Flow Measurement of Biomass and Blended Biomass Fuels in ...

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 61, NO. 5, MAY 2012

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Flow Measurement of Biomass and Blended Biomass Fuels in Pneumatic Conveying Pipelines Using Electrostatic Sensor-Arrays Xiangchen Qian and Yong Yan, Fellow, IEEE

Abstract—Key parameters such as particle velocity, concentration of solid particles, and stability of pulverized fuel flow in fuel injection pipelines are useful to power plant operators to detect fuel supply problems at an early stage. This paper presents the use of a novel multichannel instrumentation system with circular and arc-shaped electrostatic sensor arrays for the online continuous measurement of “mean” and “local” characteristics of blended biomass flow. Experimental tests were conducted on a pneumatic conveying test rig under various flow conditions on both horizontal and vertical pipes. The biomass fuels tested include willow, wood, and bark. A ground grain (flour) was used to replicate a biomass of finer particles. The results suggest that, due to the physical differences between the constituent biomass fuels, the characteristics of the flow depend on the proportion of larger biomass particles in the blend. It is found that pure flour particles travel faster and carry more electrostatic charge than those of larger biomass particles. As more biomass particles are added to the flow, the overall velocity of the flow slows down, the electrostatic charge level decreases, and the flow becomes less stable compared to the pure flour flow. Particles in the vertical pipe are found to be more evenly distributed, and the particle velocity profile across the pipe cross section is more regular when compared to those in the horizontal pipe. Index Terms—Biomass–coal flow, blended biomass, crosscorrelation, electrostatic sensor, flow measurement, pulverized fuel (PF), velocity measurement.

I. I NTRODUCTION

T

HE USE of a diverse range of biomass fuels at existing coal-fired power stations is becoming increasingly widespread to reduce greenhouse gas emissions from electrical power generation. Cofiring biomass with pulverized coal is one of the most practical strategies being adopted by power generation organizations to reduce carbon dioxide emissions. Manuscript received June 20, 2011; revised October 2, 2011; accepted October 3, 2011. Date of publication January 9, 2012; date of current version April 6, 2012. This work was supported in part by a grant (B90) from the British Coal Utilisation Research Association and in part by a grant-in-aid (EP/F061307/1) from the Research Councils U.K. Energy Program. The Associate Editor coordinating the review process for this paper was Dr. V. R. Singh. X. Qian is with the School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China, and also with the Instrumentation, Control and Embedded Systems Research Group, School of Engineering and Digital Arts, University of Kent, CT2 7NT Canterbury, U.K. (e-mail: [email protected]). Y. Yan is with the Instrumentation, Control and Embedded Systems Research Group, School of Engineering and Digital Arts, University of Kent, CT2 7NT Canterbury, U.K. (e-mail: [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TIM.2011.2175034

Meanwhile, many coal-fired power stations worldwide are being converted to burning biomass. Biomass fuels are often blended before being pneumatically injected to the boiler in order to maximize combustion performance. It is expected that biomass–coal mixture or blended biomass flow is significantly more complex than that of the pure pulverized coal flow due to the differences in physical properties between biomass and coal and between different biomass fuels. Quantitative data about biomass–coal mixture flow and blended biomass flow, such as particle velocity and concentration, will inform power plant operators so that the biomass fuels and coal can be better prepared and optimally mixed for improved fuel delivery. Despite recent advances in the field of gas–solid flow measurement, very little is known about the dynamic behaviors of biomass–coal mixtures and blended biomass flow in a pulverized fuel (PF) pipeline. There have been comprehensive reviews of existing methods for online PF flow measurement [1]–[3]. Many of the inferential methods based on light scattering [4], radiometric [5], acoustic [6], and capacitive [7] sensors are deemed practically unsuitable for online measurement of gas–solid flow. Although digital imaging techniques can provide the measurement of particle size distribution and local volumetric concentration, to make the optical sensing system operating on a full-scale PF pipeline has been a real challenge [8]. Owing to the advantages of low cost, simple structure, and robustness, electrostatic sensing techniques [8]– [13] have proven to be an effective method of determining the key parameters of gas–solid flow. Gajewski [9] used a wide single circular electrode to determine the velocity, mass flow rate, and concentration of particles based on their capacitive coupling model of the electrode. The use of a wide electrode may reduce the bandwidth and frequency response sensitivity of the measurement system. Xu et al. [10] studied the spatial sensitivity, spatial filtering effect, temporal frequency response characteristics, and bandwidth of a circular electrode following the initial work done by Yan et al. [11]. Extensive research of the nonintrusive circular electrodes and probe-type intrusive electrodes also has been undertaken through both modeling and experimental investigations. Shao et al. [12], [13] compared the performance of circular and rod electrostatic sensors through simulation and experimental work. It is confirmed in a technology status review [14] that online PF flow monitoring is a challenging area and no reliable online technique is currently available to meet this real need. To date, very little research has been undertaken on the measurement

