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air three-phase flow by integrating electrostatic and capacitive sensors
Flow Measurement and Instrumentation 24 (2012) 43–49
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Flow Measurement and Instrumentation journal homepage: www.elsevier.com/locate/flowmeasinst
Concentration measurement of biomass/coal/air three-phase flow by integrating electrostatic and capacitive sensors Juan Zhang a , Hongli Hu a,∗ , Jun Dong a , Yong Yan b,c a
State Key Laboratory of Electrical Insulation and Power Equipment, Xi’an Jiaotong University, Xi’an 710049, China
b
Centre for Multiphase Flow Metering and Combustion Process Monitoring, School of Control and Computer Engineering, North China Electric Power University, Changping District, Beijing 102206, China c
Instrumentation, Control and Embedded Systems Research Group, School of Engineering and Digital Arts, University of Kent, Canterbury, Kent CT2 7NT, UK
article
info
Article history: Received 8 March 2011 Received in revised form 23 February 2012 Accepted 22 March 2012 Keywords: Three-phase flow Concentration measurement Capacitance sensor Electrostatic sensor Flow regime identification Data fusion
abstract This paper describes an integrated instrumentation system for the volumetric-concentration measurement of biomass/coal/air three-phase flow in a pneumatic conveying pipeline. The system combines electrostatic sensors with capacitive sensors and incorporates data fusion techniques. As the electrostatic sensor is more sensitive to dilute pulverized coal and the capacitive sensor is more sensitive to biomass particles, both sensor techniques are integrated for the concentration measurement of biomass and pulverized coal in biomass/coal/air three-phase flow. First, the flow regime is identified through the Hilbert marginal spectrum of the electrostatic sensor output signal. Then, under certain identified flow regimes, the dual regression analysis method is applied to work out the biomass concentration and the pulverized coal concentration. The experimental result indicates that the fiducial error of the system is less than 5%, and the resolution is about 1%. © 2012 Elsevier Ltd. All rights reserved.
1. Introduction Pneumatic conveying of biomass/coal blends is widely seen in coal and biomass fired power stations. In 1979, the Bayfront plant in the US state of Wisconsin began to burn wood, railroad ties, and other raw materials with pulverized coal [1]. Since 1992, Electric Power Research Institute (EPRI) has carried out substantial research on the cyclone furnace and pulverized coal furnace firing biomass, focusing on the proportion of mixed fuel and biomass fuel characteristics on the combustion process [1]. Georgia Power Company is dedicated to commercial applications of biomass cocombustion and the feasibility of the implementation of mixed fuel in existing power plants [1]. The co-existence of coal, biomass, and air in a pneumatic conveying pipeline is in effect a type of threephase flow. However, there is little research in the concentration measurement of biomass/coal/air three-phase flow. Successful online measurement of biomass and coal concentration in fuel feed pipes would lead to the improvement of combustion efficiency and stability, and reduction of atmospheric pollution and corrosion of the combusting chamber [2]. The development of adequate instrumentation for this application poses a significant challenge
∗
Corresponding author. Tel.: +86 29 82668390; fax: +86 29 83237910. E-mail address: [email protected] (H. Hu).
0955-5986/$ – see front matter © 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.flowmeasinst.2012.03.003
to scientists and engineers due to the difficulty nature of the threephase flow and the variability of plant operation conditions. In particular, problems arise from the very low solid/air mass flux ratios and the cross-sectional distribution of inhomogeneous solids in the pipeline. Extensive research has been undertaken to develop solid flowmeters for the measurement of gas–solid two-phase flow. Possible techniques for the measurement of the mass flow rate of solids have been discussed in detail by Beck et al. [3], Rajan et al. [4], and Yan [5,6]. Sensing techniques for the solid concentration measurement are classified into four main categories according to the sensing principles: electrical, attenuation, nuclear magnetic resonance, and tomographic methods [5,6]. Carlson et al. [7] discussed the possibility of measurement of the phase concentration by means of an orifice plate and a Venturimeter, and suggested that the orifice plate should be installed in series with a Venturimeter to measure the phase concentration. Jin et al. [8] proposed a differential-pressure concentration method by combining a capacitance meter with a Venturimeter. They suggested the use of such meters for fine particles, but provided no experimental verification. Efforts have also been made to develop flowmeters using impact plates [9] and the Coriolis principle [10]. However, these meters cannot be directly installed on pipelines for on-line measurement. Benes and Zehnula [11] proposed a new design for a three-phase flowmeter based on the fact that solid particles being
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J. Zhang et al. / Flow Measurement and Instrumentation 24 (2012) 43–49
Fig. 1. The HHT process.
carried by flowing air impacting a properly formed obstacle generate an acoustic surface wave. Nieuwland et al. [12] developed an optical system based on the detection of light reflected by suspended particles to measure the solid concentration and axial solid velocity in dense gas–solid three-phase flow. The above methods have been employed for measuring the concentration and velocity of solid particles, but the suitability and performance of such methods for the measurement of biomass and pulverized coal are unknown. Because of the physical differences between biomass and coal, the fluid dynamics of biomass/coal/air three-phase flow in the fuel lines is rather complex. Electrostatic and capacitive methods are widely recognized as the most promising solid flow measurement techniques [5,6]. Concentration measurements based on a combination of capacitive and electrostatic sensors have many advantages over other measurement techniques. Capacitive sensors are simple in structure, free of maintenance, there is no interference of the flow field, and there is direct output of an electric signal [13,14]. Although the structure of an electrostatic sensor is very similar to that of a capacitive sensor, there are fundamental differences between their sensing principles. Capacitance sensors are sensitive to the dielectric characteristics of solid particles while electrostatic sensors detect the electrostatic charge of moving particles. In general, electrostatic sensors have advantages of high sensitivity to dilute phase flow, simple structure, low cost, and nonintrusiveness in installation [15]. In this paper, data fusion of capacitive and electrostatic sensors is used to measure the concentration of biomass and coal in pneumatic pipelines. The flow regime was first identified by performing a Hilbert–Huang transform (HHT) on the electrostatic fluctuation signals, thus making the solid concentration measurement less affected by variations in the flow regimes. Then, under certain identified flow regimes, the dual regression analysis method was applied to determine the solid concentration. 2. Hilbert–Huang transform and dual regression analysis 2.1. Hilbert–Huang transform and feature extraction Huang et al. [16] introduced a general signal analysis technique called the Hilbert–Huang transform (HHT) to efficiently extract information in both the time and the frequency domains directly from data. The HHT process is illustrated in Fig. 1. It is a two-step algorithm, combining empirical mode decomposition (EMD) and Hilbert spectral analysis, to accommodate the nonlinear and nonstationary process. So it is appropriate to process the electrostatic fluctuation signal with an HHT. This method is not based on a priori selection of kernel functions; instead, it decomposes the signal into intrinsic mode functions (IMFs) derived from the succession of extrema. It is adaptive, efficient, and without any prior assumptions [17,18]. The electrostatic fluctuation signal x(t ) is decomposed into a finite number (n) IMFs, ci (t ), i = 1, 2, . . . , n, and the residual r (t ): x(t ) =
n
ci (t ) + r (t ).
(1)
i=1
Then the Hilbert transform is applied to each IMF to obtain a complex representation of the IMF, di (t ): di ( t ) =
1
π
∞ −∞
ci (t ) t −τ
dτ .
(2)
After that, ci (t ) and di (t ) form a complex conjugate pair that defines an analytical signal, zi (t ): zi (t ) = ci (t ) + jdi (t ) = ai (t )ejθi (t ) ,
(3)
where ai (t ) and θi (t ) are the amplitude and phase angle, respectively. Finally, the instantaneous frequency of each IMF, ωi (t ), is defined as dθi (t )
. (4) dt At a given time t, the instantaneous frequency ωi (t ) and the amplitude ai (t ) are calculated simultaneously, and these values are assigned to the Hilbert spectrum, H (ω, t ). ωi (t ) =
H (ω, t ) =
Re ai (t )ej ωi (t )dt
ωi (t ) = ω others.
