Development of Soft Sensor to Identify Flow Regimes in Horizontal

Ind. Eng. Chem. Res. 2010, 49, 3001–3010

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Development of Soft Sensor to Identify Flow Regimes in Horizontal Pipe Using Digital Signal Processing Technique Kishore Muvvala,† V. Kumar,† B. C. Meikap,*,†,‡ and Sudipto Chakraborty† Department of Chemical Engineering, Indian Institute of Technology (IIT), Kharagpur, P.O. Kharagpur Technology, West Bengal, Pin - 721302, India, and School of Chemical Engineering, Howard College Campus, Faculty of Engineering, UniVersity of Kwazulu-Natal (UKZN), King George V AVenue, Durban 4041, South Africa

The work described in this article is a new analytical and experimental study of two-phase gas-liquid horizontal flow in a conduit for flow pattern identification by a digital signal processing technique. The data are acquired online using a pressure transducer and a National Instruments data acquisition card. The true signal voltage changes rather smoothly as a function of the number of samples, whereas many kinds of noise are added; as a result, rapid, random changes in amplitude from point to point within the signal are observed. To reduce the noise, a smoothing technique is employed. In the smoothing, the data points of a signal are modified so that the individual points that are higher than the immediately adjacent points are reduced and points that are lower than the adjacent points are increased. This naturally leads to a smoother signal. For this purpose, the simplest smoothing algorithm, namely, a simple moving average, is employed and is able to successfully eliminate the noise. After eliminating the noise, the slope of the curve is continuously tracked to determine sudden or abrupt changes. It is interesting to note that, from the signal obtained, the slope is always decreasing in nature and the magnitude is on the order of 5 × 10-3. According to the data and the slope, different flow regimes can be identified by using cross-correlation method. Introduction Two-phase gas-liquid flow can be defined as the interacting flow of a gas and a liquid where the interface between the phases is influenced by their motions. The interactions of the phases give rise to an infinite range of interfacial distributions, which is one of the characteristic features of a two-phase flow system. When a gas and a liquid are forced to flow together inside a pipe, at least seven different geometrical configurations, or flow regimes, are observed to occur. The regime depends on the fluid properties, the size of the conduit, and the flow rates of each of the phases. The flow regime can also depend on the configuration of the inlet; the flow regime might take some distance to, develop and it can change with distance as the pressure, which affects the gas density, changes. For fixed fluid properties and fixed conduit, the flow rates are the independent variables that, when adjusted, will often lead to changes in the flow regime. Multiphase flow regimes are very common in industrial pipelines. In the petrochemical industry, for instance, the monitoring of oil-gas flows is increasingly necessary for safe operation in exploration and production fields. The unexpected arrival of slugs at the inlet of three-phase separators installed on offshore production platforms results in severe transients to their control systems and contributes to a reduction of the operation efficiency of such equipment. The possibility of interfering with such regimes, together with the use of flow-regime inference algorithms, can widen the amount of information available to the operators of such industrial processes, increasing security and efficiency. Two-phase flow widely exists in various fields such as power plants, nuclear energy, and the chemical and metallurgical industries. Flow regime plays an important role in two-phase flow because it affects not only the flow behavior, diathermancy, and matter * To whom correspondence should be addressed. Tel.: +27-312603802. Fax: +27-31-260-3802. E-mail: [email protected]. † Indian Institute of Technology (IIT). ‡ University of Kwazulu-Natal (UKZN).

propagation properties, but also the precise measurement of twophase flow parameters. An important distinction in single-phase flow is whether the flow is laminar or turbulent or whether separation flow or secondary flows exist. This information helps in the modeling of specific phenomena because it provides an indication of the flow character for a particular geometry. Analogously, in multiphase flow, probably the key to understanding the phenomenon is the ability to identify the internal geometry of the flow, specifically, the relative locations of the interfaces between the phases; how they are affected by pressure, flow, heat flux, and channel geometry; and how transitions between flow patterns occur. Two types of flow patterns can be identified, namely, stratified and dispersed phase. A stratified flow pattern is one in which the two phases are separated by a continuous interface at a length scale comparable to the external scale of the flow, for example, a liquid film on a wall with a gas or another immiscible liquid in the center of the channel. The complete separation of the two phases usually occurs because of density differences (horizontal flow) combined with a relatively low mass flow rate of the phase near the wall compared to the other phase in the center of the channel (e.g., vertical annular flow). These separated flow patterns can occur when the phases flow in the same direction (cocurrent flow) or in opposite directions (countercurrent flow). The balance between buoyancy and inertial forces governs the transition between these two types of stratified flow. The liquid flow rate is high enough to break up the gas into bubbles, but it is not high enough to cause the bubbles to become well mixed within the liquid phase. If the pipe is oriented vertically, the phase orientation will be symmetric, but there will likely be “slip” between the phases and the gas will not move at the same speed as the liquid.1-3 In annular flow, the liquid coats the walls. However, because of gravity, the liquid distribution is not symmetric, as much more liquid is on the bottom of the pipe than the top. The velocity of the gas is large enough to cause waves to form in

