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Acoustic monitoring of pipeline flows: particulate slurries
Powder Technology 106 Ž1999. 30–36 www.elsevier.comrlocaterpowtec
Acoustic monitoring of pipeline flows: particulate slurries R. Hou a
a,)
, A. Hunt b, R.A. Williams
a
Camborne School of Mines, UniÕersity of Exeter, Redruth, Cornwall TR15 3SE, UK b Schlumberger Cambridge Research, Madingley Road, Cambridge CB3 0EL, UK
Received 27 October 1997; received in revised form 19 February 1999; accepted 19 February 1999
Abstract This paper presents a case study of the use of a non-intrusive passive acoustic sensor for on-line monitoring of flow and related process parameters. The sensor was mounted externally on a small diameter pipeline conveying dense slurries of fine silica particles. Origins of the acoustic emissions in the pipeline are discussed, particularly, with reference to interactions with flow parameters of interest in process control. The statistical and spectral characteristics of the collected acoustic signal for different experimental conditions were utilized to build quantitative models to infer parameters such as solid concentration, volume flowrate and mass flowrate. The results indicate that passive acoustic signals can be a viable tool for on-line monitoring of slurry flows. q 1999 Elsevier Science S.A. All rights reserved. Keywords: Hydraulic conveying; Passive acoustic monitoring; Signal processing; Multivariate modelling
1. Introduction Pipelines are ubiquitous in various industrial processes. The operating conditions prevailing in a pipeline flow for different applications may vary widely. The line pressure, for instance, can be as low as just a few bar in water transportation, to as high as up to 1000 bar in slurry conveying operations. In addition, characteristics of the fluids may range from clean water to highly abrasive cement slurries, viscous gel suspensions, or erosive and dangerous chemical solutions. Such diversity can make accurate flow measurement and on-line diagnosis and monitoring of such processes extremely difficult. Some typical limitations are considered below. In flowrate measurement, for example, none of the commercially-available flowmeters being used in practice is able to operate independent of the properties of the fluid. The performance of turbine flowmeters can be seriously hampered by the viscosity changes and the presence of solid particles in the flow. Similar degradation also happens when vortex shedding flowmeters or differential pressure devices are employed. The widely applied electromagnetic flowmeter principle becomes inoperable when the conductivity of the fluid drops below 10y4 Srm w1x.
)
Corresponding author
Although various cross correlation flowmeters Žsuch as electrostatic, ultrasonic. have been reported to be more universal, their application to date has been limited since implementation of the technology seems to require fairly complicated instrumentation and calibration w2x. Similar problems are also encountered in attempts to monitor particle size and other characteristics and solid concentrations dispersed in a slurry pipeline. A plethora of different sensing methods is available w3x. Some frequently used methods include radioactive probe, optical techniques and ultrasound transmission measurements. These methods unfortunately can be cumbersome to maintain, and the measurement accuracy may also be seriously affected by the specific process environment changes such as viscosity, pressure, solution chemistry, local sound celerity. The consequence is therefore that in many industrial processes, the desire to implement better process and quality control strategies and achieve a better understanding of the operation is often impeded by the lack of appropriate measuring instrumentation. One route to the alleviation of the situation relies on the provision of more reliable and resilient sensing methodologies which are capable of performing accurate and independent real time measurements. In addition, the practical implementation of the sensors should preferably also have little or no interference with the process being monitored and should demand little maintenance. A few techniques have been proposed
0032-5910r99r$ - see front matter q 1999 Elsevier Science S.A. All rights reserved. PII: S 0 0 3 2 - 5 9 1 0 Ž 9 9 . 0 0 0 5 1 - 0
R. Hou et al.r Powder Technology 106 (1999) 30–36
31
ments. So far, a number of successful applications of this method in various particulate related industries have been reported w9–12x. In this paper, we consider a systematic investigation of using the passive acoustic monitoring technique to monitor pumping of fine silica particle suspensions running in a 44.5-mm diameter pipe. The frequency spectral behaviour of the collected acoustic emission signal is correlated with respect to the process parameters of the system. The physical significance of the origin of the modulation of the natural acoustic emission signal is discussed. Fig. 1. Configuration of test rig.