0
(5)
With the Hilbert spectrum defined, we can also define the Hilbert marginal spectrum, h(ω), as h(ω) =
T
H (ω, t )dt ,
(6)
0
where T is the total data length. The following steps are taken to extract the features of the electrostatic signal Hilbert marginal spectrum for flow regime identification. First, the frequency band of the Hilbert marginal spectrum is divided into Y subbands. Then, the energy of each subband is calculated according to SEi =
N
h(ωj )2
i = 1, 2, . . . , Y ,
(7)
j =1
where SEi is the energy of the ith subband, Y is the total number of subbands, and N is the number of discrete frequency points of each subband. Finally, the subband energies of the electrostatic signal Hilbert marginal spectra of different flow regimes under various experimental conditions are input into a neural network to identify the flow regimes; this will be discussed in Section 4.1. 2.2. Dual regression analysis The aim of this study is to measure two physical quantities. They are pulverized coal volumetric-concentration CPC and biomass volumetric-concentration CB , with a capacitive sensor and an electrostatic sensor. In fact, there are inherent connections between the coal volumetric-concentration CPC and the characteristics of the two sensor outputs Ue and Uc . Ue and Uc are the true RMS (rootmean-square) value of the electrostatic signal and the mean value of the capacitance signal, respectively. The relationship can be described by the following formula: CPC = f (Ue , Uc ).
(8)
Similarly, the relationship between the biomass volumetricconcentration and the two signal characteristics can be described by CB = f (Ue , Uc ).
(9)
According to the theory of dual regression analysis, the specific descriptions of formulas (8) and (9) are as follows. CPC = α0 + α1 Ue + α2 Uc + α3 Ue2 + α4 Ue Uc + α5 Uc2 + ε1
(10)
CB = β0 + β1 Ue + β2 Uc + β
(11)
2 3 Ue
+ β4 Ue Uc + β
2 5 Uc
+ ε2 ,
J. Zhang et al. / Flow Measurement and Instrumentation 24 (2012) 43–49
45
where α0 to α5 and β0 to β5 are constant coefficients of the regression equations, while ε1 and ε2 are infinitesimals of higher order which can be ignored. If the constant coefficients are known, the regression equations, which can be used to predict the coal volumetric-concentration and biomass volumetric-concentration, are fixed. When the output characteristics are obtained, we can calculate the two volumetric-concentration values through (10) and (11). So calibration experiments must be performed first, and then the constant coefficients will be computed by using the leastsquares method with the calibration data. It is supposed that the numbers of the coal volumetricconcentration calibration points and biomass volumetricconcentration calibration points are n and m, respectively. The calibration points are CPC i (i = 1, 2, . . . , n) in its range and CBj (j = 1, 2, . . . , m) in its range. For each coal volumetric-concentration calibration point, m calibration experiments are performed with m different biomass volumetric-concentration values. So m ∗ n experiments are done, and m ∗ n groups of output characteristics (Uek , Uck ), k = 1, 2, . . . , m ∗ n, are obtained. According to the least-squares method, the constant coefficients obtained must meet the least-squares errors condition. Because the methods for obtaining α0 to α5 and β0 to β5 are similar, the method is presented for the constant coefficients α0 to α5 as an example. The square error between the actual value of the coal volumetric-concentration and the value calculated by the regression equation (10) is
∆2k = [CPC k − CPC (Uek , Uck )]2 k = 1, 2, . . . , m ∗ n,
(12)
where ∆2k is the coal volumetric-concentration square error of the kth calibration experiment. The mean-square error of the m ∗ n coal volumetric-concentration experiment is R1 =
1
m∗n [CPC k − (α0 + α1 Uek
m ∗ n k=1
2 2 + α2 Uck + α3 Uek + α4 Uek Uck + α5 Uck )]2
= R1 (α0 , α1 , α2 , α3 , α4 , α5 ).
∂ R1 = 0; ∂α1 ∂ R1 = 0; ∂α4
∂ R1 = 0; ∂α2 ∂ R1 = 0. ∂α5
3.1. Carrier air supply system Under the influence of the blower (10), air is blown into the conduit. The air supply velocity is controlled with a control valve (2). The rate and pressure of the air supply are measured by means of a counter blow type back-supported tube before entering the T-mixer (T). 3.2. Feeding systems
(13)
From the formula above, we can see that the mean-square error R1 is dependent on α0 to α5 . In order to minimize R1 , the constant coefficients should meet the conditions below:
∂ R1 = 0; ∂α0 ∂ R1 = 0; ∂α3
Fig. 2. Schematic diagram of a pneumatic conveying system.
(14)
Using the coal volumetric-concentration calibration points CPC k and the sensor output characteristics Uek and Uck , the equation set (14) is solved and the constant coefficients α0 to α5 are obtained. Similarly, the constant coefficients β0 to β5 can also be obtained. Then we can predict the coal volumetric-concentration and the biomass volumetric-concentration by (10) and (11) with the sensor output characteristics Ue and Uc which are different from the values used in the calibration. 3. The experimental system The experimental system that was used in this research is shown in Fig. 2 [19]. It has been designed and developed to facilitate the concentration measurement of solid phase in a pneumatic conveying line. It comprises the five major subsystems described in the following sections.
In the experimental facility, there are two feeding systems. One is for pulverized coal, and the other is for biomass. Each feeding system consists of a motor, a continuously adjustable gearbox, a booster fan, and a fine linear screw feeder. The feeding mass flow rate ranges from 30 g/min to 3 kg/min. The feeding systems are more stable and continuous than that adjusted by a silicon controlled rectifier (SCR). With the action of the booster fan, the feeding systems adjust the amount of discharging smoothly and accurately, and they are regarded as the calibration values in data fusion which determine the pulverized coal and biomass concentrations exactly. The mixed media are separated by a cyclone separator (8), and then collected by the collectors (9). The calibration values given by the feeding systems are more accurate than those by the collectors, because the two kinds of media cannot be separated by the cyclone separator completely. A pressure of −20 mm H2 O produced by the baffle and adjustable positive pressure by the booster fan make conveying the biomass and pulverized coal blend unhindered. The biomass and pulverized coal are both stored in a cylindrical steel discharging bin before being conveyed in the pipeline. The bottom end of the conical section is connected to a T-mixer through a short flexible connecting pipe. 3.3. Test section The test section is a vertical ceramic pipe of length 1200 mm and inner diameter the same as the conveying pipe, with an upward flow direction. As shown in Fig. 1, SC is the capacitive sensor, and SE is the electrostatic sensor. The test section is placed downstream of
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J. Zhang et al. / Flow Measurement and Instrumentation 24 (2012) 43–49
the T-mixer. The capacitive sensor for the measurement of biomass concentration is mounted at a distance of 800 mm from the pipe’s lower terminal, and the electrostatic sensor for the measurement of pulverized coal concentration is mounted in tandem with the capacitance sensor. The two sensors are connected to the signal conditioning circuit via a shielded twisted-pair cable. The construction and the dimensions of the sensors will be given later. Heaters (1) and (5) respectively are used to heat the air flow and the three-phase flow. Resistance temperature detectors (RTDs) (4) and (6) are used to measure the temperature. 3.4. Flow regime generator In the upstream of the two sensors there exists a substitutable throttle which is specially manufactured as a flow regime generator [20]. A transition flow regime is generated a short distance downstream of the throttle. For example, a stratified flow can be generated behind a gate valve, and a roping flow behind a Venturi tube or an orifice plate. The upstream edge of the throttle is especially designed to be streamlined to avoid powder accumulation, and the equivalent pore diameter of the throttle is about one tenth of its length. In the present work, two different throttles were used to generate roping flow and stratified flow regimes. It should be mentioned that, before every experiment test starts with a particular flow regime, the system is operated by a solenoid valve from no-flow to short-time flow conditions several times with pulverized coal as the conveying medium, and the flow regime can be observed by scenes projected on a screen mounted downstream of the throttle. Once a satisfactory flow regime is observed under several different conditions, the screen is then removed and the sensors are installed downstream of the throttle in order to detect the transition flow regime. 3.5. Start-up, operation, and measurement Conveying air is generated by turning on the blower. The air velocity is adjusted by a back-supported tube (3) and controlled to expectation level by regulating the valve (2) at the same time. The feeder is turned on before the feeding rate can be controlled to desired level by regulating the gearbox. Then, the fuel particles are fed to the T-mixer and they create a three-phase flow of which the concentration and velocity can be controlled through regulating the gearbox of the feeder and the valve (2), respectively. The capacitance sensing electrodes for the pneumatically conveyed biomass and coal blend should be critically designed so that the configuration can provide high and homogeneous capacitance sensitivity over the pipe cross-section, thus making the component concentration measurement less affected by variation of the flow regime. Meanwhile, cable capacitance connecting the sensor, parasitic capacitance, electromagnetic interference, leakage of weak signal, and circuit drift are all factors that may have an influence on the measurements. Efforts have been made to develop finite-element-based models and to carry out extensive simulation studies from which suggested dimensions of the electrodes for optimum and uniform sensitivity have been obtained [13]. However, practical considerations often prevent the use of the optimum design parameters [14]. Through the comparison of several electrode types, source–grid sensing electrodes have been proposed in this research. A group of stainless steel electrodes is distributed uniformly on the test pipe wall. An equivalent two-electrode capacitance is formed with the above electrodes in parallel. The optimum design parameters are as follows: the ratio of electrode width W to electrode separation d is 4:1, the number of electrodes K = 28, the electrode length L = 100 mm, and the radius of the pipe R1 = 100 mm, as shown in Fig. 3(b). Simulation studies have shown that above electrode
Fig. 3. Schematic diagram of the capacitance sensor. (a) Developed representation. (b) Cross-section.