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Fig. 1. Principle of correlation velocity measurement based on a pair of electrostatic sensors. (a) System structure. (b) Electrostatic signals and resulting correlation function.

of biomass–coal or biomass flow using nonintrusive methods. Most of the reported work in the literature is concerned with the measurement of pure coal particles across the whole cross section of the pipeline. Studies of local characteristics of biomass–coal mixture or blended biomass particles are scarce. In this paper, both “mean” and “local” particle velocities, electrostatic charge levels, and flow stabilities are investigated through experimental tests using a state-of-the-art multichannel electrostatic sensing system. As a result of recent research effort, novel electrostatic sensor arrays have been developed and evaluated. Some initial tests were conducted on a particle flow test rig to allow basic accuracy of the system to be established. Initial experimental work conducted on a horizontal pipe section of the test rig was presented at the 2011 International Instrumentation and Measurement Technology Conference [15]. This paper addresses all the related issues in much more detail along with more experimental results under a wider range of test conditions.

Fig. 1 shows the general principle of the correlation velocity measurement system. As can be seen from Fig. 1(a), two identical parallel electrostatic electrodes, one being positioned downstream of the other, are used to determine the particle velocity using a cross-correlation technique. The correlation velocity can be derived from vc =

A. Correlation Velocity Measurement The movement of solid particles in a pneumatic pipeline generates a net electrostatic charge on the surface of particles through interactions with each other, the pipeline, and the conveying air [11]. Although the amount of charge carried by particles is usually unpredictable, it can be detected by a set of insulated electrodes in conjunction with suitable designed electronic circuits, which derive induced current signals from the fluctuations in the electric field caused by the passage of the charged particles.

(1)

where L is the center-to-center spacing between the upstream and downstream electrodes and τ is the transit time taken by the particles to move from the upstream electrode to the downstream one. Typical electrostatic signals of pneumatically conveyed particles are shown in Fig. 1(b). As can be seen, the normalized cross-correlation function between the two simultaneously sampled signals xi and yi (i = 1, 2, . . . , N ) from the two electrostatic sensors can be calculated as M

II. M EASUREMENT P RINCIPLES AND S YSTEM D ESIGN In view of the intrinsic limitations of other available techniques, electrostatic sensor arrays combined with correlation techniques are applied to determine the particle velocity, charging level of the particles, and flow stability.

L τ

Rxy =

(xi − x)(yi+t − y) M M (xi − x)2 (yi+t − y)2 i=1

i=1

i=1

(t = 0, 1, 2, . . . , N − M ) (2) where N is the length of the sample set, M is the correlation length, and x and y are the mean values of the two signals, respectively. The transit time τ can be determined from the location of the dominant peak in the normalized correlation function [Fig. 1(b)]. The dominant peak is regarded as correlation coefficient, which mainly depends on the similarity of the two signals and is less dependent upon the signal amplitude. The correlation coefficient value can be used as a measure of the stability of the flow—a more stable flow gives rise to a greater correlation coefficient.

