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FLOW VISUALISATION FOR GAS SOLID MEASUREMENT USING OPTICAL
TOMOGRAPHY FAN BEAM PROJECTION
MOHD FADZLI B ABD SHAIB
UNIVERSITI TEKNOLOGI MALAYSIA
FLOW VISUALISATION FOR GAS SOLID MEASUREMENT USING OPTICAL
TOMOGRAPHY FAN BEAM PROJECTION
MOHD FADZLI B ABD SHAIB
A thesis submitted in fulfilment of the
requirements for the award of the degree of
Master of Engineering (Electrical)
Faculty of Electrical Engineering
Universiti Teknologi Malaysia
SEPTEMBER 2014
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In the name of Allah, Most Gracious, Most Merciful
To my beloved and supportive parent, brothers and sisters
To my beloved wife & children
iv
ACKNOWLEDGEMENT
To Allah the most gracious, most merciful, the all praise worthy. I would like
to express my great appreciation to my respectful supervisor, Prof. Dr. Ruzairi Hj.
Abdul Rahim. Without his valuable guidance, motivation and constant endeavour
throughout the study, this research would not have been successful.
My special gratitude goes to my colleague Dr Zarina Bt Mohd Muji, Dr
Razali B Tomari, and Mr Ariffuddin B Joret for providing technical guidance and
helpful suggestions that helped me to conduct this research.
Thank you to my parents for their unconditional support. Finally, but most
importantly, I wish to thank my wife Siti Fatimah Bt Jamalludin and my children
Muhammad Mikhail Iman B Mohd Fadzli and Nur Qaisara Iman Bt Mohd Fadzli and
Nurlisa Qistina Iman Bt Mohd Fadzli for their full support, love and understanding
throughout this wonderful journey.
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ABSTRACT
In granules manufacturing industry, a real time monitoring is vital to observe
the distribution of solid and gas mixture in pipelines. For solid and gas mixture such
as pharmaceutical and grain production, the tiny pills and grains are poured through
industrial chutes and silos in mass quantities. Nevertheless, the uncontrolled large
scale flow can cause blockage in the pipeline and consequently can cause severe
limited production efficiency. To determine the blockage area as well as its size,
various flow meters are available in the market. However, most of the flow meters
are intrusive and invasive; therefore can disrupt material flow. The optical
tomography system technique is one of the methods to be adopted because of the
ability of the system to observe material flow non-intrusively, hence determine the
affected blockage area. In this research, alternate arrangements of 16 pairs of optical
sensors which consist of transmitters and receivers have been mounted on a 10cm
acrylic pipeline. Since the fan beam projection technique has been used, infrared
Light Emitting Diode (LED) and photodiode with greater angle of projection and
response were chosen. A specially designed jig has been developed for sensor
positioning to ensure they are exactly on the periphery of the pipeline. Most previous
researchers utilised digital timing and Data Acquisition System (DAS) units to
control the projection and receiving unit of the optical tomography system. In this
research, a circuit integrated with a dsPIC30F6014A microcontroller has been
designed for controlling the projection of light by transmitters and the receiving
signal of receivers. To operate the dsPIC30F6014A microcontroller together with the
designed circuit, C programming language via MicroC compiler is applied. For
image reconstruction, Linear Back Projection (LBP) has been applied via Visual
Basic 6. Different flow regimes have been tested and analysed thoroughly to observe
the overall performance of the system. The results obtained show that the optical
tomography system developed is capable of observing multiple flows with different
flow regimes; hence successfully determine blockage area of the solid gas flow.
Apparently, the proposed single dsPIC30F6014A microcontroller usage indicates its
ability to control acquisition process effectively with 480 µs sampling time rate.
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ABSTRAK
Dalam industri pembuatan bijirin, pemantauan masa nyata adalah penting
bagi memerhatikan proses percampuran pepejal dan gas dalam paip. Untuk campuran
pepejal dan gas seperti farmaseutikal dan pengeluaran bijirin, pil-pil yang kecil dan
bijirin di tuangkan melalui pelongsor industri dan silo dalam kuantiti yang banyak.