configuration can provide a capacitance sensitivity over the pipe cross-section with inhomogeneity less than 3%. Insulating material is wrapped over the electrodes over which another thin stainless steel plate is glued to form the inner screen. A supporting steel tube outside of the inner screen acts as the outer screen. A follower is connected between the inner and outer screens, which forms equal potential between the screens. This specific design is used for preventing leakage of weak signal and eliminating the effect of electromagnetic interference and stay capacitance, as shown in Fig. 3. The two sensor plates and the screens are connected to a shielded twisted-pair cable. A protecting steel tube outside the shielded twisted-pair cable acts as the outer screen. This sensor forms the active sensor, while an identical sensor made on a similar tube forms the dummy. A differential capacitance transducer consisting of the active sensor and the dummy is formed to increase the detection sensitivity and to reduce the effects of temperature, stray parameters, and electromagnetic interference. The lengths of the shielded cables in the two cases are kept exactly the same to ensure that the nominal capacitance remains the same. During pneumatic transportation, particles carry certain net electrostatic charge due to frictional contact between particles and the pipe wall and between particles and the air stream. An electrostatic sensor can be used to detect the charge carried by the moving particles [21,22]. The construction of the electrostatic sensor is shown in Fig. 4. The inner diameter of the tested ceramic pipe R1 is 100 mm, and the outer diameter R2 is 105 mm. The electrostatic sensor is made of copper sheet, and the axial length (We) and the thickness of the sensor are 10 mm and 0.2 mm, respectively. The inner screen is also made of copper sheet. Its inner diameter R3 is 120 mm, the axial length l is 50 mm, and the thickness is 2 mm. A supporting steel tube outside the inner screen acts as the outer screen. A follower is connected between the inner and outer screens, which forms an equal potential between
J. Zhang et al. / Flow Measurement and Instrumentation 24 (2012) 43–49
47
Table 1 Flow regime identification results. Real flow regimes
Fig. 4. Construction of the electrostatic sensor.
Hilbert marginal spectrums of two flow regimes 0.045 roping flow stratified flow
0.04 0.035
Amplitude
0.03 0.025 0.02 0.015 0.01 0.005 0
0
20
40
60
80
100
Instantaneous frequency (Hz)
Fig. 5. Hilbert marginal spectra of the two flow regimes.
the screens. This sensor design is used to prevent leakage of weak signal and to eliminate the effect of electromagnetic interference. 4. Experimental results and discussion Experiments were carried out with pine sawdust and Shenmu pulverized coal. The diameter of the pulverized coal particles was less than 50 µm, and the length of the pine sawdust ranged from 0.1 to 1.2 mm. These particle sizing data were obtained with a Malvern particle analyzer. Both the pulverized coal and the pine sawdust were dried in air before the experiment started. The supply air velocity was from 10 to 15 m/s, and the air was heated to 45 °C. 4.1. Flow regime identification Different flow regimes, such as roping flow and stratified flow, were generated by the flow generator and identified through the HHT of the electrostatic fluctuation signal [23]. Fig. 5 shows the Hilbert marginal spectra of the two flow regimes. The Hilbert marginal spectrum provides a measure of total amplitude (or energy) contribution from each frequency value. It presents the cumulative amplitude over the entire data span. It is obvious that the ratio of energy embedded in different frequency bands varies with the change in flow regimes. Besides this, we also found that the amplitude of the roping flow is obviously higher than that of the stratified flow. This means that there are obvious
Test results Output of BP NN
Identification result
Roping flow (code: 0)
1 2 3 4
0.0493 0.1281 0.0738 −0.0728
T T T T
Stratified flow (code: 1)
1 2 3 4 5 6 7
0.7350 0.7452 0.7212 0.7062 0.6457 0.6981 0.6904
T T T T T T T
differences between the energies of the electrostatic signals with the flow regime variation. So it is reasonable and feasible to use the subband energy as the feature of the Hilbert marginal spectrum to identify the flow regime. First, the frequency range of Hilbert marginal spectrum from 0 to 100 Hz is divided into 10 subbands; the width of each subband is 10 Hz. Then the subband energies of the 68 groups of data are calculated by Eq. (7), including 28 roping flows and 40 stratified flows. The flow regime is identified by using a back propagation (BP) neural network. The input and output of the network are the subband energy of the electrostatic signal’s marginal spectrum and the flow regime. In order to describe the flow regime conveniently, roping flow and stratified flow are recorded as 0 and 1, respectively. The number of hidden layer neurons is 20, and the transfer function of the hidden layer and output layer are tansig and purelin, respectively. A total of 57 electrostatic signals were acquired to train the neural network, and 11 for the testing. The results are summarized in Table 1. When the output of the BP neural network is greater than 0.5, it is set to 1. Similarly, when it is less than 0.5, it is set to 0. In Table 1, T means that flow regime is identified correctly, while F means that it is identified falsely. The success rate in the flow regime identification based on Hilbert marginal spectrum is 100% (the 11 test samples includes four that were roping flow and seven that were stratified flow). This result has revealed an intrinsic and complicated relationship between the flow noise signal and the three-phase flow regime, and has indicated that the characteristics based on Hilbert marginal spectra represent the distribution of three-phase flow well. 4.2. Data fusion for solid volumetric-concentration measurement In the experiment, the calibration values of the solid concentration are adjusted by the two dischargers. So the quantity we can measure directly is the mass flow rate, i.e., the mass of the solid through the pipeline per second. Next, we will convert the mass flow rate of the pulverized coal mPC to pulverized coal volumetricconcentration. The volumes of the pulverized coal and the air through the pipeline per second are VPC =
Vair
mPC (g/s)
MPC
ρPC
1 × 103 (kg/m3 ) −6 × 10 (m3 /s)
t =
= mPC Mair = t = ν S = vπ r 2 (m3 /s), ρair
(15) (16)
where MPC and Mair are the mass of the pulverized coal and the mass of air through the pipeline in t seconds, mPC is the mass flow rate of pulverized coal, ρPC and ρair are the density of the pulverized
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J. Zhang et al. / Flow Measurement and Instrumentation 24 (2012) 43–49
Table 2 Calibration data for volumetric-concentration measurement (stratified flow). CB (∗0.1%) 4.5
7
9.5
12
1.1492 1.1544
2.1020 2.1758
3.2652 3.0869
4.0699 4.0052
4.8633 4.7805
0.8
Ue /V Uc /V
1.1857 1.1642
2.1399 2.2010
3.3020 3.1002
4.1098 4.0215
4.8998 4.7966
1.2
Ue /V Uc /V
1.2187 1.1689
2.1702 2.2158
3.3369 3.1201
4.1531 4.0289
4.9307 4.8021
1.6
Ue /V Uc /V
1.2478 1.1702
2.2015 2.2199
3.3638 3.1258
4.1812 4.0362
4.9657 4.8102
2.0
Ue /V Uc /V
1.2856 1.1726
2.2478 2.2236
3.3986 3.1425
4.2167 4.0587
4.9858 4.8169
coal and the density air, respectively (ρPC is 1 × 103 (kg/m3 ) here), ν is the supply air velocity, and S and r are the cross-sectional area and the radius of the pipeline, respectively. Then the volumetricconcentration of the pulverized coal CPC is
=
VPC + Vair
× 100% ≈
mPC × 10−6
v × πr2
VPC Vair
× 100%.
