Information and Communication Technology Seminar, Vol. 1 No. 1, August 2005
ISSN 1858-1633 2005 ICTS 17
TOMOGRAPHIC IMAGING USING INFRA RED SENSORS
Sallehuddin Ibrahim Md. Amri Md. Yunus
Department of Control and Instrumentation Engineering Faculty of Electrical Engineering, Universiti Teknologi Malaysia
81310 UTM Skudai, Johor, Malaysia sallehfke.utm.my
ABSTRAK
This paper is concerned with the development of a tomographic imaging system in order to measure a
two-phase flow involving solid particles flowing in air. The general principle underlying a tomography
system is to attach a number of non-intrusive transducers in plane formation to the vessel to be
investigated and recover from those sensors an image of the corresponding cross section through the vessel.
The method made use of infra red sensors. The sensors were configured as a combination of two
orthogonal and two rectilinear light projection system. It has the ability to accurately visualize concentration
profiles in a pipeline. The imaging is carried out on- line without invading the fluids. The system used a
combination of two orthogonal and two rectilinear infra-red projections. The sensors were installed
around a vertical transparent flow pipe. Several results are presented in this paper showing the capability of
the system to visualize the concentration profiles of solids flowing in air.
Keywords: Infra red, imaging, tomography, sensor.
1. INTRODUCTION
Flow imaging is gaining importance in process industries. As such suitable systems must be
developed for this purpose. Flow measurements belong to the most difficult ones because the medium
being measured can occur in various physical states, which complicate the measuring procedure. They are,
namely, temperature, density, viscosity, pressure, multi-component media liquid-gas, solid-gas, the
type of flow, etc. The choice of the method is further directed by specific requirements for the flowmeter,
e.g. the measuring range, minimum loss of pressure, the shortest possible recovery section, a sensor
without moving parts, continuous operation of the sensor, etc.
Tomography methods have been developed rapidly for visualizing two-phase flow of various industrial
processes, e.g. gasoil flows in oil pipelines [1], gassolid flows in pneumatic conveyors [2], and
separationmixing processes in chemical vessels [3]. Infra red tomography involves the measurement of
light attenuation detected by various infra red sensors installed around a flow pipe, and the reconstruction of
cross-sectional images using the measured data and a suitable algorithm.
Tomography began to be considered seriously as it can be used to directly analyze the internal
characteristics of process flow so that resources will be utilized in a more efficient manner and in order to
meet the demand and regulations for product quality. The use of tomography to analyze the flow regime
began in the late 1980s. Besides, concern about environmental pollution enhanced the need to find
alternative methods of reducing industrial emission and waste. In those applications, the system must be
robust and does not disturb the flow in pipelines. It should be able to operate in aggressive and fast
moving fluids. This is where tomography can play an important role as it can unravel the complexities of
flow without invading it.
Tomography can be combined with the characteristics of infra red sensors to explore the
internal characteristics of a process flow. Infra red tomography is conceptually straightforward and
inexpensive. It has a dynamic response and can be more portable compared to other types of radiation-
based tomography system. The image reconstruction by an infra red tomography system should be directly
associated to visual images observed in transparent sections of the pipelines. It has other advantages such
as negligible response time relative to process variations, high resolution, and immunity to electrical
noise and interference. This paper will explain how such a system can be designed and constructed to
measure the distribution of solids in air flowing in a pipeline.
