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ISA Transactions 51 (2012) 821–826

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ISA Transactions
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S. Ibrahim et al. / ISA Transactions 51 (2012) 821–826

2. The measurement system
In an optical tomography system, se...
S. Ibrahim et al. / ISA Transactions 51 (2012) 821–826

objects block the path from light transmitter to the receiver the
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S. Ibrahim et al. / ISA Transactions 51 (2012) 821–826

Sum of pixels (V)

sampled at 500 Hz. The individual sensor g...
S. Ibrahim et al. / ISA Transactions 51 (2012) 821–826

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Fig. 5. (a) Matrix and concentration profile of the first sampl...
826

S. Ibrahim et al. / ISA Transactions 51 (2012) 821–826

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Concentration measurements of bubbles in a water column using an optical tomography system

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Optical tomography provides a means for the determination of the spatial distribution of materials with different optical density in a volume by non-intrusive means. This paper presents results of concentration measurements of gas bubbles in a water column using an optical tomography system. A hydraulic flow rig is used to generate vertical air–water two-phase flows with controllable bubble flow rate. Two approaches are investigated. The first aims to obtain an average gas concentration at the measurement section, the second aims to obtain a gas distribution profile by using tomographic imaging. A hybrid back-projection algorithm is used to calculate concentration profiles from measured sensor values to provide a tomographic image of the measurement cross-section. The algorithm combines the characteristic of an optical sensor as a hard field sensor and the linear back projection algorithm.

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Concentration measurements of bubbles in a water column using an optical tomography system