CB =
VB + Vair
= 2.5 ×
× 100% ≈
mB × 10−6
v × πr2
VB Vair
1.6 1.4 1.2 1 0.8 0.6 0.4
0
5
10
15
20
Test sample number 12
b
Actual value Estimated value
11
× 100%
10
(17)
Similarly, as the density of the biomass ρB is 0.4 ∗ 103 , the volumetric-concentration of the biomass CB can be given as follows: VB
Pulverized coal concentration (*0.1%)
2 Ue /V Uc /V
VPC
Actual value Estimated value
1.8
0.4
CPC =
2
× 100%
Biomass concentration (*0.1%)
CPC (∗0.1%)
a
9 8 7 6 5 4 3
× 100%.
(18)
Under certain identified flow regimes, the dual regression analysis method was used to perform data fusion for solid concentration measurements. In the experiment, the ranges of the coal volumetric-concentration and the biomass volumetricconcentration are 0.4–2(∗0.1%) and 2–12(∗0.1%), respectively. The calibration data are shown in Table 2, and here the data for stratified flow are taken as an example to show the process of the dual regression. The numbers of calibration points for coal and biomass are both 5. Using dual regression analysis, the resulting constant coefficients are shown in Table 3. Under certain experimental conditions, we can predict the coal volumetric-concentration and the biomass volumetric-concentration using (10) and (11) with the sensor output characteristics Ue and Uc , which are different from the values used in the calibration. For each test sample point (both the pulverized coal concentration and the biomass concentration), the experiment was repeated 20 times. The mean of the 20 values is regarded as an estimated value, and the estimated values of all the test samples are shown in Fig. 6. From Fig. 6, it can be seen that the estimated values agree with the actual values well for both the pulverized coal concentration and the biomass concentration. For the pulverized coal volume concentration measurement, the maximum error is 0.07 ∗ 0.1%, while for the biomass concentration it is 0.18∗0.1%. So the fiducial errors of pulverized coal and biomass measurement are 4.7% and 2.0%, respectively. The repeatability errors of the pulverized coal and the biomass concentration measurement are 4% and 2%. For the pulverized coal concentration, measurements were repeated 20 times with CPC being 2.0 ∗ 0.1%. The standard deviation in the pulverized coal concentration evaluated from the established model is 0.016 ∗ 0.1%, which corresponds to 1% resolution. For the biomass concentration,
2
0
5
10
15
20
25
Test sample number
Fig. 6. Direct comparison between the estimated value and the actual value: (a) pulverized coal concentration; (b) biomass concentration. (Air velocity: 12.7 m/s; flow regime: stratified flow.)
measurements were repeated 20 times with CB being 12 ∗ 0.1%. The standard deviation in the biomass concentration evaluated from the established model is 0.1 ∗ 0.1%, which corresponds to 1% resolution. 5. Conclusion An integrated instrumentation system for the concentration measurement of biomass/coal/air flow based on electrostatic and capacitive sensing methods has been described. The flow regime is identified from the Hilbert marginal spectra of the electrostatic sensor signal. Then the outputs of the electrostatic and capacitance sensors are fused using the dual regression method to determine the pulverized coal concentration and the biomass concentration. The experimental results have demonstrated that the method has fiducial error no greater than 5%, and the resolution is about 1%. In future work, the influence of other factors on the concentration measurement, such as particle size, humidity, and type of biomass and coal, will be taken into consideration. It is envisaged that the development of a concentration measurement technique would lead to the improvement of combustion efficiency and emission reduction. Acknowledgments The authors wish to express their gratitude to the National Natural Science Foundation of China (No. 51177120), and the 863 National High Technologies R&D Project of China (2009AA04Z130).
J. Zhang et al. / Flow Measurement and Instrumentation 24 (2012) 43–49
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Table 3 The values of the constant coefficients (stratified flow).
α0
α1
α2
α3
α4
α5
β0
β1
β2
β3
β4
β5
0.0093
0.1203
0.1832
0.8329
−1.7573
0.9485
−0.4420
0.7518
1.1453
5.2058
−10.9830
5.9281
Acknowledgments also are made to the RCUK’s Energy Programme for a grant (EP/F061307/1) in aid of this research, but the views expressed are those of the authors, and not necessarily those of the RCUK. The Energy Programme is an RCUK cross-council initiative led by EPSRC and contributed to by ESRC, NERC, BBSRC, and STFC in the UK. References [1] Tillman DA. Biomass cofiring: field test results. ReportTRll39O3. Palo Alto (CA): Electric Power Research Institute; 1999. [2] Beloševiæ S. Modeling approaches to predict biomass co-firing with pulverized coal. The Open Thermodynamics Journal 2010;4:50–70. [3] Beck MS, Green RG, Thorn R. Non-intrusive measurement of solid mass flow in pneumatic conveying. Journal of Physics 1987;20:835–40. [4] Rajan VS, Radley RK, Refa KG. Multiphase flow measurement techniques—a review. Journal of Energy Resources Technology 1993;115:151–61. [5] Yan Y. Flow rate measurement of bulk solids in pneumatic pipelines: problems and solutions. Bulk Solids Handling 1995;6(3):447–56. [6] Yan Y. Mass flow measurement of bulk solids in pneumatic pipelines. Measurement Science and Technology 1996;7(12):1687–706. [7] Carlson HM, Frazier PM, Engdahl RB. Meter for flowing mixtures of air and pulverized coal. Transactions of the ASME 1948;70:65–73. [8] Jin F, Xia J, Liu R, Wang S. Mathematical models of gas/solid two-phase mass flow-rate measurement using differential pressure concentration method. On Line Flow Measurement of Particulate Solids. UK: University of Greenwich; 1998. p. 133–7. [9] Lewis BM. Recent experience in Australia with the application of impact flowmeter. On Line Flow Measurement of Particulate Solids. UK: University of Greenwich; 1998. p. 90–8. [10] Malmgren M. Online measurement system for particulate solids using Coriolis principle. On Line Flow Measurement of Particulate Solids. UK: University of Greenwich; 1998. p. 126–32. [11] Benes P, Zehnula K. New design of the three-phase flowmeters. Sensors and Actuators 2000;86:220–5.