Walau bagaimanapun, aliran dalam skala besar tidak terkawal boleh menyebabkan
saluran paip tersumbat dan seterusnya menghadkan pengeluaran bahan efisien. Bagi
mengenalpasti kawasan tersumbat dan saiz, pelbagai meter aliran boleh didapati
dalam pasaran. Walau bagaimanapun, kebanyakan meter aliran mengganggu
pengaliran dan bersifat invasif; boleh mengganggu pengaliran bahan. Teknik sistem
tomografi optik adalah salah satu kaedah yang boleh digunapakai kerana keupayaan
sistem untuk melihat aliran bahan tanpa mengganggu pengaliran, dengan itu dapat
mengenalpasti kawasan tersumbat terlibat. Dalam kajian ini, 16 pasang sensor optik
terdiri daripada pemancar dan penerima telah dipasang pada paip akrilik diameter 10
cm. Memandangkan teknik unjuran berbentuk kipas digunakan, Diod Pemancar
Cahaya (LED) radiasi infra merah dan fotodiod dengan sudut lebih besar telah
dipilih. Jig direka khas telah dibangunkan bagi memastikan kedudukan sensor
berada disekeliling paip. Kebanyakan penyelidik sebelum ini menggunakan litar
masa digital dan Sistem Pemerolehan Data (DAS) untuk mengawal unit unjuran dan
penerimaan sistem tomografi optik. Dalam kajian ini, penggabungan litar bersama
dsPIC30F6014A mikropengawal telah direka untuk mengawal unjuran cahaya untuk
pemancar dan penerimaan isyarat untuk penerima. Bagi pengoperasian
dsPIC30F6014A mikropengawal dan litar yang direka, bahasa pengaturcaraan C
melalui pengkompil MicroC digunakan. Untuk pembinaan semula imej, Unjuran
Kembali Linear (LBP) telah digunakan menggunakan Visual Basic 6. Pelbagai
model aliran telah diuji dan dianalisis dengan teliti untuk melihat prestasi
keseluruhan sistem. Dari keputusan yang diperolehi, sistem tomografi optik mampu
digunakan untuk melihat pelbagai aliran di kawasan berbeza, seterusnya dapat
menentukan kawasan yang tersumbat. Secara jelasnya, penggunaan mikropengawal
dsPIC30F6014A tunggal menunjukkan keupayaan bagi pengambilan data dengan
kadar masa 480 µs bagi persampelan data.
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TABLE OF CONTENTS
CHAPTER TITLE PAGE
DECLARATION ii
DEDICATION iii
ACKNOWLEDGEMENT iv
ABSTRACT v
ABSTRAK vi
TABLE OF CONTENTS vii
LIST OF TABLES xii
LIST OF FIGURES xiv
LIST OF SYMBOLS xvii
LIST OF ABBREVIATIONS xviii
LIST OF APPENDICES xx
1 INTRODUCTION 1
1.1 Background Problem 2
1.2 Problem Statements 3
1.3 Importance of Study 5
1.4 Research Objectives 6
1.4.1 Specific Objectives 6
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1.5 Research Scopes 6
1.6 Organisation of the Thesis 7
2 LITERATURE REVIEW 9
2.1 Introduction to Process Tomography 9
2.2 Types of Tomography Sensors 10
2.2.1 X-Ray Tomography 10
2.2.2 Electrical Capacitance Tomography (ECT) 11
2.2.3 Electrical Impedance Tomography (EIT) 12
2.2.4 Magnetic Induction Tomography (MIT) 13
2.2.5 Ultrasonic Tomography 14
2.2.6 Optical Tomography 16
2.3 Type of Projections 17
2.4 Research in Optical Tomography 18
2.5 Image Reconstruction Algorithm 26
2.6 Summary 28
3 HARDWARE DEVELOPMENT 29
3.1 Introduction 29
3.2 Sensors Selection 30
3.2.1 Transmitter Selection 32
3.2.2 Receiver Selection 34
3.3 Verification of Coverage Area for Transmitters and
Receivers 36
3.3.1 Experimental Set-Up for Determination of Sensor
Coverage 36
3.4 Arrangement of Sensors for Transmitters and Receivers 40
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3.5 Circuit Design for Transmitters and Receivers of
Optical Tomography 41
3.6 Printed Circuit Board (PCB) and Fixture Arrangement 44
3.7 Summary 45
4 SOFTWARE DEVELOPMENT 47
4.1 Introduction 47
4.2 Data Acquisition by means of Microcontroller
dsPIC30F6014A 48
4.3 Graphical User Interface (GUI) for Simulation
and Image Reconstruction 52
4.4 Sensitivity Maps for Optical Tomography System 56
4.5 Image Reconstruction via Linear Back Projection (LBP)
Technique 59
4.6 Filtration via Filtered Back Projection Technique (FBP) 62
4.7 Viewing Concentration Profile of Solid-Gas Flow 63
4.8 Graphical User Interface (GUI) for Data Analysis 65
4.9 Summary 66
5 RESULT ANALYSIS 67
5.1 Introduction 67
5.2 Important Parameters for Analysis 67
5.2.1 Means Square Error (MSE) 68
5.2.2 Peak Signal to Noise Ratio (PSNR) 68