2. IMAGE RECONSTRUCTION
The projection of infra red beam from the emitter towards the detector can be illustrated mathematically
in Figure 1. The coordinate system can be utilized to describe line integrals and projections. The object of
interest is represented by a two-dimensional function fx,y and each line integral is represented by the Ø,
x’ parameters. Line AB can be expressed as
sin cos
x y
x =
+
φ φ
1 where
⎥ ⎦
⎤ ⎢
⎣ ⎡
⎥ ⎦
⎤ ⎢
⎣ ⎡
− =
⎥ ⎦
⎤ ⎢
⎣ ⎡
y x
y x
φ φ
φ φ
cos sin
sin cos
2 which resulted in the following line integral
Information and Communication Technology Seminar, Vol. 1 No. 1, August 2005
ISSN 1858-1633 2005 ICTS 18
∫
=
line x
dx y
x f
x p
,
,
φ φ
3 Using a delta function, this can be rewritten as
4 where
N = total number of horizontal cellspixel M = total number of vertical cellspixel
The algorithm for reconstruction is performed by
approximating the density at a point by summing all the ray sum of the ray through the point. This has been
termed the discrete back projection method and can be formulated mathematically as
5 5
Figure 1. Infra red path from emitter to detector
3. SYSTEM DESIGN
The measurement system composed of four subsystems: 1 sensor, 2 signal conditioning, 3
data acquisition system, and 4 image reconstruction and display.
Figure 2 depicts the measurement section around the flow pipe which contains 64 pairs of infra red
sensors configured in a combination of orthogonal and rectilinear projection. A similar number of sensors are
installed downstream. The output of the downstream sensors should be a replica of the output from the
upstream sensors but experienced a time delay. Both signals can be cross-correlated to obtain the velocity
of the flow. The flow pipe has an external diameter of 80 mm and an internal diameter of 78mm. Since the
infra red sensors are the critical part, the selection of infra red transmitters and receivers are considered
carefully. The sensors should be arranged such that they cover the whole pipe. In tomography, the more
number of sensors used means higher resolution is achieved. Another set of sensors were constructed
100mm downstream to measure velocity using the cross-correlation method. The sensors used must be of
high performance, compact, require minimum maintenance or calibration and be intrinsically safe.
For this purpose, the emitter SFH 4510 and detector SFH 2500 was chosen due to its low cost and fast
switching time.
The infra red emitters and detectors are arranged in pairs. They are linked to the measurement section
through optical fibers. Each receiver circuit consists of a photodiode, pre-amplification, amplification and a
filter. The receiver circuits are connected to a data acquisition card manufactured by Keithley. The card
is of the type DAS-1800. Light generated by the emitters passed to flow pipe and is attenuated if it hits
an object. The light reaches the receiver and is then converted by a photodiode into current by the
receiving circuit. The signal is processed by a signal conditioning circuit. Data then entered the data
acquisition system and is converted into a digital form prior to entering the computer. A linear back
projection algorithm was developed which processed the digitized signal and displayed the concentration
profile of the solids flowing in air. The algorithm is programmed in the Visual C++ language which is a
powerful tool for such purpose.
Figure 2. Tomographic measurement section
The solid particles consisting of plastic beads were dropped onto a gravity flow rig shown in Figure 3.
The rig costing about RM100,000 was supplied by Optosensor. The beads were filled into a hopper and a
rotary valve controls the amount of beads flowing into the rig. Thus user can set various flow rates. The
measurement section was installed around the flow rig.
∑ ∑
− =
− =
∆ ⎥
⎦ ⎤
⎢ ⎣
⎡ ∆
+ ∂
=
1 1
sin cos
,
M M
N N
y x
y x
y x
f x
p
φ φ
φ
y’
X’
x y
A x’
B
1
x p
φ
,
y x
f
°
φ
Projections
φ φ
φ φ
∆ ⎥
⎦ ⎤
⎢ ⎣
⎡ ∆
− +
∂ =
∑ ∑
− =
− =
1 1
sin cos
, 1
, ˆ
M m
N n
x x
y x
x p
M y
x f
Tomographic Imaging Using Infra Red Sensors – Sallehuddin Ibrahim Md. Amri Md. Yunus
ISSN 1858-1633 2005 ICTS 19
Figure 3. Gravity fvlow rig
Figure 4. The regression line of sensors output versus measured mass flow rates
Figure 5a. Concentration profile at a flow rate of 27 gs
Figure 5b. Concentration profile at a flow rate of 49 gs Figure 5c. Concentration profile at a flow rate of 71 gs
4. RESULT