QIAN AND YAN: FLOW MEASUREMENT OF BIOMASS AND BLENDED BIOMASS FUELS IN PNEUMATIC PIPELINES

Fig. 2.

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Schematic of the multichannel electronic signal processing system.

The concentration of particles is another important parameter indicating the quantity of solids in the pipeline. The electrostatic sensor offers the most cost-effective and the simplest means of measuring the relative concentration of particles among all proposed techniques methods [3]. Because the electrostatic sensor responds only to moving particles, the measured concentration data enjoy a large degree of immunity from the effects of solid accretion, which adversely affect other technologies [3]. The major problem in applying this sensing technique lies in relating the concentration to be measured to the magnitude of the charge signal, which depends upon the physical properties of the particles (size, shape, distribution, conductivity, permittivity, chemical composition, moisture content, and so on) and conveying conditions (pipe size, pipe wall roughness, line temperature, and so on). The concentration and velocity of solids are also known to be factors contributing to the magnitude of the charge signal. It is therefore extremely difficult to interpret measurement results except when all the aforementioned parameters are well defined and constant. In this paper, the root-mean-square (rms) magnitude of the electrostatic signal (or rms charge level) is adopted to indicate relative particle concentration of the flow under steady-flow conditions. Since the particle flow condition is mostly dilute in PF pipelines, the rms charge level can be regarded as proportional to the actual concentration of particles in the pipeline [11].

of metal shielding materials are inserted between the electrodes and connected to the common ground. The installation of the printed circuit boards (PCBs) on the sensing head is also shown in Fig. 3(c). As can be seen, the four preamplifier units are connected to four arrays of arcshaped electrodes, while the main PCB is attached to the four circular electrodes. The preamplified signals from both arc-shaped electrodes and circular electrodes are connected to multiplexers to be selected by the user according to the measurement requirement. The selected signals (up to four channels) are amplified through a secondary amplifier to reach a certain level suitable for sampling before passing through a second-order Sallen–Key low-pass filter with a cutoff frequency of 100 kHz. The filter is used to eliminate high-frequency noise mainly from analog electronic modules and external interferences. The analog signals are sampled and processed in the signal processing unit (Fig. 2). A USB port on a laptop is used to power up the signal processing system and to realize the data communication between the embedded electronic circuit and the laptop. A grounded metal screen is mounted on each piece of PCB to minimize surrounding electronic and electromagnetic interferences. III. E XPERIMENTAL R ESULTS AND D ISCUSSIONS A. Test Rig

B. Electronic Signal Processing System Fig. 2 shows a block diagram of the multichannel signal processing system. The whole system comprises four units, namely, the sensing unit, signal conditioning unit, signal processing unit, and system control unit. The structure of the electrostatic sensing head, including electrodes (stainless steel), insulation material (polyvinyl chloride), and connection terminals, is shown in Fig. 3. The spool piece has an inner diameter of 50 mm and houses five arrays of electrodes, each of them consisting of four identical nonintrusive electrodes. The axial width of each electrode is 2 mm. The center-to-center spacing between any two adjacent electrodes is 16 mm. As can be seen from Fig. 3(a), the circular electrode array (Array E) is mounted flush to the inner pipe wall. Four arrays of arc-shaped electrodes are evenly distributed around the pipe wall (Array A on the top, Arrays B and D in the middle, and Array C at the bottom). The circular and arc-shaped electrode arrays are combined to determine the “mean” and “local” characteristics of the particles, respectively. In order to minimize the interference between the electrodes, small pieces