5.2.3 Percentage Area Error 69
5.2.4 Concentration of Solid 70
5.3 Simulation Analysis for Various Types of Flow 70
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5.3.1 Simulation for Full-Flow Model 70
5.3.2 Simulation for Half-Flow Model 71
5.3.3 Simulation for Quarter-Flow Model 73
5.3.4 Simulation for Middle-Circular Flow Model 74
5.3.5 Simulation for Two Different Locations of
Round-Flow Model 75
5.3.6 Simulation for Square Flow Model 76
5.4 Comparing the Image Quality for Different Types of Flow 77
5.5 Analysis of Image Obtained from Developed Optical
Tomography System 81
5.5.1 Full-Flow Model Image Obtained from
Hardware of Optical Tomography System 82
5.5.2 Half-Flow Model Image Obtained from
hardware of Optical Tomography System 83
5.5.3 Quarter-Flow Model Image Obtained from
Hardware of Optical Tomography System 87
5.5.4 Middle-Circular Flow Model Image Obtained from
Hardware of Optical Tomography System 88
5.5.5 Two Different Locations of Round Model Image
Obtained from Hardware of Optical Tomography
System 93
5.5.6 Polygon Shape Detection (Square Shape) from
Hardware of Optical Tomography System 96
5.5.7 Polygon Shape Detection (Triangle Shape) from
Hardware of Optical Tomography System 99
5.6 Analysis of Overall Performance of the System Based on
Different Types of Image Flow 102
5.7 Analysis of the Image Obtained from Rice Flow
Experiment from Developed Optical Tomography
System 106
5.8 Summary 107
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6 CONCLUSIONS AND RECOMMENDATIONS 110
6.1 Conclusions 110
6.2 Research Contribution 111
6.3 Recommendation for future works 112
REFERENCES 114
Appendices A - G 119 - 138
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LIST OF TABLES
TABLE NO. TITLE PAGE
3.1 Different models of infrared LED 32
3.2 Different models of photodiode 34
4.1 List of input/output ports for dsPIC30F6014A 49
4.2 List of input ports used for activation of transmitters 49
4.3 List of ADC ports for dsPIC30F6014A 50
5.1 Simulation analysis for full-flow model 71
5.2 Simulation analysis for half-flow model 72
5.3 Simulation for quarter-flow model 73
5.4 Simulation for middle-circular flow model 75
5.5 Simulation for two different locations of round-flow model 76
5.6 Simulation for square-flow model 77
5.7 Image of full-flow model 82
5.8 Image for half-flow (black flow) model 84
5.9 Image for half-flow (white flow) model 86
5.10 Image for quarter-flow (black flow) model 88
5.11 Image for quarter-flow (white flow model) 89
5.12 Image for middle-circular flow (black flow) model 91
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5.13 Image for middle-circular flow (white flow) model 92
5.14 Image for two different locations of
round-flow (black flow) model 94
5.15 Image for two different locations of
round-flow (white flow) model 95
5.16 Image for square-flow (black flow) model 96
5.17 Image for square-flow (white flow) model 98
5.18 Image for triangle-flow (black flow) model 100
5.19 Image for triangle-flow (white flow) model 101
5.20 Image for Rice Full Flow Experiment 107
5.21 Image for Rice Half Flow Experiment 108
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LIST OF FIGURES
FIGURE NO. TITLE PAGE
2.1 Basic tomography system 10
2.2 Cross-sectional view of ECT sensor with 12 electrodes 12
2.3 Concept of EIT systems 13
2.4 MIT system topology 14
2.5 Concept of Ultrasonic tomography 15
2.6 Parallel projection (a) general concept
(b) tomography application 17
2.7 Fan beam projection (a) general concept
(b) tomography application 18
2.8 Parallel configuration for optical tomography 19
2.9 Configuration of two orthogonal and two rectilinear
projections 20
2.10 Optical stopper and alternate arrangement of Tx and Rx 21
2.11 Combination of rectilinear and orthogonal projection
on the same plane 22
2.12 Experiment set up for study of the flames concentrations 23
2.13 Fan Beam Arrangement (a) 4 Light sources, 15 beams;
(b) 15 Light sources, 5 beams; (c) 15 Light sources, 15 beams 23
2.14 Fan Beam arrangement with 32 pairs of infrared emitters
and photodiode receivers 24
2.15 Combination of parallel and fan beam projection 25