  1. 1. ISA Transactions 51 (2012) 821–826 Contents lists available at SciVerse ScienceDirect ISA Transactions journal homepage: www.elsevier.com/locate/isatrans Concentration measurements of bubbles in a water column using an optical tomography system S. Ibrahim a,n, Mohd Amri.Md Yunus a, R.G. Green b, K. Dutton b a b Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia Materials and Engineering Research Institute, Sheffield Hallam University, City Campus, Sheffield, S1 1WB, United Kingdom a r t i c l e i n f o abstract Article history: Received 21 June 2011 Received in revised form 27 April 2012 Accepted 27 April 2012 Available online 22 May 2012 Optical tomography provides a means for the determination of the spatial distribution of materials with different optical density in a volume by non-intrusive means. This paper presents results of concentration measurements of gas bubbles in a water column using an optical tomography system. A hydraulic flow rig is used to generate vertical air–water two-phase flows with controllable bubble flow rate. Two approaches are investigated. The first aims to obtain an average gas concentration at the measurement section, the second aims to obtain a gas distribution profile by using tomographic imaging. A hybrid back-projection algorithm is used to calculate concentration profiles from measured sensor values to provide a tomographic image of the measurement cross-section. The algorithm combines the characteristic of an optical sensor as a hard field sensor and the linear back projection algorithm & 2012 ISA. Published by Elsevier Ltd. All rights reserved. Keywords: Bubbles Concentration Optical tomography Optical fibre sensors Tomography 1. Introduction In multi-phase flow measurement both the phase distribution and the velocity profiles vary significantly with temporal and spatial resolution. This is due to the different phases arranging themselves in various ways. For a multi-phase flow, the flow patterns are primarily functions of the volumetric fluxes of all phases. The flow patterns are functions of superficial velocities or pressure drops and are depicted in flow profiles. Hence it is important to have a knowledge of the flow profiles in order to design heat or heat and mass transfer equipment and to design fluid-based conveying processes [1]. Information on gas concentration is vital in various applications. In the medical field, it is important to have information on anaesthetic gas, oxygen or heliox concentration. In order to obtain a precise bill, it is vital to record the exact heating value of the gas for gas metering purpose. In gas burner appliances, flame optimisation is conducted for the purpose of efficiency and emission. This is carried out by regulating the mixing ratio of air and gas. Generally, accurate gas concentration measurement is vital in many gas handling applications [2]. Many techniques have been employed for gas or bubble detection in two-phase flows; both intrusive and non-intrusive measurement n Corresponding author. Tel.: þ60 19 7411 434; fax: þ60 7 55 66 272. E-mail addresses: salleh@fke.utm.my (S. Ibrahim), mus_utm@yahoo.com (M.Amri.M. Yunus), r.g.green@shu.ac.uk (R.G. Green), k.dutton@shu.ac.uk (K. Dutton). techniques were developed for such purpose. However, it is important that sensors being used for measurement purposes do not in any way perturb the quantity being measured. Point sensors are not generally suitable as they disturb the flow field. Non-intrusive techniques possess the advantage of not modifying the flow field and they are suitable for laboratory tests [3]. The word tomography comes from the Greek words ‘tomos’ which means a cut or slice and ‘graphein’ which means to write [4]. Process tomography is a methodology in which the internal characteristics of process vessel reaction or pipeline flows are acquired from measurements on or outside the domain of interest in a non-invasive fashion [5]. This paper describes an optical tomography system which is used to reconstruct an image from measurements obtained from several sensors placed around the measurement section of a hydraulic flow rig. Light travelling through a transparent medium suffers attenuation for various reasons, including scattering and absorption. Different materials cause varying levels of attenuation and it is this phenomenon that forms the concept of optical tomography. The voltage generated by the optical sensors is proportional to the level of received light. It is related to the amount of attenuation in the path of the light beam caused by the flow regime [6]. Information about the optical characteristics of a flow can be obtained if a view consisting of an optical emitter and detector pair are positioned either side of the measurement section. A larger area can be interrogated if several views are combined to form a projection. The image of the flow can be reconstructed if several different projections are utilised [7]. 0019-0578/$ - see front matter & 2012 ISA. Published by Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.isatra.2012.04.010