[12] Nieuwland JJ, Meijer R, Kuipers JA, Swaaij W. Measurement of solids concentration and axial solids velocity in gas–solids three-phase flows. Powder Technology 1996;87:127–39. [13] Xie CG, Stott AL, Plaskowski A, Beck MS. Design of capacitance electrodes for concentration measurement of two-phase flow. Measurement Science and Technology 1990;1:65–78. [14] Sen S, Das PK, Dutta PK, Maity B, Chaudhuri S, Mandal C, Roy SK. PC-based gas–solids two-phase mass flowmeter for pneumatically conveying systems. Flow Measurement and Instrumentation 2000;11:205–12. [15] Bailey AG. Charging of solids and powders. Journal of Electrostatics 1993;30: 167–80. [16] Huang NE, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London 1998;8. [17] Pegram GS, Peel MC, McMahon TA. Empirical mode decomposition using rational splines: an application to rainfall time series. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 2008. [18] Li H, Zhang YP, Zheng HQ. Hilbert–Huang transform and marginal spectrum for detection and diagnosis of localized defects in roller bearings. Journal of Mechanical Science and Technology 2009;23(2):291–301. [19] Hu HL, Xu TM, Hui SE. A novel capacitive system for concentration measurement of pneumatically conveyed pulverized fuel at power station. Flow Measurement and Instrumentation 2006;17(2):87–92. [20] Chen X, Hu HL, Liu F, Gao XX. Image reconstruction for an electrical capacitance tomography system based on a least-squares support vector machine and a self-adaptive particle swarm optimization algorithm. Measurement Science and Technology 2011;22. [21] Robert MC, Yan Y. An instrumentation system using combined sensing strategies for on-line mass flow rate measurement and particle sizing. In: Instrumentation and measurement technology conference. 2004. [22] Xu CL, Zhang J. Dense-phase pneumatically conveyed coal particle velocity measurement using electrostatic probes and cross correlation algorithm. Journal of Physics: Conference Series 2009;147. [23] Hu HL, Zhang J, Dong J, Luo ZY, Xu TM. Identification of gas–solid two-phase flow regimes using Hilbert–Huang transform and neural-network techniques. Instrumentation Science & Technology 2011;39(2):1–13.
Contents lists available at SciVerse ScienceDirect
Flow Measurement and Instrumentation journal homepage: www.elsevier.com/locate/flowmeasinst
Concentration measurement of biomass/coal/air three-phase flow by integrating electrostatic and capacitive sensors Juan Zhang a , Hongli Hu a,∗ , Jun Dong a , Yong Yan b,c a
State Key Laboratory of Electrical Insulation and Power Equipment, Xi’an Jiaotong University, Xi’an 710049, China
b
Centre for Multiphase Flow Metering and Combustion Process Monitoring, School of Control and Computer Engineering, North China Electric Power University, Changping District, Beijing 102206, China c
Instrumentation, Control and Embedded Systems Research Group, School of Engineering and Digital Arts, University of Kent, Canterbury, Kent CT2 7NT, UK
article
info
Article history: Received 8 March 2011 Received in revised form 23 February 2012 Accepted 22 March 2012 Keywords: Three-phase flow Concentration measurement Capacitance sensor Electrostatic sensor Flow regime identification Data fusion
abstract This paper describes an integrated instrumentation system for the volumetric-concentration measurement of biomass/coal/air three-phase flow in a pneumatic conveying pipeline. The system combines electrostatic sensors with capacitive sensors and incorporates data fusion techniques. As the electrostatic sensor is more sensitive to dilute pulverized coal and the capacitive sensor is more sensitive to biomass particles, both sensor techniques are integrated for the concentration measurement of biomass and pulverized coal in biomass/coal/air three-phase flow. First, the flow regime is identified through the Hilbert marginal spectrum of the electrostatic sensor output signal. Then, under certain identified flow regimes, the dual regression analysis method is applied to work out the biomass concentration and the pulverized coal concentration. The experimental result indicates that the fiducial error of the system is less than 5%, and the resolution is about 1%. © 2012 Elsevier Ltd. All rights reserved.
1. Introduction Pneumatic conveying of biomass/coal blends is widely seen in coal and biomass fired power stations. In 1979, the Bayfront plant in the US state of Wisconsin began to burn wood, railroad ties, and other raw materials with pulverized coal [1]. Since 1992, Electric Power Research Institute (EPRI) has carried out substantial research on the cyclone furnace and pulverized coal furnace firing biomass, focusing on the proportion of mixed fuel and biomass fuel characteristics on the combustion process [1]. Georgia Power Company is dedicated to commercial applications of biomass cocombustion and the feasibility of the implementation of mixed fuel in existing power plants [1]. The co-existence of coal, biomass, and air in a pneumatic conveying pipeline is in effect a type of threephase flow. However, there is little research in the concentration measurement of biomass/coal/air three-phase flow. Successful online measurement of biomass and coal concentration in fuel feed pipes would lead to the improvement of combustion efficiency and stability, and reduction of atmospheric pollution and corrosion of the combusting chamber [2]. The development of adequate instrumentation for this application poses a significant challenge
∗
Corresponding author. Tel.: +86 29 82668390; fax: +86 29 83237910. E-mail address: [email protected] (H. Hu).
0955-5986/$ – see front matter © 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.flowmeasinst.2012.03.003
to scientists and engineers due to the difficulty nature of the threephase flow and the variability of plant operation conditions. In particular, problems arise from the very low solid/air mass flux ratios and the cross-sectional distribution of inhomogeneous solids in the pipeline. Extensive research has been undertaken to develop solid flowmeters for the measurement of gas–solid two-phase flow. Possible techniques for the measurement of the mass flow rate of solids have been discussed in detail by Beck et al. [3], Rajan et al. [4], and Yan [5,6]. Sensing techniques for the solid concentration measurement are classified into four main categories according to the sensing principles: electrical, attenuation, nuclear magnetic resonance, and tomographic methods [5,6]. Carlson et al. [7] discussed the possibility of measurement of the phase concentration by means of an orifice plate and a Venturimeter, and suggested that the orifice plate should be installed in series with a Venturimeter to measure the phase concentration. Jin et al. [8] proposed a differential-pressure concentration method by combining a capacitance meter with a Venturimeter. They suggested the use of such meters for fine particles, but provided no experimental verification. Efforts have also been made to develop flowmeters using impact plates [9] and the Coriolis principle [10]. However, these meters cannot be directly installed on pipelines for on-line measurement. Benes and Zehnula [11] proposed a new design for a three-phase flowmeter based on the fact that solid particles being
44
J. Zhang et al. / Flow Measurement and Instrumentation 24 (2012) 43–49
Fig. 1. The HHT process.