Experimental work was undertaken on a 50-mm bore stainless steel particle flow test rig. Fig. 4 shows the layout of the test rig. There are five test sections on different parts of the flow rig, allowing test work in both vertical and horizontal pipelines. The axial direction of vertical pipe sections is about 10◦ to the vertical plane due to the limitation of the test rig support frame. An industrial suction system is connected to the lower right side of the stainless steel pipeline to generate a stable air flow. A vibratory feeder at the top right of the rig is used to feed particulate material into the rig. As both the power supply of the suction system and the vibratory feeder are adjustable, various particle flow conditions can be created. B. Test Materials Relevant physical properties of the test materials are summarized in Table I with their static images, which were taken using an in-house particle imager, being shown in Fig. 5. Three common biomass fuels were tested, namely, willow, wood, and bark. A ground grain (flour) was used as a fine biomass flow base. There are two primary reasons for this. First, the flour is a

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Fig. 3. Structure of the electrostatic sensor arrays. (a) Cross-sectional view of the arc-shaped and circular electrodes. (b) Longitudinal view. (c) Photos of the electrostatic sensing head.

Fig. 4. Layout of the particle flow test rig.


TABLE I P ROPERTIES OF T EST M ATERIALS

substitute for pulverized coal in order to study biomass–coal mixture and blended biomass flow behaviors and to comply with health and safety regulations of the university laboratory. Second, the flour replicates the fine constituent element of a blended biomass. As can be seen from Table I and Fig. 5, the size and shape of coal and flour are similar but quite different from those of biomass fuels. The equivalent diameters of the biomass fuel particles can be greater than 2 mm, which is much bigger than that of the flour (150 µm) [16] and coal (60 µm). As Fig. 5 shows, the biomass fuels have much higher mean aspect ratio (the ratio of the longest to the shortest diameters of a particle) than the flour, which means that the flour particles are roundish while biomass particles are irregular. All of these properties are expected to affect the flow characteristics.

C. Test Program As shown in Table II, three groups of experiments were conducted using the test materials on both horizontal and vertical pipes. The mass flow rate of the biomass fuels was set to a constant at 25 g/min, while for the pure flour and blended biomass tests, the mass flow rates were set to be 36 and 54 g/min, respectively, under test conditions.

D. System Tests Some initial experimental work was undertaken to assess the performance of the developed particle flow metering system. Fig. 6 shows a direct comparison between the measured particle velocity and the conveying air velocity for flour and willow. The air velocity was determined using a digital hot-wire anemometer traversing across the pipe cross section. It is evident that a good linear relationship exists between the measured correlation velocity and the air velocity. The willow particles travel slower than the flour particles, and the slip velocity between the particles and the conveying air are about 1.8 and 0.5 m/s, respectively. The flour velocity at the top of the pipe cross section is lower than that of the other parts (middle and bottom of the pipe), where the velocities are quite similar. As to the willow flow, top and middle parts of the flow travel faster than other parts of the flow at lower air velocity, while the middle and bottom parts of the flow run fastest and slowest, respectively, when the air velocity was above 20 m/s. The test results demonstrate that the developed system performs well for the online measurement of particle velocity.