2. Experimental to meet these challenges based on mechanical w4x, acoustic w5x, laser w6x and electric capacitance w7x methods. We are concerned in this paper with passive acoustic monitoring w8x which relies on the ‘natural’ acoustic noise produced by fluid flowing inside a pipeline. The method uses multivariate statistical analysis techniques to infer process related information. In essence, the monitoring technique exploits the fact that when particulate slurries or solid particles interact with the inner surface of a pipeline, the resulting sound waves will generate impulses in the vicinity of the collision, causing the bulk of the pipeline to oscillate. The oscillation signal can then be collected through a clamp-on piezoelectric acoustic sensor fixed to the exterior of the pipeline. This signal is transformed into a suitable electrical signal form Žvoltage or current. for further analysis. Since the main contributing source of the system comes from the striking momentum of the particles or suspensions, the amplitude of the recorded signal is expected to be sensitive to variations in physical characteristics of the flow, such as solid concentration, flowrate, density, viscosity. Therefore, by using an appropriate statistical analysis technique, the relationship between the flow property and recorded signal can be established. Hence, in principle the technique offers a both convenient and non-intrusive way of implementing on-line measure-
2.1. Flow rig and experimental materials The test loop ŽFig. 1. consisted of a positive displacement pump Žmodel CMD80B4, Mono Pumps., standard 44.5 mm steel piping, a 300-l circulating tank and ball valves. Flowrate changes can be made by adjusting the back pressure on the pump by means of bypass valve setting. A three-phase 7.5 kW converter Žmodel 3G3IVA4075-EVI, Omaron, Japan. was also employed so that the rotational speed of the pump could be changed by varying the operating frequency of the power input to the pump motor. A fine commercial silica flour product ŽHPF-2 from Hepworth Minerals and Chemicals., with an average particle size Ž d 50 . of 13 mm was used as the experiment material throughout the test. The experiment was carried out in a factorial design fashion, with a total of four levels of solid concentrations ranging from 10 to 40 wt.% Žcorresponding to solid volume fraction of 0.04 to 0.2. and three different volume flowrate values at each solid concentration level being investigated. The volume flowrate was measured to "0.1 lrs by sampling the stream and weighing a known collected volume off-line. Detailed experimental conditions are listed in Table 1. All the experiments were carried out at room temperature.
Table 1 Event numbers and experimental conditions Event number a
Solid concentration Žwt.%.
Mass flowrate Žkgrs.
Volume flowrate Žlrs.
1–10 11–20 21–30 31–40 41–50 51–60 61–70 71–80 81–90 91–100 101–110 111–120
10.0 10.0 10.0 20.0 20.0 20.0 30.0 30.0 30.0 40.0 40.0 40.0
0.7 0.8 0.9 1.7 1.8 2.0 2.5 2.8 3.0 3.7 3.9 4.3
6.6 7.3 8.4 7.1 7.8 8.3 6.9 7.8 8.2 7.1 7.4 7.9
a
Each event corresponds to a duration of 16 s of data collection.
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R. Hou et al.r Powder Technology 106 (1999) 30–36
Fig. 2. General arrangement of signal processing system.
2.2. Acoustic sensors, signal and data processing A single 190-kHz piezoelectric type acoustic sensor Žmodel NS2002, AV Technology. was used in the experiments. The sensor was fixed with an epoxy adhesive ŽPermabond. onto the first right angle bend of the pipeline downstream of the pump outlet ŽFig. 1.. This location was selected from preliminary tests which showed that the highest signal-to-noise ratio can be obtained. The signal processing system consists of a pre-amplifier, signal conditioning unit, control PC with an analogue to digital conversion card, and necessary data storage facilities. Fig. 2 shows the general arrangement of the system and full details of other signal processing hardware involved in the experiment and the subsequent data acquisition procedures have been given elsewhere w10x. The signal was first amplified locally and then fed into the signal conditioning unit for demodulation and filtering. After that the signal was channelled to the ADC card and digitized at a sampling rate of 2000 Hz. Finally a 512-point fast fourier transform ŽFFT. algorithm with Hanning window averaging and 2r3 section overlapping strategy was initiated to transform the recorded signal from time domain into frequency domain to reveal any detailed spectral characteristics of the signal. A typical power density spectrum structure over the full frequency range of the collected signals is illustrated in Fig. 3. It is seen that all the useful informations appear to fall in the frequency range below 400 Hz and predomi-
Fig. 3. Typical power density spectrum structure in full frequency range.
nately 0–200 Hz. The 2000-Hz data sampling rate can, therefore, be well justified. For each experiment condition, a total of 160-s duration of data were collected. They were then divided into 10 independent ‘event’ sections, each event representing a measurement period of 16-s data. To provide good representative and statistical accuracy, the averages of these 10 events were used in this paper as the results for a specified experiment condition.
3. Results and discussions Typical energy vs. frequency spectra showing the effect of volume flowrates at solid concentration of 10 and 20 wt.% are illustrated in Fig. 4 and Fig. 5, respectively. Similar results with different volume flowrates were also observed at solid concentrations of 30 and 40 wt.%, as detailed elsewhere w8x. Figs. 4 and 5 clearly indicate that the signal spectral characteristics are rather sensitive to both solid concentration and volume flowrate changes. One closer inspection it became evident that the spectral peaks are actually occurring at regular frequency intervals. This implies that there is possibly only one basic frequency component in the spectra. The multiple peaks are merely the harmonics of this basic frequency component. In these tests, the main contributing source of the acoustic signal apparently is the oscillation of the pipeline hardware as imposed by the prevailing flow slurry. Since the operation of the displacement pump is the only driving force to the flow, it was assumed that the frequency of the spectrum should be closely related to the pulsating rate of the flow. A distinctive operating feature of this type of pump is that it generally causes the flow to pulsate at the frequency of pump rotation.
Fig. 4. Effect of volume flowrate on the power density spectrum at solid concentration of 10 wt.%.
R. Hou et al.r Powder Technology 106 (1999) 30–36
Fig. 5. Effect of volume flowrate on the power density spectrum at solid concentration of 20 wt.%.