  2. 2. 822 S. Ibrahim et al. / ISA Transactions 51 (2012) 821–826 2. The measurement system In an optical tomography system, several groups of transmitter and receiver pairs are used in a system to provide better solution and to minimise the aliasing which occurs when two particles intercept the same view [8]. In this project, four dichroic halogen bulbs act as light projectors. The light receivers consist of an array of optical fibre sensors arranged in a combination of two orthogonal and two rectilinear projections (Fig. 1). The orthogonal projections each consist of an 8 Â 8 array of optical fibre sensors whereas the rectilinear projections consist of an 11 Â 11 array of optical fibre sensors (the numbers of sensors is chosen so that they will give a balanced sensitivity). Thus the total number of optical fibre sensors used is thirty eight. Ideally the two orthogonal and two rectilinear projections system should be in the same plane. However, if they were they would overlap each other and so two of the projections have to be placed in a separate plane. These planes are separated by only a few mm with the two orthogonal projection system placed on top of the rectilinear projections with respect to the direction of flow. Each optical fibre receiver has a length of 200 cm from the flow pipe to the electronic circuit. The receiver circuit is designed for signal conditioning using amplifiers and filters. The final outputs of the circuit are electrical signals consisting of a rectified voltage and an averaged voltage. The rectified voltage enables unipolar data acquisition and consequent signal processing using a PC. The output of the amplifier should be proportional to the gas flow rate passing the associated sensor. If all the averaged voltage amplifier outputs are summed, they should be proportional to the gas flow rate indicated by the gas rotameter. All the electronic circuits are placed in an earthed metal box to minimise electrical noise pick-up. The rectified analogue signals from an array of optical transducers, covering a cross-section of the pipe, are converted into digital form by the Keithley Instruments DAS-1800HC data acquisition system and passed into an image reconstruction system. For concentration measurements, a sampling frequency of 500 Hz per channel is chosen as the velocities and flow rates associated with this project are relatively low (i.e. 0.2–0.3 m/s). This enables two hundred and twenty-two points to be collected for each of the thirty-eight channels, which allows 0.44 s of flow data to be obtained. Data acquired by the receiver circuit is processed using the hybrid linear back projection algorithm, which has been described in a recent paper by Ibrahim et al. [9] in order to generate two-dimensional images of the bubbles in water. The algorithm incorporates both a priori knowledge and linear back projection (LBP) in order to improve the accuracy of the image reconstruction. Since optical sensors are hard field sensors, the material in the flow is assumed only to vary the intensity of the received signal. This enables a priori knowledge from the optical sensors to be used as a constraint in the reconstruction. The optical sensor signal is conditioned so when no Projector 2 Flow Pipe Projector 1 Top Aluminium Plate Optical Fibre Receivers Optical Fibre Receivers Perspex Optical Fibre Receivers Projector 3 Perspex Bottom Aluminium Plate Projector 4 Fig. 1. Arrangement of the projectors and optical fibres around the flow pipe—isometric view.
  3. 3. S. Ibrahim et al. / ISA Transactions 51 (2012) 821–826 objects block the path from light transmitter to the receiver the sensor will produce a zero output value, neglecting the effect of noise inherent in the system. The system was tested on the hydraulic flow rig shown in Fig. 2. The measurement system is built around a vertical pipe 1.27 m long with circular cross-section. Control of the water flow is effected by the use of a pump and by various valves installed in the rig. Bubbles are injected into the measurement section through two bubble injectors placed at the base of the vertical section. The two small air injectors are utilised to blow different sized gas bubbles from the bottom of the pipe. Small bubbles are generated by a porous plug in the base of the flow rig, producing bubbles which visually appear to be in the range 10 mm. Large bubbles are produced by direct gas injection into the flowing water. These bubbles are about 20 mm in diameter. When the bubbles rise up and pass though the imaging cross-section, the two-phase distribution over the imaging plane can be measured. The time history of bubbles rising up through the measurement section can be obtained in an off-line manner with the data stored on the hard-disc. Control of bubble flow is achieved through the use of two valves linked to the two bubble injectors. The valves can control the size of the bubbles as well as generating various flow regimes. The air pressure supply to the bubble injectors can be varied from 0 to 420 kP. Throughout the experiment, gas is injected at a constant pressure of 50 kP. The bubble collapsed as it reached the surface of the water. The flow pipe is made of perspex to enable visual observation of the flow. The lower measurement section, consisting of thirtyeight sensors, is placed 62 cm above the gas injection points and the second sensing array, which also consists of thirty-eight sensors, is placed 15 cm downstream of the former. The measurement section is of modular construction and comprises a series of perspex blocks 90 mm square with an 80 