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E. Tests on a Horizontal Pipe Fig. 7 shows typical results of particle velocity, rms charge level, and correlation coefficient as a function of time for flour and willow under three different velocity conditions (24, 20, and 15 m/s). The circular electrodes (Array E) were selected as the measuring electrodes, and the mass flow rates of flour and biomass are 36 and 25 g/min, respectively. As each array of electrodes consists of four equally spaced identical electrodes, six sets of correlation velocity and correlation coefficient can be obtained from different electrode combinations. It is noticed that there are no significant differences in the measured correlation velocities between electrode pairs for biomass flow while there were consistent gaps among the flour flow velocity results. Such phenomena indicate that the electrostatic properties of very fine particles (flour or pulverized coal) and relatively bigger particles (biomass fuels) in the pipeline are different, depending upon the physical properties of the material. The results illustrate that the four circular electrodes have given almost identical rms charge levels, which is a good indication of the matched electronic properties of the four signal conditioning units. As the air slows down, the charge level of flour flow decreases significantly, while that of the biomass flow does not change much when the air velocity is below 20 m/s. As expected, the correlation coefficient depends on the spacing of the electrodes with a shorter spacing (electrodes 1 and 2) giving rise to better similarity between the two signals. The correlation coefficients of both flour and biomass flows stay relatively stable regardless of changes in air velocity, which means that the tested flows are stable when the velocity of conveying air is faster than 15 m/s. This also suggests that the measurement system is capable of providing reliable particle velocity measurement under realistic flow conditions. On the basis of the experimental results for the pure material flow, a series of tests was conducted using willow–flour blends. Fig. 8 shows the particle velocity measured by the closest two electrodes of each sensor array under different flow conditions. The mass flow rate of the flour and willow–flour blend was fixed at 54 g/min. “F + 3%W” in the legend represents that there is 3% of willow in the blend. As shown in Fig. 8, the correlation velocity of the blend flow is consistently lower than that of the pure flour flow. The velocity decreases when the mass fraction of willow increases in the blend. It can be seen that the particles at the top of the pipe cross section run slightly faster than those in the middle but slower than those at the bottom. This is attributable to the gravitational effect in a horizontal pipe. Unlike the measured correlation velocity, the stability (correlation coefficient) of the blends changes considerably compared to the pure flour flow when the velocity increases. As can be seen from Fig. 9, the correlation coefficients measured from different sensor arrays are consistently larger than that from pure flour flow. This may result from the stable flow patterns due to the presence of biomass fuel particles in lower velocity conditions. However, the correlation coefficient reduces when the conveying air velocity increases, particularly in the middle and at the bottom of the pipe due to the increased turbulence in the pipeline, while the pure flour flow is fairly stable because of the fineness of the flour particles distributed more evenly in

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Fig. 5. Images of the test materials. (a) Coal (not to scale). (b) Flour (not to scale). (c) Willow. (d) Wood. (e) Bark. TABLE II T EST P ROGRAM

Fig. 6. Comparison between correlation velocity and air velocity for four electrode pairs at different locations. (a) Flour with mass flow rate of 36 g/min. (b) Willow with mass flow rate of 25 g/min.

the pipe cross section. It is possible that, in the lower velocity conditions, the flour and willow particles are better mixed (the smaller flour particles even adhere to the surface of the much larger willow particles) and travel together in the pipeline, while as the flow speeds up, the two materials gradually separate out and run at slightly different velocities—the induced current on the electrode depends on both of the materials, and as a result,

the correlation coefficient between the two adjacent electrodes decreases. The rms charge levels of different blends measured by electrode arrays E, A, B, and C are shown in Fig. 10. As can be seen, the charge levels on the circular electrodes are much higher than those on the arc-shaped ones. This makes sense as the sensing area of the circular electrode covers the entire circumference


Fig. 7.

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Results from circular electrodes for three different air velocities (24, 20, and 15 m/s).

Fig. 8. Comparison of correlation velocities between different biomass blends. (a) Sensor Array E. (b) Sensor Array A. (c) Sensor Array B. (d) Sensor Array C.

of the pipe cross section. It is obvious that the charge level goes higher as the particles speed up and the mass flow rate increases. The higher charge levels detected by the electrodes

Fig. 9. Comparison of correlation coefficients between different biomass blends. (a) Sensor Array E. (b) Sensor Array A. (c) Sensor Array B. (d) Sensor Array C.

at the middle and bottom parts of the pipe may stem from the fact that more particles are distributed at the central and lower parts of the pipe because of the gravitational effect.

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Fig. 10. Comparison of rms charge levels between different biomass blends. (a) Sensor Array E. (b) Sensor Array A. (c) Sensor Array B. (d) Sensor Array C.