10.1021/ie9019215  2010 American Chemical Society Published on Web 02/12/2010

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Ind. Eng. Chem. Res., Vol. 49, No. 6, 2010

Figure 1. Schematic diagrams of flow regimes in a horizontal pipe.

the liquid and also to atomize some of the liquid. For liquid layers that are not too thin, the maximum possible wave amplitude scales roughly as the liquid thickness. The slug regime is characterized by the presence of liquid-rich slugs that span the entire channel or pipe diameter. These slugs travel at a speed that is a substantial fraction of the gas velocity, and they occur intermittently. They cause large fluctuations in both pressure and liquid flow rate that look like a large flow surge or a large wave. Two-phase flow is characterized by the existence of a range of constantly varying interfaces resulting in a range of different flow patterns that are not found in single-phase flow. In addition, there exists a difference in velocity in the two phases as a result of the difference in fluid properties giving rise to slip between the phases and the distribution of velocity is not uniform in comparison to single-phase flow. The various possible flow regimes in a horizontal pipe are presented in Figure 1. Xie et al.4 reported the predictions of the flow regimes using the artificial neural technique, by frequency domain analysis. The artificial neural network (ANN) trained and tested for sensor 1 predicted the flow regimes reasonably well when applied directly to the normalized pressure power spectrum density characteristics of the other two sensors, indicating a good deal of transportability. Shim and Chul5 identified flow regimes and measure quality in two-phase flow using pressure fluctuations in an annulus. Flow regime was identified based on wavelet analysis and the back-propagation (BP) neural technique. Flow patterns were identified using back-propagation networks trained on simulated data based on a capacitance tomography sensor.6 Noninvasive techniques such as electrical capacitance tomography (ECT) are beginning to make promising contributions to control systems and are well suited for flow-regime identification in opaque pipes or conduits. Aldowrish et al.7 identified twophase flow patterns using a fuzzy methodology. Selli and Seleghim8 identified two-phase flow patterns through Gabor transforms and neural network processing. A literature survey reveals that most of the techniques for identifying flow regimes in multiphase flow use pressure fluctuations. Among the methods reported in the literature, some have very large mathematical calculations and complicated experimental setups. Therefore, in this study, an attempt has been made to develop a new simple cross-correlation method for identifying the flow regimes in horizontal flow. Data Acquisition and Analysis Techniques A data acquisition system is a device designed to measure and record some parameters. A schematic representation of a data acquisition system is shown in Figure 2. The purpose of a data acquisition system is generally the analysis of the recorded data. The data acquisition system is normally electronics-based

Figure 2. Schematic representation of a data acquisition system.

and consists of hardware and software. The hardware part consists of sensors, cables, and electronic components, and the software part consists of the data acquisition logic and the analysis software. Data acquisition is the sampling of the real world to generate data that can be manipulated by a computer. Sometimes abbreviated DAQ, data acquisition typically involves the acquisition of signals and waveforms and the processing of these signals to obtain the desired information. The components of data acquisition systems include appropriate sensors that convert any measurement parameter into an electrical signal, which is acquired by data acquisition hardware. Acquired data are displayed, analyzed, and stored on a computer, using either vendor-supplied software or custom displays, and controls can be developed using various text-based programming languages. Data acquisition begins with the physical phenomenon or physical property of a system to be measured. This physical property or phenomenon could be the temperature or temperature change of a room, the intensity or intensity change of a light source, the pressure inside a chamber, the force applied to an object, or many other things. An effective data acquisition system can measure all of the different properties or phenomena under investigation. A transducer is a device that converts a physical property or phenomenon into a corresponding measurable electrical signal, such as voltage or current. The ability of a data acquisition system to measure different phenomena depends on the transducers used to convert the physical phenomena into signals that can be measured by the data acquisition hardware. Transducers are synonymous with sensors in DAQ systems. There are specific transducers for many different applications, such as measuring temperature, pressure, or fluid flow. Signals can be digital (sometimes called logic signals) or analog depending on the transducer used. Signal conditioning acts as an intermediate stage between signal acquisition and future signal analysis stages. Signal conditioning might be necessary if the signal from the transducer is not suitable for the DAQ hardware to be used. Signal conditioning can involve the amplification and attenuation of a signal to “prepare” it for the next stage of processing. “Conditioning” of a signal basically means to manipulate a signal in such a way that it meets the requirements for the next stage for further processing. The signal might be amplified or deamplified or multiplexed or demultiplexed, or it might require filtering to eliminate noise. DAQ hardware is what usually interfaces between the signal and a personal computer (PC). It could be in the form of modules that can be connected to the computer’s ports [parallel, serial, universal serial bus (USB), etc.] or cards connected to slots [peripheral component interconnect (PCI), industry standard architecture (ISA)] in the motherboard. DAQ cards often contain multiple components [multiplexer, advance data connector (ADC), digital-to-analog


