematical formulation of transmission tomography
11.2.2 Radon transform and direct back-projection
11.2.2.1 Direct back-projection
11.2.3 Fourier transform and filtered back-projection
11.2.4 Algebraic reconstruction technique
11.2.5 Maximum likelihood expectation maximization for transmission tomography
11.2.5.1 Gradient type algorithm for transmission tomography
11.2.5.2 Maximum likelihood expectation maximization algorithm using prior models
11.3 Electrical tomography
11.3.1 Mathematical formulation of electrical tomography
11.3.2 Image reconstruction based on transmission tomography algorithms
11.3.3 Linear algorithms
11.3.4 Gradient-based algorithms
11.3.5 Dynamic algorithms
11.4 Diffraction tomography
11.4.1 Mathematical formulation of diffraction tomography
11.4.2 Born and Rytov approximations for weakly scattered objects
11.4.3 Fourier diffraction theorem
11.4.4 Image reconstruction methods for weakly scattering objects
11.4.5 Image reconstruction methods for highly scattering objects
11.5 Future trends
11.6 Source of further information
References
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
12.3.1 Calderon's method in a circular ET sensor
12.3.2 Iterative Calderon's method based on the closed-loop control
12.3.3 Dbar method in a circular ET sensor
12.3.4 Factorization method in a circular ET sensor
12.3.5 Direct methods implementation in a noncircular ET sensor
12.4 Dirichlet-to-Neumann/Neumann-to-Dirichlet maps
12.4.1 Construction of the Dirichlet-to-Neumann map
12.4.2 Construction of the Neumann-to-Dirichlet map
12.4.3 Fast calculation of the Dirichlet-to-Neumann map from the stiffness matrix in the finite electrode model
12.5 Calculation of the scattering transforms
12.5.1 Approximation of t(k) using texp
12.5.2 Approximation of t(k) using tb(k)
12.6 Applications in image reconstruction
12.6.1 Numerical cases
12.6.2 Static phantoms
12.7 Dynamic flame monitoring
12.8 Future trends
12.9 Further information
References
13. Machine learning process information from tomography data
13.1 Introduction
13.2 Machine learning methods for information needs
13.2.1 Outline of neural computing
13.2.2 Artificial neural network—perceptron
13.2.3 Artificial network training
13.3 Artificial neural networks for IPT applications
13.3.1 ECT example
13.3.2 ANN versus linear back-projection
13.3.3 Multilayer example
13.4 Case study A—estimating multiphase flow parameters
13.4.1 Overview and measurement requirements
13.4.2 ECT sensor and simulated ECT measurements
13.4.3 Network design and training for flow estimation
13.4.4 Network trials
13.4.5 Evaluation of ANN performance of flow component sensing
13.5 Case study B—estimating inline rheology properties of product flow
13.5.1 Overview and measurement requirements
13.5.2 Sensing and processing system
13.5.3 Machine learning approach
13.5.4 Network trials
13.5.5 Evaluation of ML performance for rheology sensing
13.6 Future trends
13.6.1 Deep learning futures for ANNs for IPT
13.6.2 Practical future steps forward in machine learning for IPT
13.6.3 Predicting the future of ML in process sensing
References
Further reading
14. Advanced electrical tomography visualisation
14.1 Introduction
14.2 Background
14.2.1 Multiphase flow patterns
14.2.2 Multiphase flow visualisation technologies
14.3 Advanced visualization of multiphase flow
14.3.1 Three-dimensional volume rendering
14.3.2 Isosurface
14.3.3 Bubble reconstruction
14.4 Multidimensional data fusion
14.4.1 Two-phase flow
14.4.2 Three-phase flow
14.5 Future trends
References
15. Applications of electrical resistance tomography to chemical engineering
15.1 Introduction
15.2 Applications of ERT
15.2.1 Mixing investigations
15.2.1.1 Mixing quality and mixing time/index investigations
15.2.1.2 Mixer and baffle characteristics investigations
15.2.1.3 3-Dimensional mixing visualizations
15.2.2 Flow investigations
15.2.2.1 Flow and phase distribution visualizations
15.2.2.2 Velocity and flow profile measurement investigations
15.2.3 Phase holdup investigations
15.2.4 Solid particles suspension, dissolution, and precipitation
15.2.5 Monitoring separation and phase boundaries
15.2.6 Concentration monitoring
15.2.7 Cleaning-in-place
15.2.8 Malfunction detection
15.2.9 Process control
15.3 Conclusions
References
16. From process understanding to process control—Applications in industry
16.1 Introduction
16.1.1 Process instrumentation levels
16.1.2 Distinctiveness of process tomography in industrial applications
16.2 Applications to improve process understanding
16.2.1 Case study 1 - monitoring rocket-motor combustion
16.2.2 Case study 2 - smart tanks for space
16.3 Process modeling and optimization
16.3.1 Measurement objective
16.3.2 Design considerations
16.3.3 Implementation
16.4 Process analytics
16.4.1 Vortex finder—monitoring of semicontinuous crystallization
16.4.1.1 Process
16.4.1.2 Measurement objective
16.4.1.3 Implementation
16.4.2 Rheology
16.4.2.1 Development of electrical resistance rheometry
16.4.2.2 Velocity profile
16.4.2.3 Rheology
16.5 Process monitoring for process control
16.5.1 Dense phase hydraulic conveying
16.5.1.1 Sensor
16.5.1.2 Electronics
16.5.1.3 Software