To confirm this assumption, the frequency of the mains supplying the pump was adjusted using a three phase converter. This subsequently varied the rotation speed of the pump. A digital tachometer was then employed to read off the pump rotation speed at a specific mains frequency. Fig. 6 shows the frequency spectra of the signal when the mains frequency was set at 40, 50 and 60 Hz, respectively. It can be seen from the figure that increasing mains frequency not only causes the spectrum to shift towards left Žlower frequencies., but also results in a steady increase of the underlying characteristic frequency of the spectrum. The latter was estimated by measuring the interval between two consecutive peaks in the spectra namely: 10, 12.5 and 15 Hz when the mains frequencies were 40, 50 and 60 Hz, respectively. Moreover, these measurements were, as expected, found to be exactly the same as the pump rotation speed values which were read off from the tachometer as 600, 750 and 900 rpm for each corresponding mains frequency. In summary, it appears that the frequency spectrum of the signal is actually a function of the imposed pulsation, modulated by the flow parameters in the vicinity of the sensor mounting position. The magnitude of the acceleration experienced by the sensor is likely to be a function of the flow momentum, solids concentration and flowrate. Hence the analysis of variations in the amplitude of characteristic spectral peaks provides a means of monitoring, indirectly, the properties of the slurry.
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the flow and the statistical and spectral characteristics of the signal. The selection of the variable entries in the model was based on the statistical significance of the partial F-test result set at 95% confidence level, with the multiple correlation coefficient R 2 being used to judge the model performance. As the implementation of the technique requires two independent data sets: one to develop the models Žtraining set. and the other to validate Žvalidation set., we decided to split up the 10 events data collected at each experimental condition into two equal parts: the first five events being compiled into the training set and the remaining five into the validation set. This results in a training and validation set each having 60 events of data. The data set is arranged in a ‘row oriented’ format, with each row representing a particular test event and each column a specific predictor variable. Since preliminary tests revealed that some time domain statistical characteristic values of the signal Žmaximum, minimum, and rootmean-squares ŽRMS. etc.., also exhibited a certain degree of sensitivity to the experimental condition variations, it was then decided that both the time-domain and frequency-domain information of the recorded signal should be included in the training or validation data set. The final data matrix consisted of following column entries. Ø Columns 1–7 hold the seven statistical parameters of the signal in the time-domain, including values for the maximum ŽMax., minimum ŽMin., mean ŽMean., standard deviation ŽSTD., RMS, skewness ŽSkew. and kurtosis ŽKurt.. Ø Columns 8–59 hold the first 52 spectral characteristics of a power density spectrum which corresponds to the frequency domain information within 0–200 Hz. Using the training data set, the following linear models were developed for predicting the solid concentration Ž Sc .,
4. Modelling A stepwise regression analysis technique using the socalled ‘best subset’ strategy w13x was employed in this paper to derive relations between the physical properties of
Fig. 6. Effect of mains frequency changes on the power density spectrum of the signal at 20 wt.% solid concentration.
R. Hou et al.r Powder Technology 106 (1999) 30–36
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Table 2 ANOVA and R 2 values obtained from the regression models Parameter
Source of variation
Degrees of freedom
Sum of squares ŽSS.
Solid concentration Žwt.%.
total corrected regression < b 0 residuals total corrected regression < b 0 residuals total corrected regression < b 0 residuals
59 8 51 59 5 54 59 7 52
7500 7400.0 100.0 81.0 78.7 2.2 18.8 18.6 0.2
Mass flowrate Žkgrs.
Volume flowrate Žlrs.
mass flowrate Ž Mf . and volume flowrate Ž Vf . of the pipeline flow. Sc s 65.3 y 1.6 F50.8 y 35.4 F156.3 y 47.6 F82.0 y 6.6 F62.5 q 17.7F136.7 y 4.4 F109 .4 y 6.9F101.6 q 2.2 F11.7
Ž 1.
Mf s 7.6 y 4.0 F43.0 y 0.6 F101.6 y 1.2 F62.5 y 5.5F82.0 y 3.7F117.2
Ž 2.
Vf s 14.1 y 0.9F171 .9s1.5F183.6 q 0.5F101.6 y 0.3F121.1 q 3.2 F175.8 y 0.9Mean q 0.03F23.4
Ž 3.
It can be seen from the above equations that almost all the entries to the models are composed of power density spectrum data, which implies the importance of the signal spectral characteristics effectively providing a finger print of a process. Table 2 lists the variance analysis result and the value of multiple correlation coefficient R 2 for each predictive model. The results indicate that good model sensitivities can be expected for all three parameters ŽEqs. Ž1. – Ž3... The solid concentration, mass and volume flowrate models have each accounted 98.7%, 97.3% and
Fig. 7. Measured data and predicted values for the solid concentration.
Mean of squares Ž s 2 .
Model R 2 value Ž%. 98.7
925.0 2.0 97.3 15.8 0.0 98.9 2.7 0.0
98.9% of total variations, respectively, about the mean value of the corresponding observations. Figs. 7–9 show the predicted values against the actual measured data for solid concentration, mass flowrate and volume flowrate, respectively, when the validation data set was applied to the established models. It is clear that reasonably good predictions have been achieved for all the three parameters. The prediction accuracy for the volume flowrate is especially impressive. Table 3 summarized the average performance for each of the models. The typical average prediction errors for both solid concentration and volume flowrate are well below 5%. Almost all the significant errors have been noted to be occurring in the prediction of mass flowrate. This is, however, not of much concern here, as the mass flowrate can actually be deducted from the following equation: Mf s Vf
½
100
Ž Scr2.7 . q Ž 100 y Sc .
y1
5
Ž 4.
where Mf , Vf and Sc are mass flowrates in kilograms per second Žkgrs., volume flowrate in liters per second Žlrs.