carried by flowing air impacting a properly formed obstacle generate an acoustic surface wave. Nieuwland et al. [12] developed an optical system based on the detection of light reflected by suspended particles to measure the solid concentration and axial solid velocity in dense gas–solid three-phase flow. The above methods have been employed for measuring the concentration and velocity of solid particles, but the suitability and performance of such methods for the measurement of biomass and pulverized coal are unknown. Because of the physical differences between biomass and coal, the fluid dynamics of biomass/coal/air three-phase flow in the fuel lines is rather complex. Electrostatic and capacitive methods are widely recognized as the most promising solid flow measurement techniques [5,6]. Concentration measurements based on a combination of capacitive and electrostatic sensors have many advantages over other measurement techniques. Capacitive sensors are simple in structure, free of maintenance, there is no interference of the flow field, and there is direct output of an electric signal [13,14]. Although the structure of an electrostatic sensor is very similar to that of a capacitive sensor, there are fundamental differences between their sensing principles. Capacitance sensors are sensitive to the dielectric characteristics of solid particles while electrostatic sensors detect the electrostatic charge of moving particles. In general, electrostatic sensors have advantages of high sensitivity to dilute phase flow, simple structure, low cost, and nonintrusiveness in installation [15]. In this paper, data fusion of capacitive and electrostatic sensors is used to measure the concentration of biomass and coal in pneumatic pipelines. The flow regime was first identified by performing a Hilbert–Huang transform (HHT) on the electrostatic fluctuation signals, thus making the solid concentration measurement less affected by variations in the flow regimes. Then, under certain identified flow regimes, the dual regression analysis method was applied to determine the solid concentration. 2. Hilbert–Huang transform and dual regression analysis 2.1. Hilbert–Huang transform and feature extraction Huang et al. [16] introduced a general signal analysis technique called the Hilbert–Huang transform (HHT) to efficiently extract information in both the time and the frequency domains directly from data. The HHT process is illustrated in Fig. 1. It is a two-step algorithm, combining empirical mode decomposition (EMD) and Hilbert spectral analysis, to accommodate the nonlinear and nonstationary process. So it is appropriate to process the electrostatic fluctuation signal with an HHT. This method is not based on a priori selection of kernel functions; instead, it decomposes the signal into intrinsic mode functions (IMFs) derived from the succession of extrema. It is adaptive, efficient, and without any prior assumptions [17,18]. The electrostatic fluctuation signal x(t ) is decomposed into a finite number (n) IMFs, ci (t ), i = 1, 2, . . . , n, and the residual r (t ): x(t ) =
n
ci (t ) + r (t ).
(1)
i=1
Then the Hilbert transform is applied to each IMF to obtain a complex representation of the IMF, di (t ): di ( t ) =
1
π
∞ −∞
ci (t ) t −τ
dτ .
(2)
After that, ci (t ) and di (t ) form a complex conjugate pair that defines an analytical signal, zi (t ): zi (t ) = ci (t ) + jdi (t ) = ai (t )ejθi (t ) ,
(3)
where ai (t ) and θi (t ) are the amplitude and phase angle, respectively. Finally, the instantaneous frequency of each IMF, ωi (t ), is defined as dθi (t )
. (4) dt At a given time t, the instantaneous frequency ωi (t ) and the amplitude ai (t ) are calculated simultaneously, and these values are assigned to the Hilbert spectrum, H (ω, t ). ωi (t ) =
H (ω, t ) =
Re ai (t )ej ωi (t )dt
ωi (t ) = ω others.
0
(5)
With the Hilbert spectrum defined, we can also define the Hilbert marginal spectrum, h(ω), as h(ω) =
T
H (ω, t )dt ,
(6)
0
where T is the total data length. The following steps are taken to extract the features of the electrostatic signal Hilbert marginal spectrum for flow regime identification. First, the frequency band of the Hilbert marginal spectrum is divided into Y subbands. Then, the energy of each subband is calculated according to SEi =
N
h(ωj )2
i = 1, 2, . . . , Y ,
(7)
j =1
where SEi is the energy of the ith subband, Y is the total number of subbands, and N is the number of discrete frequency points of each subband. Finally, the subband energies of the electrostatic signal Hilbert marginal spectra of different flow regimes under various experimental conditions are input into a neural network to identify the flow regimes; this will be discussed in Section 4.1. 2.2. Dual regression analysis The aim of this study is to measure two physical quantities. They are pulverized coal volumetric-concentration CPC and biomass volumetric-concentration CB , with a capacitive sensor and an electrostatic sensor. In fact, there are inherent connections between the coal volumetric-concentration CPC and the characteristics of the two sensor outputs Ue and Uc . Ue and Uc are the true RMS (rootmean-square) value of the electrostatic signal and the mean value of the capacitance signal, respectively. The relationship can be described by the following formula: CPC = f (Ue , Uc ).
(8)
Similarly, the relationship between the biomass volumetricconcentration and the two signal characteristics can be described by CB = f (Ue , Uc ).
(9)
According to the theory of dual regression analysis, the specific descriptions of formulas (8) and (9) are as follows. CPC = α0 + α1 Ue + α2 Uc + α3 Ue2 + α4 Ue Uc + α5 Uc2 + ε1
(10)
CB = β0 + β1 Ue + β2 Uc + β
(11)
2 3 Ue
+ β4 Ue Uc + β
2 5 Uc
+ ε2 ,
J. Zhang et al. / Flow Measurement and Instrumentation 24 (2012) 43–49
45
where α0 to α5 and β0 to β5 are constant coefficients of the regression equations, while ε1 and ε2 are infinitesimals of higher order which can be ignored. If the constant coefficients are known, the regression equations, which can be used to predict the coal volumetric-concentration and biomass volumetric-concentration, are fixed. When the output characteristics are obtained, we can calculate the two volumetric-concentration values through (10) and (11). So calibration experiments must be performed first, and then the constant coefficients will be computed by using the leastsquares method with the calibration data. It is supposed that the numbers of the coal volumetricconcentration calibration points and biomass volumetricconcentration calibration points are n and m, respectively. The calibration points are CPC i (i = 1, 2, . . . , n) in its range and CBj (j = 1, 2, . . . , m) in its range. For each coal volumetric-concentration calibration point, m calibration experiments are performed with m different biomass volumetric-concentration values. So m ∗ n experiments are done, and m ∗ n groups of output characteristics (Uek , Uck ), k = 1, 2, . . . , m ∗ n, are obtained. According to the least-squares method, the constant coefficients obtained must meet the least-squares errors condition. Because the methods for obtaining α0 to α5 and β0 to β5 are similar, the method is presented for the constant coefficients α0 to α5 as an example. The square error between the actual value of the coal volumetric-concentration and the value calculated by the regression equation (10) is
∆2k = [CPC k − CPC (Uek , Uck )]2 k = 1, 2, . . . , m ∗ n,
(12)
where ∆2k is the coal volumetric-concentration square error of the kth calibration experiment. The mean-square error of the m ∗ n coal volumetric-concentration experiment is R1 =
1
m∗n [CPC k − (α0 + α1 Uek
m ∗ n k=1
2 2 + α2 Uck + α3 Uek + α4 Uek Uck + α5 Uck )]2
= R1 (α0 , α1 , α2 , α3 , α4 , α5 ).
∂ R1 = 0; ∂α1 ∂ R1 = 0; ∂α4
∂ R1 = 0; ∂α2 ∂ R1 = 0. ∂α5
3.1. Carrier air supply system Under the influence of the blower (10), air is blown into the conduit. The air supply velocity is controlled with a control valve (2). The rate and pressure of the air supply are measured by means of a counter blow type back-supported tube before entering the T-mixer (T). 3.2. Feeding systems
(13)
From the formula above, we can see that the mean-square error R1 is dependent on α0 to α5 . In order to minimize R1 , the constant coefficients should meet the conditions below:
∂ R1 = 0; ∂α0 ∂ R1 = 0; ∂α3
Fig. 2. Schematic diagram of a pneumatic conveying system.
(14)
Using the coal volumetric-concentration calibration points CPC k and the sensor output characteristics Uek and Uck , the equation set (14) is solved and the constant coefficients α0 to α5 are obtained. Similarly, the constant coefficients β0 to β5 can also be obtained. Then we can predict the coal volumetric-concentration and the biomass volumetric-concentration by (10) and (11) with the sensor output characteristics Ue and Uc which are different from the values used in the calibration. 3. The experimental system The experimental system that was used in this research is shown in Fig. 2 [19]. It has been designed and developed to facilitate the concentration measurement of solid phase in a pneumatic conveying line. It comprises the five major subsystems described in the following sections.