3.1 Topology for optical tomography system 30
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3.2 Electromagnetic wavelength 31
3.3 Spectral range for OSRAM-SFH 485P &
OSRAM-SFH484-2 33
3.4 Spectral range for OSRAM-SFH203PFA &
OSRAM-SFH213-FA 35
3.5 Diagram of angle for transmitter and receiver’s IR LED 37
3.6 Graph showing signal from transmitter and receiver 38
3.7 Graph showing receiver’s response for radius of 5cm 39
3.8 Graph showing receiver’s response for radius of 10 cm 39
3.9 Arrangement of Tx and Rx around the acrylic cylinder 40
3.10 Light projection circuit 41
3.11 Signal conditioning circuit 43
3.12 New design for sensor jig, (a) single jig design,
(b) jig embedded in acrylic cylinder 44
3.13 Integration of PCB and optical tomography jig, (a) front view,
(b) upper view 45
4.1 Input/output port arrangements for dsPIC30F6014A 48
4.2 Flowchart of port usage for data acquisition process 51
4.3 Representation of GUI for optical tomography system 52
4.4 Tomogram after filtration process 52
4.5 Array set of pixel value based on reading of sensors
from the hardware of optical tomography system 54
4.6 Flowchart for communication between VB6 and
dsPIC30F6014A microcontroller 55
4.7 Projection paths for Tx2 and Rx7 57
4.8 Total Projection paths for Tx0 and Rx0 until Rx15 58
4.9 LPBA and fan beam projection, (a) general concept,
(b) projection beam summation 59
4.10 Maximum voltage received by receiver 60
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4.11 Attenuated voltage received by receiver 61
4.12 Flowchart for image reconstruction 64
4.13 GUI developed via MATLAB for analyzed of the system 65
5.1 Comparison of material distribution profile between
simulation and real image 78
5.2 Comparison of area error between simulation and real image 79
5.3 Comparison of MSE between simulation and real image 80
5.4 Comparison of PSNR between simulation and real image 80
5.5 Static flow experiment set up 81
5.6 Material distributions for different types of flow 103
5.7 Area errors for different types of flow 104
5.8 MSE for different types of flow 105
5.9 PSNR for different types of flow 105
5.10 Rice Flow Experiment, (a) Full flow, (b) Half flow 106
xvii
LIST OF SYMBOLS
V - Voltage
Ω - Ohm
3D - Three Dimension
dB - Decibel
s - Second
k - Kilo
M - Mega
mm - Milimeter
nm - Nano Meter
Av - Gain
V+ - Non-inverting Input
V- - Inverting Input
Vin - Input voltage at the receiver
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LIST OF ABBREVIATIONS
ADC - Analogue to Digital Conversion
AGC - Averaging Group Colour
CT - Computed Tomography
DAQ - Data Acquisition
DAS - Data Acquisition System
ECT - Electrical Capacitance Tomography
EIT - Electrical Impedance Tomography
FBP - Filtered Back Projection
GND - Ground
GUI - Graphical User Interfaces
Hz - Hertz
I2C - Inter-Integrated Circuit
IC - Integrated Circuit
IR - Infrared Light Emitting Diode
kHz - Kilo Hertz
LBP - Linear Back Projection
LBPA - Linear Back Projection Algorithm
LED - Light Emitting Diode
MHz - Mega Hertz
xix
MIT - Magnetic Induction Tomography
MSE - Mean Square Error
OP-AMP - Operational Amplifier
PC - Personal Computer
PCB - Printed Circuit Board
PCI - Peripheral Component Interconnect
PIC - Peripheral Interface Controller
PSNR - Peak Signal To Noise Ratio
PVC - Poly(vinyl chloride)
Rx - Receiver
SIE - Space Image Evaluating
Tx - Transmitter
UART - Universal Asynchronous Receiver/Transmitter
USB - Universal Serial Bus
VB - Visual Basic
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LIST OF APPENDICES
APPENDIX TITLE PAGE
A Visitation Letter to “Faiza Beras Sdn Bhd” 118
B Datasheet of SHF485P 119
C Datasheet of SFH203PFA 121
D Datasheet of dsPIC30F6014A 123
E MicroC Compiler Programming 124
F VB6 Programming for Image Reconstruction 128
G Matlab Programing 135
H List of Publications and Awards 137
CHAPTER 1
INTRODUCTION
The word tomography derives from the Greek and means a cut picture or
image. From an engineering perspective, tomography is about obtaining information
or data on the internal structure of an object without the need to invade or disrupt
material flow.