mm diameter central bore so that when bolted together they provide a continuous 80 mm diameter internal flow passage. In order to reduce optical distortion and to allow optical observation, a flat square shape perspex window is used [10]. The flow rig is equipped with two 823 rotameters: a water rotameter (0–7 l/min) and a gas rotameter (0–7 l/min). Each rotameter provides direct readings of the total flow rate of water and bubbles respectively. For the experiments described in this paper water always formed the continuous phase and the gas flow was always in the bubbly regime. The volumetric flow rate of bubbles ranged from 0 to 7 l/min. 3. Average concentration measurement The measurements presented in this section consider the results from each sensor as a continuous sample of the gas concentration within its sensing field. The method was to obtain two hundred and twenty-two samples for all thirty-eight sensors; each sensor was Table 1 The flow rates of bubbles and the corresponding sum of pixel voltages for small bubbles and large bubbles. Flow rates (l/min) Sum of pixel voltages for small bubbles (V) Error percentage of small bubbles (%) Sum of pixel voltages for large bubbles (V) Error percentage of large bubbles (%) 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 4.9 262.2 345.6 285.4 240.5 249.3 243.8 201.6 188 185.5 180.1 186.4 185.4 179.3 188.2 2 6.4 3.2 17.3 30.3 27.7 29.3 40.6 45.5 46.2 47.8 46.0 46.3 48.0 45.4 4.9 291.1 334.2 365.5 397.9 389.3 412 408.4 406.4 390.8 371.4 340.3 316.1 303.9 282.4 5.1 4.0 5.9 1.2 0.1 0.7 0.5 0.9 0.9 1.1 1.0 17.4 23.3 26.2 31.5 Fig. 2. The hydraulic flow rig.
  4. 4. 824 S. Ibrahim et al. / ISA Transactions 51 (2012) 821–826 Sum of pixels (V) sampled at 500 Hz. The individual sensor gains were compensated for in software. The mean value for each sensor was calculated at the specified flow rate used with the standard linear back projection algorithm to produce an image consisting of grey level pixels; white represents maximum flow, black zero flow. These pixel grey levels are represented by pixel voltages. The pixel voltages are summed over the measurement cross-section. The values obtained for each flow rate for small bubbles are compared with the values obtained for large bubbles to observe the effect of flow rates and bubble size. Table 1 shows the sum of pixels for small bubbles and large bubbles corresponding to various volumetric flow rates. The results in Table 1 are shown graphically in Fig. 3 and discussed in Section 5. Gas Flow Rate Calibration 450 400 350 300 250 200 150 100 50 0 Measured results (Small bubbles) Measured results (Big bubbles) Polynomial regression (Big bubbles) Polynomial regression (Small bubbles) 0 2 4 6 Flow Rate (l/min) Fig. 3. Gas flow rate calibration graph. 8 4. Concentration profiles The measurement system for concentration consists of thirtyeight sensors. Ideally, with zero flow, all sensors should have zero output. In practice many of the sensors have an output voltage due to factors such as drift, intrinsic noise and offset in operational amplifiers. To reduce these errors all the sensors were sampled at 500 Hz for 0.44 s with no gas flow at the start of each series of experiments. The root mean square voltage was then calculated for each sensor to provide a zero flow reference voltage. The zero flow reference voltages were used to correct the gas flow measurements for offset errors in each sensor. The experiments were conducted with the laboratory lights switched off to ensure that the mains lighting did not affect the light receivers. Measurements were made by energising all thirty-eight optical sensors and monitoring their outputs at several gas flow rates ranging from 0 l/min to 7 l/min. Throughout the experiment, water flowed upwards at a volumetric flow rate of 3 l/min. The following results show a sequence of images representing the reconstructed fields of bubbles flowing in water, which were generated at selected volumetric flow rates. A sequence of images representing small bubbles flowing at a volumetric flow rate of 0.5 l/min, are shown on Fig. 4a–d. A sequence of images representing large bubbles generated at a volumetric gas flow rate of 0.5 l/min, are shown on Fig. 5a–d. The existence of various colours in the matrix of Fig. 4a–d as well as Fig. 5a–d except the black colour indicates the location of bubbles. Black colour indicates no flow. For example in Fig. 4a, the concentration profile show the bubbles are located at pixels (6,3), (4,5), (5,5), (2,6), (4,6), (5,6) and (6,6). Fig. 4. (a) Matrix and concentration profile of the first sample representing small bubbles at 0.5 l/min. (b) Matrix and concentration profile of the second sample representing small bubbles at 0.5 l/min. (c) Matrix and concentration profile of the third sample representing small bubbles at 0.5 l/min. (d) Matrix and concentration profile of the fourth sample representing small bubbles at 0.5 l/min.