F. Tests on a Vertical Pipe In a power station, PF may go through many sections of pipelines of different orientations before reaching the burners. It is thus necessary to study the movement behaviors of mixture flow in a vertical pipeline. The electrostatic sensing head was installed in the vertical test section, which is about 10◦ from the vertical plane (Fig. 5). Experimental tests under the same conditions on the horizontal test section were repeated on the vertical section. Typical results, which were selected from a large amount of data obtained, are presented here. Fig. 11 shows the measured particle velocity, correlation coefficient, and rms charge level as a function of time under high-airvelocity conditions (about 27 m/s) from both horizontal and vertical pipe tests. Electrode pairs 1&2 and 1&4 of each sensor array (Arrays A–E) were selected as the measuring electrodes in turn. As can be seen from Fig. 11, two groups of results from the horizontal pipe section, which use pure flour and flour–willow blend with a mass flow rate of 54 g/min, are selected to make a comparison among nine groups of results on the vertical pipe. It is clear that the correlation velocity on the vertical pipe is slightly greater than that on the horizontal pipe due to the gravitational effect. Biomass and flour–biomass blend flows travel slower than the pure flour flow, and the velocity decreases with the proportion of biomass in the blends. Unlike in the horizontal pipe, the flow at the left part (Sensor Array A) of the pipe cross section runs always faster than that at the other parts. The front (Sensor Array D) and back (Sensor Array B) of the flow travel at a similar velocity but faster than that of the right (Sensor Array C) part of the flow. This is not surprising as the test section on the vertical pipe was installed just five times the pipe diameter away from the elbow of the

horizontal test section (Fig. 5). It is interesting to note that particles at the back (Sensor Array B) move slightly faster than those in the front (Sensor Array D) because of the small angle between the flow line and the vertical plane. As expected, the correlation velocity from the circular electrode pairs (Sensor Array E) is roughly an average of the results from other sensor arrays. The correlation coefficient between electrodes 1 and 2 is consistently higher than that between electrodes 1 and 4. This result is similar to those obtained from the horizontal test section. From the correlation coefficient obtained, it is clear that the particle flow in the vertical pipe is relatively more stable than that in the horizontal pipe, because the friction between the flow and the pipe wall is much smaller due to the free-fall effect. Pure flour flow gives the highest correlation coefficient, and as the mass ratio of the biomass–flour blend increases, the correlation coefficient drops. It is noticed that the correlation coefficient on the left part of the pipe is consistently higher than that in other parts. Again, this is due to the centrifugal force when particles turn at a 90◦ angle at the bend. It is natural that the charge level goes higher as the mass flow rate of particles increases. It can be seen from the results on the horizontal pipe that higher charge level is detected in the middle of the pure flour flow. As willow dust was added into the flour flow, more particles were present at the central and lower parts of the pipe due to the gravitational effect of large willow dust. The charge levels measured from the vertical pipe are quite different from those from the horizontal pipe as the gravity has little effect in such a condition. As expected, similar charge levels are measured on the front, right, and back of the pipe, which means that the particle distribution in most part of the pipe cross section is relatively uniform. Again, a higher charge level is always measured from the electrode on the left side of the pipe (Sensor Array A) due to the centrifugal force at the bend. The experimental results presented previously indicate that the movement behaviors of the flour–willow blend flow differ from those of pure flour flow or pure willow flow. In the horizontal pipe, the flour and willow particles are well mixed and travel together in the pipeline under the lower velocity conditions, while the two materials gradually separate out and run at slightly different velocities as the flow speeds up. As a result, the overall stability of blend flow in the horizontal pipe has no significant change when the conveying air velocity keeps higher than 15 m/s, while the stability of different part of the flow goes down with increasing conveying air velocity, particularly in the middle and bottom regions of the pipe. The blend flow travels slightly slower than the pure flour flow and does not have a fine linear relationship with the conveying air velocity in both horizontal and vertical pipes. For the circular electrodes, there are no significant differences in the correlation coefficient and charge level between the pure flour flow and blend flow. As more biomass is added into pure fine flour flow, the overall velocity and rms charge level decrease in both horizontal and vertical pipes. The experimental results also indicate that the fuel particles in a vertical pipe are more evenly distributed across the pipe with a more regular velocity profile.