16.5.1.4 Performance
16.5.2 Wider application of dense phase hydraulic conveying
16.6 Conclusions and future trends
Thanks and Acknowledgments
References
17. Applications of tomography in oil–gas industry—Part 1
17.1 Introduction
17.2 Seismic tomography in hydrocarbon exploration and reservoir characterization
17.2.1 Basics of seismic waves
17.2.2 Traveltime tomography
17.2.3 Seismic depth imaging—stacking and migration
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
Acknowledgments
References
18. Applications of tomography in oil–gas industry—Part 2
18.1 Introduction
18.2 Cross-well electromagnetic tomography in hydrocarbon reservoir monitoring
18.2.1 Principles of EM induction cross-well tomography
18.2.2 EM cross-well tomography case 1: imaging fluid flow on a reservoir scale
18.2.3 EM cross-well tomography case 2: water flood monitoring
18.2.4 EM cross-well tomography case 3: imaging steam fronts
18.3 Potential of tomography in hydrocarbon production monitoring
18.3.1 Case study: ECT for multiphase flow WLR and liquid fraction measurement
18.4 Future trends
18.5 Source of further information and advice
Acknowledgments
References
19. Applications of tomography in multiphase transportation
19.1 Introduction
19.2 Flow pattern and flow pattern identification with IPT
19.2.1 Flow patterns in multiphase transportation
19.2.2 Flow pattern identification with IPTs
19.3 Multiphase transportation process measurement and monitoring with IPTs
19.3.1 Phase fraction measurement
19.3.2 Flow velocity measurement
19.3.3 Flow process analysis and characterization
19.4 IPT in multiphase flow measurement with multisensor fusion
19.4.1 Multimodality IPTs
19.4.2 IPTs combined with other sensors
19.5 Conclusions and future trends
Acknowledgment
References
20. Measurement and characterization of slurry flow using Electrical Resistance Tomography
20.1 Introduction
20.2 Physical mechanisms governing hydraulic transport of solid particles
20.2.1 Slurry flow pattern
20.3 Slurry flow characterization with Electrical Resistance Tomography
20.3.1 Solids volume fraction measurement
20.3.2 Solids axial velocity measurement
20.3.3 Solids flow monitoring and visualization
20.3.4 Characterization of flow patterns and analysis of stratified slurry flow by ERT
20.4 Limitations of ERT in slurry flow measurement and characterization
20.5 Conclusions and future trends
20.6 Sources of further information
References
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
References
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
22.4.1 Fluid bed characterization using computer tomography
22.4.2 Fluid bed characterization by X-ray fluoroscopy and pressure measurements
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
Acknowledgments
References
23. Applications of tomography in bubble column and fixed bed reactors
23.1 Introduction
23.2 Bubble column reactors
23.2.1 Fluid phase fraction measurements
23.2.2 Fluid velocity measurements
23.3 Fixed bed reactors
23.3.1 Hydrodynamic studies
23.3.2 Reaction and mass transfer studies
23.3.3 Complex porous structures
23.4 Future trends
23.5 Sources of further information
References
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
24.5.1 Velocity measurement methods–based cross-correlation technique
24.5.2 Spatial cross-correlation for flow angular velocity calculation
24.5.3 Process modeling
24.5.4 Gravity swirl-drop measurement
24.6 Toward to improvement of process measurement
24.6.1 Method for improving the accuracy of angular velocity determination
24.6.2 Phantom validation
24.6.3 Real case study
24.7 Future trends
References
Further reading
25. Applications of electrical capacitance tomography in industrial systems
25.1 Introduction
25.1.1 Basic requirements for ECT system
25.1.1.1 Sensor
25.1.1.2 Data acquisition system
25.1.1.3 Software
25.1.2 Fundamental advantages of ECT in industrial application
25.1.2.1 Passive sensor
25.1.2.2 Noninvasive
25.1.2.3 Low power
25.1.2.4 High speed
25.1.2.5 Cost effective
25.1.2.6 Compact
25.1.3 Adapting ECT for industrial applications
25.2 Two-phase gas–solid systems
25.2.1 Fluidized bed reactor
25.2.1.1 FB background
25.2.1.2 Tomography techniques for FB
25.2.1.3 ECT verification in FB
25.2.1.4 ECT application in circulating FB
25.2.2 High temperature FB
25.3 Two-phase air–water systems
25.4 Three-phase systems
25.4.1 Trickle bed reactor
25.5 Future trends
25.6 Source of further information
References
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
26.3.1 Multiphase rig for two-phase (air/water) flow
26.3.2 Particulate flow
26.3.3 AI techniques in identifying flow regimes in multiphase flow
26.4 AI techniques relevant for process control
26.4.1 Results from AI techniques using fuzzy logic and fuzzy neural network
26.4.2 Results from AI techniques using neural networks—LSTM
26.4.3 Results from AI techniques using support vector machines—fluidized bed columns
26.5 Possibilities for AI-assisted control
26.6 Future trends
26.7 Sources of further information
Acknowledgments
References
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
References
Index
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
R
S
T
U
V
W
X
Z