  5. 5. S. Ibrahim et al. / ISA Transactions 51 (2012) 821–826 825 Fig. 5. (a) Matrix and concentration profile of the first sample representing large bubbles at 0.5 l/min. (b) Matrix and concentration profile of the second sample representing large bubbles at 0.5 l/min. (c) Matrix and concentration profile of the third sample representing large bubbles at 0.5 l/min. (d) Matrix and concentration profile of the fourth sample representing large bubbles at 0.5 l/min. 5. Discussion of results The gas flow rate calibration graph (Fig. 3) shows the sum of voltages in all pixels within the flow pipe plotted against the volumetric flow rate of the bubbles for both small bubbles and large bubbles. The results shown in Table 1 give a noise level of 4.9 V, at zero flow, which corresponds to levels of 1.4% of maximum flow reading for small bubbles and 1.2% for large bubbles. The results obtained in section 4 indicate that the system reacts to large and small bubbles in a similar manner. However, the peak of the graph occurs at higher flow rates with large bubbles than small bubbles. Fig. 3 shows the peak occurring at a gas flow rate of 1 l/min for small bubbles and 3 l/min for large bubbles. Empirical equations obtained using EXCEL software have been fitted to the results. For small bubbles the equation used is Sum of pixels ¼ À0:32x6 þ 7:76x5 þ 72:93x4 þ340:26x3 þ 810:16x2 þ 860:21x ð1Þ where x is the gas flow rate in l/min. The equation used the large bubbles is in the centre of the pipe. This means that the majority of small bubbles are confined to the central part of the measurement cross-section and only affect a few sensors. As the flow rate increases the bubbles get closer together. In the case of small bubbles, the gas flow rate calibration graph shows that initially from 0 to 1 l/min, the sum of the pixels increases, but from 1.5 l/min to 4 l/min, the sum of the pixels begins to decrease as the flow rate increases. The small gas bubbles are generally confined to the centre of the pipe. As the volumetric flow rate increases, more bubbles are released, resulting in only a few sensors being affected and as such, the sensors as well as the electronics have little time to recover from bubbles that flowed previously. The signal conditioning causes the signal at high gas flow rate to gradually reduce. Large bubbles occupy a much larger cross section of the conveyor than small bubbles and so the majority of the sensors detect the presence of the bubbles. The sum of the pixels increases over the flow rate of 0–3 l/min as shown in Fig. 3. However, beyond the volumetric flow rate of 3 l/min, the sum of the pixels begins to decrease as shown in Fig. 3. Sum of pixels ¼ À0:31x6 þ 7:39x5 À67:63x4 þ 302:38x3 À694:29x2 þ 796:74x þ 10:76 ð2Þ where x again is the gas flow rate in l/min. The majority of measurements were made with circulating water to ensure that the bubbles flowed upwards in the pipe. However, the flowing water appeared to keep the small bubbles 6. Conclusions In this paper, a non-intrusive concentration measurement of bubbles flowing in a water column has been presented. The results showed that the measurement system is able to fulfil the original objectives of obtaining an average gas concentration
  6. 6. 826 S. Ibrahim et al. / ISA Transactions 51 (2012) 821–826 and gas distribution profiles. For future work, it is suggested that experiments will be conducted over a larger range of flow regimes. Ideally in an industrial environment, it is preferable to use laser as the light source due to its monochromatic and coherent characteristics. The resolution can be increased by increasing the number of views per light sources for each pixel. Further investigation using other types of reconstruction algorithms and different forms of filtering techniques should be performed. The use of multi modality tomography should be investigated in which the optical tomographic system can be combined with other types of sensing with the aim of comparing the accuracy of the measurements and increasing the understanding of the flow process. Acknowledgement The authors wish to acknowledge the assistance of Universiti Teknologi Malaysia for providing the funds and resources in carrying out this research. References [1] Dyakowski T. Process tomography applied to multi-phase flow measurement. Measurement Science & Technology 1996;7:343–53. [2] /http://www.sensirion.com/en/pdf/RD-notes/RD-note_February2012.pdfS. [3] Rossi GL. Error analysis based development of a bubble velocity measurement chain. Flow Measurement and Instrumentation 1996;7(1):39–47. [4] Northrop RB. Noninvasive instrumentation and measurement in medical diagnosis. Boca Raton, FL: CRC Press; 2002. [5] Wang M. Process tomography (editorial). Measurement Science and Technology 2006:17. [6] Abdul Rahim R, Kok San C. Optical tomography imaging in pneumatic conveyor. Sensors and Transducers Journal 2008;95(8):40–8. [7] Daniels AR. Dual modality tomography for the monitoring of constituent volumes in multi-component flow. PhD thesis. Sheffield Hallam University; 1996. [8] Abdul Rahim R, Fea P J, Kok San C. Optical tomography sensor configuration using two orthogonal and two rectilinear projection arrays. Flow Measurement and Instrumentation 2005;16:327–40. [9] Ibrahim S, Green R G, Dutton K, Evans K, Goude A, Abdul Rahim R. Optical sensor configurations for process tomography. Measurement science & technology 1999;10:1079–86. [10] King NW, Purfit GL, Pursley WC. A laboratory facility for the study of mixing phenomena in water/oil flows. In: Proceedings of the symposium flow metering and proving techniques in the offshore oil industry (Aberdeen); 1983: p. 233–41.

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