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Fig. 11. Comparison between particle flows on both horizontal and vertical pipelines.

IV. C ONCLUSION A multichannel flow measurement system, comprising electrostatic sensor arrays and electronic and computing elements, has proven effective for the online continuous measurement of biomass or blended biomass flow. Experimental investigations of the dynamic behaviors of biomass flows using the developed system on both horizontal and vertical pneumatic conveying pipelines have been conducted on a laboratory-scale test rig. The flow characteristics have been determined and compared in terms of correlation velocity, rms charge level, and correlation coefficient. These parameters have provided quantitative information about the dynamics of biomass flow and blended biomass flow. Test results have demonstrated that, due to the physical differences between the biomass fuels and the flour, the proportion of larger biomass fuels in the blend affects the characteristics of the flow. Biomass fuel particles carry less electrostatic charge and travel slower than the flour particles. The blended biomass flow pattern is more complex than that of pure flour flow and fluctuates more significantly as the conveying air speeds up. The willow particles are found to move at a slower velocity and carry less electrostatic charges than bark and willow particles because of their differences in size and shape. Due to the gravitational effect, particles

in the downward vertical pipe travel slightly faster and are distributed more evenly in the pipe cross section than those in the horizontal pipe. For the same reason, the particle velocity profile across a vertical pipe is more regular than that across a horizontal pipe.

ACKNOWLEDGMENT The views expressed are those of the authors and not necessarily those of the British Coal Utilisation Research Association or RCUK. The Energy Program is an RCUK cross-council initiative led by the Engineering and Physical Sciences Research Council and contributed to by the Economic and Social Research Council, Natural Environment Research Council, Biotechnology and Biological Sciences Research Council, and Science and Technology Facilities Council. R EFERENCES [1] Y. Yan and D. Stewart, “Guide to the flow measurement of particulate solids in pipelines, Part 1: Fundamentals and Principles,” Powder Handling Proc., vol. 13, no. 4, pp. 343–352, 2001. [2] Y. Yan, “Mass flow measurement of bulk solids in pneumatic pipelines,” Meas. Sci. Technol., vol. 7, no. 12, pp. 1687–706, Dec. 1996.