In the experimental facility, there are two feeding systems. One is for pulverized coal, and the other is for biomass. Each feeding system consists of a motor, a continuously adjustable gearbox, a booster fan, and a fine linear screw feeder. The feeding mass flow rate ranges from 30 g/min to 3 kg/min. The feeding systems are more stable and continuous than that adjusted by a silicon controlled rectifier (SCR). With the action of the booster fan, the feeding systems adjust the amount of discharging smoothly and accurately, and they are regarded as the calibration values in data fusion which determine the pulverized coal and biomass concentrations exactly. The mixed media are separated by a cyclone separator (8), and then collected by the collectors (9). The calibration values given by the feeding systems are more accurate than those by the collectors, because the two kinds of media cannot be separated by the cyclone separator completely. A pressure of −20 mm H2 O produced by the baffle and adjustable positive pressure by the booster fan make conveying the biomass and pulverized coal blend unhindered. The biomass and pulverized coal are both stored in a cylindrical steel discharging bin before being conveyed in the pipeline. The bottom end of the conical section is connected to a T-mixer through a short flexible connecting pipe. 3.3. Test section The test section is a vertical ceramic pipe of length 1200 mm and inner diameter the same as the conveying pipe, with an upward flow direction. As shown in Fig. 1, SC is the capacitive sensor, and SE is the electrostatic sensor. The test section is placed downstream of
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J. Zhang et al. / Flow Measurement and Instrumentation 24 (2012) 43–49
the T-mixer. The capacitive sensor for the measurement of biomass concentration is mounted at a distance of 800 mm from the pipe’s lower terminal, and the electrostatic sensor for the measurement of pulverized coal concentration is mounted in tandem with the capacitance sensor. The two sensors are connected to the signal conditioning circuit via a shielded twisted-pair cable. The construction and the dimensions of the sensors will be given later. Heaters (1) and (5) respectively are used to heat the air flow and the three-phase flow. Resistance temperature detectors (RTDs) (4) and (6) are used to measure the temperature. 3.4. Flow regime generator In the upstream of the two sensors there exists a substitutable throttle which is specially manufactured as a flow regime generator [20]. A transition flow regime is generated a short distance downstream of the throttle. For example, a stratified flow can be generated behind a gate valve, and a roping flow behind a Venturi tube or an orifice plate. The upstream edge of the throttle is especially designed to be streamlined to avoid powder accumulation, and the equivalent pore diameter of the throttle is about one tenth of its length. In the present work, two different throttles were used to generate roping flow and stratified flow regimes. It should be mentioned that, before every experiment test starts with a particular flow regime, the system is operated by a solenoid valve from no-flow to short-time flow conditions several times with pulverized coal as the conveying medium, and the flow regime can be observed by scenes projected on a screen mounted downstream of the throttle. Once a satisfactory flow regime is observed under several different conditions, the screen is then removed and the sensors are installed downstream of the throttle in order to detect the transition flow regime. 3.5. Start-up, operation, and measurement Conveying air is generated by turning on the blower. The air velocity is adjusted by a back-supported tube (3) and controlled to expectation level by regulating the valve (2) at the same time. The feeder is turned on before the feeding rate can be controlled to desired level by regulating the gearbox. Then, the fuel particles are fed to the T-mixer and they create a three-phase flow of which the concentration and velocity can be controlled through regulating the gearbox of the feeder and the valve (2), respectively. The capacitance sensing electrodes for the pneumatically conveyed biomass and coal blend should be critically designed so that the configuration can provide high and homogeneous capacitance sensitivity over the pipe cross-section, thus making the component concentration measurement less affected by variation of the flow regime. Meanwhile, cable capacitance connecting the sensor, parasitic capacitance, electromagnetic interference, leakage of weak signal, and circuit drift are all factors that may have an influence on the measurements. Efforts have been made to develop finite-element-based models and to carry out extensive simulation studies from which suggested dimensions of the electrodes for optimum and uniform sensitivity have been obtained [13]. However, practical considerations often prevent the use of the optimum design parameters [14]. Through the comparison of several electrode types, source–grid sensing electrodes have been proposed in this research. A group of stainless steel electrodes is distributed uniformly on the test pipe wall. An equivalent two-electrode capacitance is formed with the above electrodes in parallel. The optimum design parameters are as follows: the ratio of electrode width W to electrode separation d is 4:1, the number of electrodes K = 28, the electrode length L = 100 mm, and the radius of the pipe R1 = 100 mm, as shown in Fig. 3(b). Simulation studies have shown that above electrode
Fig. 3. Schematic diagram of the capacitance sensor. (a) Developed representation. (b) Cross-section.
configuration can provide a capacitance sensitivity over the pipe cross-section with inhomogeneity less than 3%. Insulating material is wrapped over the electrodes over which another thin stainless steel plate is glued to form the inner screen. A supporting steel tube outside of the inner screen acts as the outer screen. A follower is connected between the inner and outer screens, which forms equal potential between the screens. This specific design is used for preventing leakage of weak signal and eliminating the effect of electromagnetic interference and stay capacitance, as shown in Fig. 3. The two sensor plates and the screens are connected to a shielded twisted-pair cable. A protecting steel tube outside the shielded twisted-pair cable acts as the outer screen. This sensor forms the active sensor, while an identical sensor made on a similar tube forms the dummy. A differential capacitance transducer consisting of the active sensor and the dummy is formed to increase the detection sensitivity and to reduce the effects of temperature, stray parameters, and electromagnetic interference. The lengths of the shielded cables in the two cases are kept exactly the same to ensure that the nominal capacitance remains the same. During pneumatic transportation, particles carry certain net electrostatic charge due to frictional contact between particles and the pipe wall and between particles and the air stream. An electrostatic sensor can be used to detect the charge carried by the moving particles [21,22]. The construction of the electrostatic sensor is shown in Fig. 4. The inner diameter of the tested ceramic pipe R1 is 100 mm, and the outer diameter R2 is 105 mm. The electrostatic sensor is made of copper sheet, and the axial length (We) and the thickness of the sensor are 10 mm and 0.2 mm, respectively. The inner screen is also made of copper sheet. Its inner diameter R3 is 120 mm, the axial length l is 50 mm, and the thickness is 2 mm. A supporting steel tube outside the inner screen acts as the outer screen. A follower is connected between the inner and outer screens, which forms an equal potential between
J. Zhang et al. / Flow Measurement and Instrumentation 24 (2012) 43–49
47
Table 1 Flow regime identification results. Real flow regimes
Fig. 4. Construction of the electrostatic sensor.
Hilbert marginal spectrums of two flow regimes 0.045 roping flow stratified flow
0.04 0.035
Amplitude
0.03 0.025 0.02 0.015 0.01 0.005 0
0
20
40
60
80
100
Instantaneous frequency (Hz)
Fig. 5. Hilbert marginal spectra of the two flow regimes.