Fig. 8. Measured data and predicted values for the mass flowrate.
R. Hou et al.r Powder Technology 106 (1999) 30–36
35
sensor mounted on a pipe are influenced by the hydrodynamic characteristics of the flow regime. The recorded signal can be used as a ‘finger print’ of the system. The experimental measurements show that the characteristic frequency in the power density spectrum of the signal is a manifestation of the speed of the positive displacement pump. These characteristic spectra are modulated Žin amplitude. by the mass and velocity related properties of the flow. Use of the multivariate stepwise regression analysis technique to account quantitatively for the relationships between the signal characteristics and the flow conditions has proved to be successful, with a typical average predicting error of less than 5% being readily achieved for all the experiment conditions tested. It should be noted that although the nature of the empirical regression approach determines that the models developed in this study are only applicable to the same system configuration as well as same type of materials, it is anticipated that the technology itself can well be expanded to other flow configurations and types of slurry. This philosophy has been tested for alternative applications. In practical terms, the models can be deduced quite quickly based on a well designed experimental on-line calibration process. The viability of this method depends upon the mechanical flow properties of the two-component mixture, and is unlikely to be universally applicable. Nevertheless, labora-
Fig. 9. Measured data and predicted values for the volume flowrate.
and solid concentration in weight percent Žwt.%., respectively.
5. Conclusions It can be concluded that in the present investigation the mechanical transmissions received by a tuned piezoelectric Table 3 Summary of model performance Parameter
Measured value
Average predicted value
Comparative error Ž%.
Solid concentration Žwt.%.
10.0 20.0 30.0 40.0 0.7 0.8 0.9 1.7 1.8 2.0 2.5 2.8 3.0 3.7 3.9 4.3 6.6 7.3 8.4 7.1 7.8 8.3 6.9 7.8 8.2 7.1 7.4 7.9
10.5 21.5 29.0 39.4 0.9 0.7 1.0 2.0 1.7 2.3 2.4 2.3 3.2 3.6 4.0 4.0 6.6 7.4 8.2 7.1 7.7 8.4 7.0 7.7 8.2 7.1 7.4 7.9
5.0 7.5 3.3 1.5 28.57 12.5 11.1 17.7 5.6 15.0 4.0 17.9 6.7 2.7 2.6 7.5 0.0 1.4 2.4 0.0 1.3 1.2 1.5 1.3 0.0 0.0 0.0 0.0
Mass flowrate Žkgrs.
Volume flowrate Žlrs.
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R. Hou et al.r Powder Technology 106 (1999) 30–36
tory investigations suggest that many types of aqueous mineral slurries could be monitored in this manner. The methodology can be extended to consider more complex Žthree or more components. mixtures including a polymer-based continuous phase w8x. Acknowledgements The authors wish to thank Schlumberger Cambridge Research and ORS Award Committee for the financial sponsorship of the research. References w1x M.K. Bevir, The theory of induced voltage electromagnetic flowmeters, J. Fluid Mech. 43 Ž1970. 577–590. w2x M.S. Beck, A. Plaskowski, Cross Correlation Flowmeters: Their Design and Application, Adam Hilger, Bristol, 1987. w3x R.A. Williams, C.G. Xie, F.J. Dickin, S.J. Simons, M.S. Beck, Multi-phase flow measurement in powder processing, Powder Technology 66 Ž1991. 203–224.
w4x T.J.S. Brain, R.W.W. Scott, Survey of pipeline flowmeters, J. Phys. E 15 Ž1982. 967–980. w5x T. Folkestad, K.S. Mylvaganam, Acoustic measurements detect sand in North Sea flow lines, Oil and Gas Journal 27 Ž1990. 33–39. w6x C.J. Lorenzen, C. Carlhoff, U. Hahn, M. Jogwich, Applications of laser-induced emission spectral-analysis for industrial-process and quality-control, J. Anal. Atom. Spectrosc. 7 Ž1992. 1029–1035. w7x C.G. Xie, A.L. Stott, A. Plaskowski, M.S. Beck, Design of capacitance electrodes for concentration measurement of two-phase flow, Measurement Science and Technology 1 Ž1990. 65–78. w8x R. Hou, PhD Thesis, 1998, in preparation. w9x R.A. Williams, S.J. Peng, D. Brown, N. Parkinson, P. James, On-line measurement of hydrocyclone performance using acoustic emission, Hydrocyclone 1996, Mechanical Engineering Publications, London, 1996, 241–252. w10x R. Hou, A. Hunt, R.A. Williams, Acoustic monitoring of hydrocyclone performance, Minerals Engineering 11 Ž1998. in press. w11x T.J. Fowler, Chemical industry applications of acoustic emission, Materials Evaluation 50 Ž1992. 875–882. w12x B.P. Sergiev, A.V. Pachkov, Use of the acoustic emission method for monitoring the condition of industrial pipeline, Chemical and Petroleum Engineering 30 Ž1994. 321–324. w13x N. Draper, H. Smith, Applied Regression Analysis, Wiley, New York, 1981.