Even though the tomography field is considered as mature technology and
offers only low-resolution imaging, it remains popular thanks to its ability to
penetrate the internal structure of the object without the need to slice the object.
Tomography was first used for medical examination purposes, and gradually its
industrial application occurred where online monitoring is concerned (Dyakowski et
al, 2000).
Several tomography sensors exist and they are divided into “hard field” and
“soft field” sensors. Hard field sensors are equally sensitive to parameters measured
in all positions throughout the measurement volume and its sensitivity is independent
of the distribution inside and outside the measurement region whereas with soft field
sensors the sensitivity of the measured parameter depends on position in the
measurement volume as well as on the distribution of parameters inside and outside
2
this region (Johansen et al, 1996). An example of a hard field sensor is the X-ray
(earliest technique) and an electrical capacitance tomography sensor (ECT) is an
example of soft field sensors.
1.1 Background Problem
Process tomography has begun to spread extensively in industrial field
research which uses different tomography techniques for monitoring flows of various
types of component mixture inside pipelines. Indeed, study of the flow of solid and
gas mixtures is vital and the tomography technique can improve the overall
performance of the industrial process. The important feature of process tomography
is its capability in terms of providing information for multiphase flow rates and
material distribution or concentration profile inside pipelines in real time.
An industrial tomography system must have significant characteristics such
as high speed of data acquisition, good responses (capable of online monitoring) and
low cost compared with the current flow meter industry. This is vital since most of
the material flow inside a pipeline moves at very high speed and requires very good
responses, especially particle flow in the food and chemical industries.
The right data acquisition system, besides very high speed, must also have
sufficient analogue input and digital output and be able to be integrated with
tomography sensors and computers. Most previous research has utilised a
combination of the PIC microcontroller or designed circuit and the data acquisition
system (DAQ) in developing a tomography system (Abdul Rahim, 2005; Zheng et al,
2008). DAQ cards have been used for interfacing the sensor device in computers for
better image reconstruction. Even though the DAQ card has often been selected by
researchers, it is not a good choice in terms of the cost-effectiveness of the whole
3
system. The price of DAQ cards ranges from RM 6 k to RM 20 k per unit (retail
value from official website National Instrument http://www.ni.com/data-
acquisition/), which is exorbitant. This is at odds with the original aim of producing a
sensor device using low-cost apparatus (Minagawa et al, 2012). Alternative methods
of data conversion should be considered. Muji (2012) used a different combination
of a peripheral interface controller (PIC) and I2C protocol to develop an optical
tomography system. This method is much better than the DAQ system in terms of
cost-effectiveness. The I2C protocol is needed for combination of several PICs. This
combination is required since a single PIC is unable to provide enough analogue
input and digital output for most tomography systems. Using the I2C protocol could
be intricate and complicated, however. Hence, an alternative microcontroller should
be chosen to fulfil the above-mentioned needs. In 2001, Microchip released a dsPIC
series of chips with a 16-bit microcontroller instead of an 8-bit for normal PIC. This
dsPIC can cater for large numbers of analogue and digital input/output ports, thus
eliminating the need for an I2C protocol.
1.2 Problem Statement
The production, processing and transport of particulate or granular
materials such as minerals, powders or cereals, is of immense industrial importance.
A pneumatic conveying system is a common process to transfer this bulk material
through an enclosed pipeline. However, in pneumatic conveying system often could
cause blockage due to uncontrolled large scale and condense flow of the material
inside the pipeline; hence could adversely affect the whole productivity. There are
several current flow meters available to detect material flow and identifying blockage
area inside the pipeline. However, most of this equipment is intrusive with exorbitant
price. The developed optical tomography is the cost effective option to observe and
identifying the blockage area without need to invade the material flow.
4
In gravitational driven flow of granular material, the pipeline used has its
minimum size of diameter so that conveying of the bulk material could flow easily.
In the same way, the developed optical tomography fixture should be the same size
with the diameter of the pipeline to capture the data without disrupts the material
flow. However, most of the develop optical tomography by previous researcher do
not meet the minimum size of the pipe line. Instead, the designed fixture is lower size
in diameter compared with the diameter of the pipeline used in industry. This will
limit the capability of the sensor observing the flow with larger size of the pipeline.