the screens. This sensor design is used to prevent leakage of weak signal and to eliminate the effect of electromagnetic interference. 4. Experimental results and discussion Experiments were carried out with pine sawdust and Shenmu pulverized coal. The diameter of the pulverized coal particles was less than 50 µm, and the length of the pine sawdust ranged from 0.1 to 1.2 mm. These particle sizing data were obtained with a Malvern particle analyzer. Both the pulverized coal and the pine sawdust were dried in air before the experiment started. The supply air velocity was from 10 to 15 m/s, and the air was heated to 45 °C. 4.1. Flow regime identification Different flow regimes, such as roping flow and stratified flow, were generated by the flow generator and identified through the HHT of the electrostatic fluctuation signal [23]. Fig. 5 shows the Hilbert marginal spectra of the two flow regimes. The Hilbert marginal spectrum provides a measure of total amplitude (or energy) contribution from each frequency value. It presents the cumulative amplitude over the entire data span. It is obvious that the ratio of energy embedded in different frequency bands varies with the change in flow regimes. Besides this, we also found that the amplitude of the roping flow is obviously higher than that of the stratified flow. This means that there are obvious
Test results Output of BP NN
Identification result
Roping flow (code: 0)
1 2 3 4
0.0493 0.1281 0.0738 −0.0728
T T T T
Stratified flow (code: 1)
1 2 3 4 5 6 7
0.7350 0.7452 0.7212 0.7062 0.6457 0.6981 0.6904
T T T T T T T
differences between the energies of the electrostatic signals with the flow regime variation. So it is reasonable and feasible to use the subband energy as the feature of the Hilbert marginal spectrum to identify the flow regime. First, the frequency range of Hilbert marginal spectrum from 0 to 100 Hz is divided into 10 subbands; the width of each subband is 10 Hz. Then the subband energies of the 68 groups of data are calculated by Eq. (7), including 28 roping flows and 40 stratified flows. The flow regime is identified by using a back propagation (BP) neural network. The input and output of the network are the subband energy of the electrostatic signal’s marginal spectrum and the flow regime. In order to describe the flow regime conveniently, roping flow and stratified flow are recorded as 0 and 1, respectively. The number of hidden layer neurons is 20, and the transfer function of the hidden layer and output layer are tansig and purelin, respectively. A total of 57 electrostatic signals were acquired to train the neural network, and 11 for the testing. The results are summarized in Table 1. When the output of the BP neural network is greater than 0.5, it is set to 1. Similarly, when it is less than 0.5, it is set to 0. In Table 1, T means that flow regime is identified correctly, while F means that it is identified falsely. The success rate in the flow regime identification based on Hilbert marginal spectrum is 100% (the 11 test samples includes four that were roping flow and seven that were stratified flow). This result has revealed an intrinsic and complicated relationship between the flow noise signal and the three-phase flow regime, and has indicated that the characteristics based on Hilbert marginal spectra represent the distribution of three-phase flow well. 4.2. Data fusion for solid volumetric-concentration measurement In the experiment, the calibration values of the solid concentration are adjusted by the two dischargers. So the quantity we can measure directly is the mass flow rate, i.e., the mass of the solid through the pipeline per second. Next, we will convert the mass flow rate of the pulverized coal mPC to pulverized coal volumetricconcentration. The volumes of the pulverized coal and the air through the pipeline per second are VPC =
Vair
mPC (g/s)
MPC
ρPC
1 × 103 (kg/m3 ) −6 × 10 (m3 /s)
t =
= mPC Mair = t = ν S = vπ r 2 (m3 /s), ρair
(15) (16)
where MPC and Mair are the mass of the pulverized coal and the mass of air through the pipeline in t seconds, mPC is the mass flow rate of pulverized coal, ρPC and ρair are the density of the pulverized
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J. Zhang et al. / Flow Measurement and Instrumentation 24 (2012) 43–49
Table 2 Calibration data for volumetric-concentration measurement (stratified flow). CB (∗0.1%) 4.5
7
9.5
12
1.1492 1.1544
2.1020 2.1758
3.2652 3.0869
4.0699 4.0052
4.8633 4.7805
0.8
Ue /V Uc /V
1.1857 1.1642
2.1399 2.2010
3.3020 3.1002
4.1098 4.0215
4.8998 4.7966
1.2
Ue /V Uc /V
1.2187 1.1689
2.1702 2.2158
3.3369 3.1201
4.1531 4.0289
4.9307 4.8021
1.6
Ue /V Uc /V
1.2478 1.1702
2.2015 2.2199
3.3638 3.1258
4.1812 4.0362
4.9657 4.8102
2.0
Ue /V Uc /V
1.2856 1.1726
2.2478 2.2236
3.3986 3.1425
4.2167 4.0587
4.9858 4.8169
coal and the density air, respectively (ρPC is 1 × 103 (kg/m3 ) here), ν is the supply air velocity, and S and r are the cross-sectional area and the radius of the pipeline, respectively. Then the volumetricconcentration of the pulverized coal CPC is
=
VPC + Vair
× 100% ≈
mPC × 10−6
v × πr2
VPC Vair
× 100%.
CB =
VB + Vair
= 2.5 ×
× 100% ≈
mB × 10−6
v × πr2
VB Vair
1.6 1.4 1.2 1 0.8 0.6 0.4
0
5
10
15
20
Test sample number 12
b
Actual value Estimated value
11
× 100%
10
(17)
Similarly, as the density of the biomass ρB is 0.4 ∗ 103 , the volumetric-concentration of the biomass CB can be given as follows: VB
Pulverized coal concentration (*0.1%)
2 Ue /V Uc /V
VPC
Actual value Estimated value
1.8
0.4
CPC =
2
× 100%
Biomass concentration (*0.1%)
CPC (∗0.1%)
a
9 8 7 6 5 4 3
× 100%.
(18)
Under certain identified flow regimes, the dual regression analysis method was used to perform data fusion for solid concentration measurements. In the experiment, the ranges of the coal volumetric-concentration and the biomass volumetricconcentration are 0.4–2(∗0.1%) and 2–12(∗0.1%), respectively. The calibration data are shown in Table 2, and here the data for stratified flow are taken as an example to show the process of the dual regression. The numbers of calibration points for coal and biomass are both 5. Using dual regression analysis, the resulting constant coefficients are shown in Table 3. Under certain experimental conditions, we can predict the coal volumetric-concentration and the biomass volumetric-concentration using (10) and (11) with the sensor output characteristics Ue and Uc , which are different from the values used in the calibration. For each test sample point (both the pulverized coal concentration and the biomass concentration), the experiment was repeated 20 times. The mean of the 20 values is regarded as an estimated value, and the estimated values of all the test samples are shown in Fig. 6. From Fig. 6, it can be seen that the estimated values agree with the actual values well for both the pulverized coal concentration and the biomass concentration. For the pulverized coal volume concentration measurement, the maximum error is 0.07 ∗ 0.1%, while for the biomass concentration it is 0.18∗0.1%. So the fiducial errors of pulverized coal and biomass measurement are 4.7% and 2.0%, respectively. The repeatability errors of the pulverized coal and the biomass concentration measurement are 4% and 2%. For the pulverized coal concentration, measurements were repeated 20 times with CPC being 2.0 ∗ 0.1%. The standard deviation in the pulverized coal concentration evaluated from the established model is 0.016 ∗ 0.1%, which corresponds to 1% resolution. For the biomass concentration,
2
0
5
10
15
20
25
Test sample number
Fig. 6. Direct comparison between the estimated value and the actual value: (a) pulverized coal concentration; (b) biomass concentration. (Air velocity: 12.7 m/s; flow regime: stratified flow.)
measurements were repeated 20 times with CB being 12 ∗ 0.1%. The standard deviation in the biomass concentration evaluated from the established model is 0.1 ∗ 0.1%, which corresponds to 1% resolution. 5. Conclusion An integrated instrumentation system for the concentration measurement of biomass/coal/air flow based on electrostatic and capacitive sensing methods has been described. The flow regime is identified from the Hilbert marginal spectra of the electrostatic sensor signal. Then the outputs of the electrostatic and capacitance sensors are fused using the dual regression method to determine the pulverized coal concentration and the biomass concentration. The experimental results have demonstrated that the method has fiducial error no greater than 5%, and the resolution is about 1%. In future work, the influence of other factors on the concentration measurement, such as particle size, humidity, and type of biomass and coal, will be taken into consideration. It is envisaged that the development of a concentration measurement technique would lead to the improvement of combustion efficiency and emission reduction. Acknowledgments The authors wish to express their gratitude to the National Natural Science Foundation of China (No. 51177120), and the 863 National High Technologies R&D Project of China (2009AA04Z130).
J. Zhang et al. / Flow Measurement and Instrumentation 24 (2012) 43–49
49
Table 3 The values of the constant coefficients (stratified flow).
α0
α1
α2
α3
α4
α5
β0
β1
β2
β3
β4
β5
0.0093
0.1203
0.1832
0.8329
−1.7573
0.9485
−0.4420
0.7518
1.1453
5.2058
−10.9830
5.9281
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