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[3] Y. Zheng and Q. Liu, “Review of techniques for the mass flow rate measurement of pneumatically conveyed solids,” Measurement, vol. 44, no. 4, pp. 589–604, May 2011. [4] X. Cai, J. Li, X. Ouyang, Z. Zhao, and M. Su, “In-line measurement of pneumatically conveyed particle by a light transmission fluctuation method,” Flow Meas. Instrum., vol. 16, no. 5, pp. 315–320, Oct. 2005. [5] I. R. Barratt, Y. Yan, and B. Byrne, “A parallel-beam radiometric instrumentation system for the mass flow measurement of pneumatically conveyed solids,” Meas. Sci. Technol., vol. 12, no. 9, pp. 1515–1528, Sep. 2001. [6] S. J. Tallon and C. E. Davies, “The effect of pipeline location on acoustic measurement of gas-solid pipeline flow,” Flow Meas. Instrum., vol. 11, no. 3, pp. 165–169, Sep. 2000. [7] H. L. Hu, Q. Zhou, T. Xu, S. Hui, Q. Zhao, and H. Tan, “Capacitancebased system for solid concentration measurement in gas/solid two-phase flow,” Chin. J. Sci. Instrum., vol. 28, no. 11, pp. 1947–1950, 2007. [8] R. M. Carter, Y. Yan, and S. D. Cameron, “On-line measurement of particle size distribution and mass flow rate of particles using combined imaging and electrostatic sensors,” Flow Meas. Instrum., vol. 16, no. 5, pp. 309–314, 2005. [9] J. B. Gajewski, “Electrostatic nonintrusive method for measuring the electric charge, mass flow rate, and velocity of particulates in the twophase gas-solid pipe flows—Its only or as many as 50 years of historical evolution,” IEEE Trans. Ind. Appl., vol. 44, no. 5, pp. 1418–1430, Sep./Oct. 2008. [10] C. Xu, S. Wang, G. Tang, D. Yang, and B. Zhou, “Sensing characteristics of electrostatic inductive sensor for flow parameters measurement of pneumatically conveyed particles,” J. Electrostics, vol. 65, no. 9, pp. 582– 592, 2007. [11] Y. Yan, B. Byrne, S. Woodhead, and J. Coulthard, “Velocity measurement of pneumatically conveyed solids using electrodynamic sensors,” Meas. Sci. Technol., vol. 6, no. 5, pp. 515–537, May 1995. [12] J. Shao, J. Krabicka, and Y. Yan, “Comparative study of electrostatic sensors with circular and probe electrodes for velocity measurement of pulverised coal,” Chin. J. Sci. Instrum., vol. 28, no. 11, pp. 1921–1926, 2007. [13] J. Shao, J. Krabicka, and Y. Yan, “Velocity measurement of pneumatically conveyed particles using intrusive electrostatic sensors,” IEEE Trans. Instrum. Meas., vol. 59, no. 5, pp. 1477–1484, May 2010. [14] DTI, Pulverised Fuel (PF) flow measurement and control methods for utility boilers, Technology Status Report 014, DTI/Pub URN 01/156 2001. [15] X. Qian, Y. Yan, and A. Malmgren, “Flow measurement of pneumatically conveyed biomass-coal particles using multi-channel electrostatic sensors,” in Proc. IEEE I2MTC, Hangzhou, China, May 10–12, 2011, pp. 1428–1433. [16] [Online]. Available: http://www.wikipedia.org

Xiangchen Qian received the B.Eng. degree in industrial automation from Tianjin University of Technology, Tianjin, China, in 2004 and the M.Sc. degree in automatic measurement and devices from Tianjin University, Tianjin, in 2007. He is currently working toward the Ph.D. degree in electronics engineering at both the University of Kent, Canterbury, U.K., and Tianjin University. His primary interest lies in the development of online gas–solid flow measurement instruments for coal-fired power plants and other industries. His research interests include digital signal processing, sensor design, and software development.

Yong Yan (M’04–SM’04–F’11) received the B.Eng. and M.Sc. degrees in instrumentation and control engineering from Tsinghua University, Beijing, China, in 1985 and 1988, respectively, and the Ph.D. degree in solid flow measurement and instrumentation from the University of Teesside, Middlesbrough, U.K., in 1992. He started his academic career in 1988 as an Assistant Lecturer with Tsinghua University. In 1989, he joined the University of Teesside as a Research Assistant. After a short period of postdoctoral research, he worked initially as a Lecturer with the University of Teesside, during 1993–1996, and then as a Senior Lecturer, Reader, and Professor, respectively, with the University of Greenwich, London, during 1996–2004. He is currently a Professor of electronic instrumentation, the Head of Instrumentation, Control and Embedded Systems Research Group, and the Director of Research at the School of Engineering and Digital Arts, University of Kent, Canterbury, U.K. He has published in excess of 270 research papers in journals and conference proceedings in addition to 12 research monographs. Dr. Yan is a Fellow of the Institution of Engineering Technology (formerly IEE), the Institute of Physics, and the Institute of Measurement and Control, U.K. He was awarded the Achievement Medal by the IEE in 2003, the Engineering Innovation Prize by the IET in 2006, and the Rushlight Commendation Award in 2009. In recognition of his contributions to pulverized fuel flow metering and flame imaging, he was named an IEEE fellow in 2011.