For construction of optical tomography system, most of previous researcher
utilizing the usage of DAQ card along with projection circuit for transmitting and
receiving signals. Besides, the combination of several microcontrollers is one of the
options for researchers in this area to manage the system with higher number of input
and output. However, both of this approach may not fulfill the main aims which are
to produce low cost and uncomplicated flow meter towards industrial needs.
Over the past several years, observing black material flows is always an
option for many of the researcher. However, the capability of this system to monitor
only one type of color range could restrain its application towards certain industries
only.
.
5
1.3 Importance of Study
In the chemical and food industries, several types of material or foods need to
be processed or produced in liquid solid form such as particles and granules. In terms
of solid forms, the material might be presented as raw material or final products.
Identifying the internal characteristics of solid flows along the conveying system is
essential for observation of the overall performance of the process flow. This can be
done by obtaining the concentration profile or distribution of particles inside the
pipeline. Most of the pipeline or conveying system of solid flow is opaque, and the
flow pattern cannot be observed with the naked eye. Hence, non-intrusive monitoring
flow is needed to observe solid flow in gas medium. This is important to avoid flow
disruption or collision between the material and the monitoring device.
Tomography is the most effective technology for observing the internal
characteristics of solid flow without interrupting the internal process flow. There are
several tomography sensing approaches but to date there is no specific online
monitoring system for monitoring solids in a pneumatic conveying pipeline (Zheng
et al, 2010).
The optical tomography system is one of the most popular techniques for
observing the solid flow inside the pipeline. Most research utilises a pipeline which
has a small diameter of 60mm as medium of solid and gas flow (Chan, 2002; Leong
et al, 2005). A personal visit to a rice manufacturing industry in Malaysia (Appendix
A) revealed, however, that the minimum diameter of the pipeline applied in this kind
of industry is actually 100mm. This suggests that the ability of optical sensors to
observe material flow should be examined in the case of pipelines with a bigger
diameter. The complexity of the overall circuit will also be simplified by the usage
of a dsPIC30F6014A microcontroller. Most of the manufacturing industries handle
material which is generally dark or bright in colour. The performance of the sensors
on different colours is thoroughly examined here to observe the compatibility of the
6
system. It is hoped that the result will help other researchers to contribute to the
development and application of optical tomography in real industry.
1.4 Research Objectives
The aim of this research is to develop an optical tomography system using fan
beam projection configuration and an online monitoring system for solid-flow
visualisation.
1.4.1 Specific Objectives
This researched aimed to meet the following objectives:
i. Design optical tomography measurement hardware
ii. Develop tomography software display
iii. Integrate the software and hardware for verification purposes
iv. Test and verify the ability of the system to observe different flow models with
black and white colours.
1.5 Research Scope
(i) Design of a Sensor Fixture
The important things when deciding on a sensor fixture are the sensors'
physical parameters, light projection angle, type of material suitable for
7
constructing a fixture that suits the desired system and integration of the
fixture with the designed circuit.
(ii) Sensor Selection
The type of sensors for transmitters and receivers needs to be decided. In this
optical tomography system, infrared (IR) LED is selected for the transmitters
and a photo-detector LED for the receivers.
(iii) Circuit design
Current for transmitter, amplifier circuit for receivers and microcontroller
unit for digital timing are required, plus data conversion from analogue to
digital form.
(iv) Software design for real-time image reconstruction
The software needed has two parts. The first part involves programming with
a MicroC compiler to give instructions to the microcontroller for activation or
deactivation of transmitters and data conversion at the receiver side. The
second part involves developing an algorithm for image reconstruction using
Visual Basic 6.
1.6 Organisation of Thesis
Chapter 1 presents an introduction to process tomography, the research’s
background problem, the problem statement, the importance of the study, the
research objectives and the scope of the study.
8
Chapter 2 lists several common types of tomographic techniques and the
general principles of optical tomography.
Chapter 3 explains optical tomography modelling and hardware design. This
includes optical sensor arrangement, mounting techniques, signal conditioning
circuits and data acquisition systems.
Chapter 4 details the software development for generating a pulse for
activation and deactivation of the transmitter, timing sequence, data acquisition and
image reconstruction.
Chapter 5 describes the image obtained from different types of flow models.
Comparisons of the concentration value of the different models are presented.
Chapter 6 concludes, mention state research contribution and suggests further
work to improve on the present study.
114
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