Industrial Tomography: Systems and Applications

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. 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