Acoustic monitoring of pipeline flows: particulate slurries R. Hou a
a,)
, A. Hunt b, R.A. Williams
a
Camborne School of Mines, UniÕersity of Exeter, Redruth, Cornwall TR15 3SE, UK b Schlumberger Cambridge Research, Madingley Road, Cambridge CB3 0EL, UK
Received 27 October 1997; received in revised form 19 February 1999; accepted 19 February 1999
Abstract This paper presents a case study of the use of a non-intrusive passive acoustic sensor for on-line monitoring of flow and related process parameters. The sensor was mounted externally on a small diameter pipeline conveying dense slurries of fine silica particles. Origins of the acoustic emissions in the pipeline are discussed, particularly, with reference to interactions with flow parameters of interest in process control. The statistical and spectral characteristics of the collected acoustic signal for different experimental conditions were utilized to build quantitative models to infer parameters such as solid concentration, volume flowrate and mass flowrate. The results indicate that passive acoustic signals can be a viable tool for on-line monitoring of slurry flows. q 1999 Elsevier Science S.A. All rights reserved. Keywords: Hydraulic conveying; Passive acoustic monitoring; Signal processing; Multivariate modelling
1. Introduction Pipelines are ubiquitous in various industrial processes. The operating conditions prevailing in a pipeline flow for different applications may vary widely. The line pressure, for instance, can be as low as just a few bar in water transportation, to as high as up to 1000 bar in slurry conveying operations. In addition, characteristics of the fluids may range from clean water to highly abrasive cement slurries, viscous gel suspensions, or erosive and dangerous chemical solutions. Such diversity can make accurate flow measurement and on-line diagnosis and monitoring of such processes extremely difficult. Some typical limitations are considered below. In flowrate measurement, for example, none of the commercially-available flowmeters being used in practice is able to operate independent of the properties of the fluid. The performance of turbine flowmeters can be seriously hampered by the viscosity changes and the presence of solid particles in the flow. Similar degradation also happens when vortex shedding flowmeters or differential pressure devices are employed. The widely applied electromagnetic flowmeter principle becomes inoperable when the conductivity of the fluid drops below 10y4 Srm w1x.
)
Corresponding author
Although various cross correlation flowmeters Žsuch as electrostatic, ultrasonic. have been reported to be more universal, their application to date has been limited since implementation of the technology seems to require fairly complicated instrumentation and calibration w2x. Similar problems are also encountered in attempts to monitor particle size and other characteristics and solid concentrations dispersed in a slurry pipeline. A plethora of different sensing methods is available w3x. Some frequently used methods include radioactive probe, optical techniques and ultrasound transmission measurements. These methods unfortunately can be cumbersome to maintain, and the measurement accuracy may also be seriously affected by the specific process environment changes such as viscosity, pressure, solution chemistry, local sound celerity. The consequence is therefore that in many industrial processes, the desire to implement better process and quality control strategies and achieve a better understanding of the operation is often impeded by the lack of appropriate measuring instrumentation. One route to the alleviation of the situation relies on the provision of more reliable and resilient sensing methodologies which are capable of performing accurate and independent real time measurements. In addition, the practical implementation of the sensors should preferably also have little or no interference with the process being monitored and should demand little maintenance. A few techniques have been proposed
0032-5910r99r$ - see front matter q 1999 Elsevier Science S.A. All rights reserved. PII: S 0 0 3 2 - 5 9 1 0 Ž 9 9 . 0 0 0 5 1 - 0
R. Hou et al.r Powder Technology 106 (1999) 30–36
31
ments. So far, a number of successful applications of this method in various particulate related industries have been reported w9–12x. In this paper, we consider a systematic investigation of using the passive acoustic monitoring technique to monitor pumping of fine silica particle suspensions running in a 44.5-mm diameter pipe. The frequency spectral behaviour of the collected acoustic emission signal is correlated with respect to the process parameters of the system. The physical significance of the origin of the modulation of the natural acoustic emission signal is discussed. Fig. 1. Configuration of test rig.
2. Experimental to meet these challenges based on mechanical w4x, acoustic w5x, laser w6x and electric capacitance w7x methods. We are concerned in this paper with passive acoustic monitoring w8x which relies on the ‘natural’ acoustic noise produced by fluid flowing inside a pipeline. The method uses multivariate statistical analysis techniques to infer process related information. In essence, the monitoring technique exploits the fact that when particulate slurries or solid particles interact with the inner surface of a pipeline, the resulting sound waves will generate impulses in the vicinity of the collision, causing the bulk of the pipeline to oscillate. The oscillation signal can then be collected through a clamp-on piezoelectric acoustic sensor fixed to the exterior of the pipeline. This signal is transformed into a suitable electrical signal form Žvoltage or current. for further analysis. Since the main contributing source of the system comes from the striking momentum of the particles or suspensions, the amplitude of the recorded signal is expected to be sensitive to variations in physical characteristics of the flow, such as solid concentration, flowrate, density, viscosity. Therefore, by using an appropriate statistical analysis technique, the relationship between the flow property and recorded signal can be established. Hence, in principle the technique offers a both convenient and non-intrusive way of implementing on-line measure-
2.1. Flow rig and experimental materials The test loop ŽFig. 1. consisted of a positive displacement pump Žmodel CMD80B4, Mono Pumps., standard 44.5 mm steel piping, a 300-l circulating tank and ball valves. Flowrate changes can be made by adjusting the back pressure on the pump by means of bypass valve setting. A three-phase 7.5 kW converter Žmodel 3G3IVA4075-EVI, Omaron, Japan. was also employed so that the rotational speed of the pump could be changed by varying the operating frequency of the power input to the pump motor. A fine commercial silica flour product ŽHPF-2 from Hepworth Minerals and Chemicals., with an average particle size Ž d 50 . of 13 mm was used as the experiment material throughout the test. The experiment was carried out in a factorial design fashion, with a total of four levels of solid concentrations ranging from 10 to 40 wt.% Žcorresponding to solid volume fraction of 0.04 to 0.2. and three different volume flowrate values at each solid concentration level being investigated. The volume flowrate was measured to "0.1 lrs by sampling the stream and weighing a known collected volume off-line. Detailed experimental conditions are listed in Table 1. All the experiments were carried out at room temperature.
Table 1 Event numbers and experimental conditions Event number a
Solid concentration Žwt.%.
Mass flowrate Žkgrs.
Volume flowrate Žlrs.
1–10 11–20 21–30 31–40 41–50 51–60 61–70 71–80 81–90 91–100 101–110 111–120
10.0 10.0 10.0 20.0 20.0 20.0 30.0 30.0 30.0 40.0 40.0 40.0
0.7 0.8 0.9 1.7 1.8 2.0 2.5 2.8 3.0 3.7 3.9 4.3
6.6 7.3 8.4 7.1 7.8 8.3 6.9 7.8 8.2 7.1 7.4 7.9
a
Each event corresponds to a duration of 16 s of data collection.
32
R. Hou et al.r Powder Technology 106 (1999) 30–36
Fig. 2. General arrangement of signal processing system.
2.2. Acoustic sensors, signal and data processing A single 190-kHz piezoelectric type acoustic sensor Žmodel NS2002, AV Technology. was used in the experiments. The sensor was fixed with an epoxy adhesive ŽPermabond. onto the first right angle bend of the pipeline downstream of the pump outlet ŽFig. 1.. This location was selected from preliminary tests which showed that the highest signal-to-noise ratio can be obtained. The signal processing system consists of a pre-amplifier, signal conditioning unit, control PC with an analogue to digital conversion card, and necessary data storage facilities. Fig. 2 shows the general arrangement of the system and full details of other signal processing hardware involved in the experiment and the subsequent data acquisition procedures have been given elsewhere w10x. The signal was first amplified locally and then fed into the signal conditioning unit for demodulation and filtering. After that the signal was channelled to the ADC card and digitized at a sampling rate of 2000 Hz. Finally a 512-point fast fourier transform ŽFFT. algorithm with Hanning window averaging and 2r3 section overlapping strategy was initiated to transform the recorded signal from time domain into frequency domain to reveal any detailed spectral characteristics of the signal. A typical power density spectrum structure over the full frequency range of the collected signals is illustrated in Fig. 3. It is seen that all the useful informations appear to fall in the frequency range below 400 Hz and predomi-
Fig. 3. Typical power density spectrum structure in full frequency range.
nately 0–200 Hz. The 2000-Hz data sampling rate can, therefore, be well justified. For each experiment condition, a total of 160-s duration of data were collected. They were then divided into 10 independent ‘event’ sections, each event representing a measurement period of 16-s data. To provide good representative and statistical accuracy, the averages of these 10 events were used in this paper as the results for a specified experiment condition.
3. Results and discussions Typical energy vs. frequency spectra showing the effect of volume flowrates at solid concentration of 10 and 20 wt.% are illustrated in Fig. 4 and Fig. 5, respectively. Similar results with different volume flowrates were also observed at solid concentrations of 30 and 40 wt.%, as detailed elsewhere w8x. Figs. 4 and 5 clearly indicate that the signal spectral characteristics are rather sensitive to both solid concentration and volume flowrate changes. One closer inspection it became evident that the spectral peaks are actually occurring at regular frequency intervals. This implies that there is possibly only one basic frequency component in the spectra. The multiple peaks are merely the harmonics of this basic frequency component. In these tests, the main contributing source of the acoustic signal apparently is the oscillation of the pipeline hardware as imposed by the prevailing flow slurry. Since the operation of the displacement pump is the only driving force to the flow, it was assumed that the frequency of the spectrum should be closely related to the pulsating rate of the flow. A distinctive operating feature of this type of pump is that it generally causes the flow to pulsate at the frequency of pump rotation.
Fig. 4. Effect of volume flowrate on the power density spectrum at solid concentration of 10 wt.%.
R. Hou et al.r Powder Technology 106 (1999) 30–36
Fig. 5. Effect of volume flowrate on the power density spectrum at solid concentration of 20 wt.%.
To confirm this assumption, the frequency of the mains supplying the pump was adjusted using a three phase converter. This subsequently varied the rotation speed of the pump. A digital tachometer was then employed to read off the pump rotation speed at a specific mains frequency. Fig. 6 shows the frequency spectra of the signal when the mains frequency was set at 40, 50 and 60 Hz, respectively. It can be seen from the figure that increasing mains frequency not only causes the spectrum to shift towards left Žlower frequencies., but also results in a steady increase of the underlying characteristic frequency of the spectrum. The latter was estimated by measuring the interval between two consecutive peaks in the spectra namely: 10, 12.5 and 15 Hz when the mains frequencies were 40, 50 and 60 Hz, respectively. Moreover, these measurements were, as expected, found to be exactly the same as the pump rotation speed values which were read off from the tachometer as 600, 750 and 900 rpm for each corresponding mains frequency. In summary, it appears that the frequency spectrum of the signal is actually a function of the imposed pulsation, modulated by the flow parameters in the vicinity of the sensor mounting position. The magnitude of the acceleration experienced by the sensor is likely to be a function of the flow momentum, solids concentration and flowrate. Hence the analysis of variations in the amplitude of characteristic spectral peaks provides a means of monitoring, indirectly, the properties of the slurry.
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the flow and the statistical and spectral characteristics of the signal. The selection of the variable entries in the model was based on the statistical significance of the partial F-test result set at 95% confidence level, with the multiple correlation coefficient R 2 being used to judge the model performance. As the implementation of the technique requires two independent data sets: one to develop the models Žtraining set. and the other to validate Žvalidation set., we decided to split up the 10 events data collected at each experimental condition into two equal parts: the first five events being compiled into the training set and the remaining five into the validation set. This results in a training and validation set each having 60 events of data. The data set is arranged in a ‘row oriented’ format, with each row representing a particular test event and each column a specific predictor variable. Since preliminary tests revealed that some time domain statistical characteristic values of the signal Žmaximum, minimum, and rootmean-squares ŽRMS. etc.., also exhibited a certain degree of sensitivity to the experimental condition variations, it was then decided that both the time-domain and frequency-domain information of the recorded signal should be included in the training or validation data set. The final data matrix consisted of following column entries. Ø Columns 1–7 hold the seven statistical parameters of the signal in the time-domain, including values for the maximum ŽMax., minimum ŽMin., mean ŽMean., standard deviation ŽSTD., RMS, skewness ŽSkew. and kurtosis ŽKurt.. Ø Columns 8–59 hold the first 52 spectral characteristics of a power density spectrum which corresponds to the frequency domain information within 0–200 Hz. Using the training data set, the following linear models were developed for predicting the solid concentration Ž Sc .,
4. Modelling A stepwise regression analysis technique using the socalled ‘best subset’ strategy w13x was employed in this paper to derive relations between the physical properties of
Fig. 6. Effect of mains frequency changes on the power density spectrum of the signal at 20 wt.% solid concentration.
R. Hou et al.r Powder Technology 106 (1999) 30–36
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Table 2 ANOVA and R 2 values obtained from the regression models Parameter
Source of variation
Degrees of freedom
Sum of squares ŽSS.
Solid concentration Žwt.%.
total corrected regression < b 0 residuals total corrected regression < b 0 residuals total corrected regression < b 0 residuals
59 8 51 59 5 54 59 7 52
7500 7400.0 100.0 81.0 78.7 2.2 18.8 18.6 0.2
Mass flowrate Žkgrs.
Volume flowrate Žlrs.
mass flowrate Ž Mf . and volume flowrate Ž Vf . of the pipeline flow. Sc s 65.3 y 1.6 F50.8 y 35.4 F156.3 y 47.6 F82.0 y 6.6 F62.5 q 17.7F136.7 y 4.4 F109 .4 y 6.9F101.6 q 2.2 F11.7
Ž 1.
Mf s 7.6 y 4.0 F43.0 y 0.6 F101.6 y 1.2 F62.5 y 5.5F82.0 y 3.7F117.2
Ž 2.
Vf s 14.1 y 0.9F171 .9s1.5F183.6 q 0.5F101.6 y 0.3F121.1 q 3.2 F175.8 y 0.9Mean q 0.03F23.4
Ž 3.
It can be seen from the above equations that almost all the entries to the models are composed of power density spectrum data, which implies the importance of the signal spectral characteristics effectively providing a finger print of a process. Table 2 lists the variance analysis result and the value of multiple correlation coefficient R 2 for each predictive model. The results indicate that good model sensitivities can be expected for all three parameters ŽEqs. Ž1. – Ž3... The solid concentration, mass and volume flowrate models have each accounted 98.7%, 97.3% and
Fig. 7. Measured data and predicted values for the solid concentration.
Mean of squares Ž s 2 .
Model R 2 value Ž%. 98.7
925.0 2.0 97.3 15.8 0.0 98.9 2.7 0.0
98.9% of total variations, respectively, about the mean value of the corresponding observations. Figs. 7–9 show the predicted values against the actual measured data for solid concentration, mass flowrate and volume flowrate, respectively, when the validation data set was applied to the established models. It is clear that reasonably good predictions have been achieved for all the three parameters. The prediction accuracy for the volume flowrate is especially impressive. Table 3 summarized the average performance for each of the models. The typical average prediction errors for both solid concentration and volume flowrate are well below 5%. Almost all the significant errors have been noted to be occurring in the prediction of mass flowrate. This is, however, not of much concern here, as the mass flowrate can actually be deducted from the following equation: Mf s Vf
½
100
Ž Scr2.7 . q Ž 100 y Sc .
y1
5
Ž 4.
where Mf , Vf and Sc are mass flowrates in kilograms per second Žkgrs., volume flowrate in liters per second Žlrs.
Fig. 8. Measured data and predicted values for the mass flowrate.
R. Hou et al.r Powder Technology 106 (1999) 30–36
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sensor mounted on a pipe are influenced by the hydrodynamic characteristics of the flow regime. The recorded signal can be used as a ‘finger print’ of the system. The experimental measurements show that the characteristic frequency in the power density spectrum of the signal is a manifestation of the speed of the positive displacement pump. These characteristic spectra are modulated Žin amplitude. by the mass and velocity related properties of the flow. Use of the multivariate stepwise regression analysis technique to account quantitatively for the relationships between the signal characteristics and the flow conditions has proved to be successful, with a typical average predicting error of less than 5% being readily achieved for all the experiment conditions tested. It should be noted that although the nature of the empirical regression approach determines that the models developed in this study are only applicable to the same system configuration as well as same type of materials, it is anticipated that the technology itself can well be expanded to other flow configurations and types of slurry. This philosophy has been tested for alternative applications. In practical terms, the models can be deduced quite quickly based on a well designed experimental on-line calibration process. The viability of this method depends upon the mechanical flow properties of the two-component mixture, and is unlikely to be universally applicable. Nevertheless, labora-
Fig. 9. Measured data and predicted values for the volume flowrate.
and solid concentration in weight percent Žwt.%., respectively.
5. Conclusions It can be concluded that in the present investigation the mechanical transmissions received by a tuned piezoelectric Table 3 Summary of model performance Parameter
Measured value
Average predicted value
Comparative error Ž%.
Solid concentration Žwt.%.
10.0 20.0 30.0 40.0 0.7 0.8 0.9 1.7 1.8 2.0 2.5 2.8 3.0 3.7 3.9 4.3 6.6 7.3 8.4 7.1 7.8 8.3 6.9 7.8 8.2 7.1 7.4 7.9
10.5 21.5 29.0 39.4 0.9 0.7 1.0 2.0 1.7 2.3 2.4 2.3 3.2 3.6 4.0 4.0 6.6 7.4 8.2 7.1 7.7 8.4 7.0 7.7 8.2 7.1 7.4 7.9
5.0 7.5 3.3 1.5 28.57 12.5 11.1 17.7 5.6 15.0 4.0 17.9 6.7 2.7 2.6 7.5 0.0 1.4 2.4 0.0 1.3 1.2 1.5 1.3 0.0 0.0 0.0 0.0
Mass flowrate Žkgrs.
Volume flowrate Žlrs.
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R. Hou et al.r Powder Technology 106 (1999) 30–36
tory investigations suggest that many types of aqueous mineral slurries could be monitored in this manner. The methodology can be extended to consider more complex Žthree or more components. mixtures including a polymer-based continuous phase w8x. Acknowledgements The authors wish to thank Schlumberger Cambridge Research and ORS Award Committee for the financial sponsorship of the research. References w1x M.K. Bevir, The theory of induced voltage electromagnetic flowmeters, J. Fluid Mech. 43 Ž1970. 577–590. w2x M.S. Beck, A. Plaskowski, Cross Correlation Flowmeters: Their Design and Application, Adam Hilger, Bristol, 1987. w3x R.A. Williams, C.G. Xie, F.J. Dickin, S.J. Simons, M.S. Beck, Multi-phase flow measurement in powder processing, Powder Technology 66 Ž1991. 203–224.
w4x T.J.S. Brain, R.W.W. Scott, Survey of pipeline flowmeters, J. Phys. E 15 Ž1982. 967–980. w5x T. Folkestad, K.S. Mylvaganam, Acoustic measurements detect sand in North Sea flow lines, Oil and Gas Journal 27 Ž1990. 33–39. w6x C.J. Lorenzen, C. Carlhoff, U. Hahn, M. Jogwich, Applications of laser-induced emission spectral-analysis for industrial-process and quality-control, J. Anal. Atom. Spectrosc. 7 Ž1992. 1029–1035. w7x C.G. Xie, A.L. Stott, A. Plaskowski, M.S. Beck, Design of capacitance electrodes for concentration measurement of two-phase flow, Measurement Science and Technology 1 Ž1990. 65–78. w8x R. Hou, PhD Thesis, 1998, in preparation. w9x R.A. Williams, S.J. Peng, D. Brown, N. Parkinson, P. James, On-line measurement of hydrocyclone performance using acoustic emission, Hydrocyclone 1996, Mechanical Engineering Publications, London, 1996, 241–252. w10x R. Hou, A. Hunt, R.A. Williams, Acoustic monitoring of hydrocyclone performance, Minerals Engineering 11 Ž1998. in press. w11x T.J. Fowler, Chemical industry applications of acoustic emission, Materials Evaluation 50 Ž1992. 875–882. w12x B.P. Sergiev, A.V. Pachkov, Use of the acoustic emission method for monitoring the condition of industrial pipeline, Chemical and Petroleum Engineering 30 Ž1994. 321–324. w13x N. Draper, H. Smith, Applied Regression Analysis, Wiley, New York, 1981.