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because of its simplicity and small influence on pipes. Using differential pressure, there are many techniques for predicting flow regimes. The interface between the sensor and PC is the data acquisition card and signals before a data acquisition device acquires them. Data acquisition typically involves the acquisition of signals and waveforms and the processing of signals to obtain the desired information. Data acquisition is the process of gathering or generating information in an automated fashion from analog and digital measurement sources such as sensors and devices under investigation. Data acquisition uses a combination of PC-based measurement hardware and software to provide a flexible, user-defined measurement system. A signal is defined as any physical quantity that varies with time, space, temperature, pressure, and so on. A system is defined as a physical quantity that performs an operation on a signal. For example, a filter used to reduce the noise corrupting a desired information bearing signal is called a system. Filtering

Figure 3. (a) Data acquisition card details. (b) Schematic representation of analog-to-digital conversion. (c) Architecture of digital signal processing.

converter (DAC), high-speed timers, random-access memory (RAM)]. Driver software, which usually comes with the DAQ hardware or from other vendors, allows the operating system to recognize the DAQ hardware and programs to access the signals being read by the DAQ hardware. Data analysis is the act of transforming data with the aim of extracting useful information and facilitating conclusions. Depending on the type of data and the question, this might include application of statistical methods, curve fitting, selection or exclusion of certain subsets based on specific criteria, or other techniques. The current research study involves a detailed study of the prediction of flow patterns in the air-water system. For this purpose, a horizontal pipe with appropriate dimensions was used. During the experimental study, the flow regimes at various feed flow rates were identified. A program was developed using Matlab to determine flow regimes in a horizontal pipe in real time by acquiring live data using a pressure sensor and a National Instruments data acquisition card; details are presented in Figure 3a. Methods for the Estimation of Flow Patterns In recent years, many methods have been proposed for the identification of flow regimes, such as impedance-based instrumentation, artificial neural networks, and wavelet analysis. However, complex equipment and large numbers of calculations are needed in these works, and most of the present identification methods are valid only under certain flow conditions; only a few of them can be implemented in an industrial environment. Therefore, flow-regime identification is still of great interest today. Differential pressure is a fast-response signal in two-phase flow. It is highly suitable for use in identifying flow regimes

One of the most widely used complex signal processing operations is filtering, whose main objective is to alter the spectrum according to some given specifications. The system implementing this operation is called a filter. For example, a filter might be designed to pass certain frequency components in a signal through the system and to block other frequency components. The range of frequencies of the signal components allowed to pass through the filter is called the passband, and the range of frequencies of the signal components blocked by the filter is called the stopband. A filter is the system that removes the noise. A simple moving average acts as a simple low-pass filter eliminating the noise. To convert an analog signal into digital signal, the interfaces are sampling, quantizing, and coding, as shown in Figure 3b. The sampling rate, or sampling frequency, defines the number of samples per second taken from a continuous signal to make a discrete signal. In digital signal processing, quantization is the process of approximating (“mapping”) a continuous range of values (or a very large set of possible discrete values) by a relatively small (“finite”) set of (“values that can still take on a continuous range”) discrete symbols or integer values. The final step required of converting an analog signal to a form acceptable to digital computer is called coding. Actuators are devices that transform an input signal (mainly an electrical signal) into motion, and the architecture of digital signal processing is shown in Figure 3c. Analog Input and Output An analog input is a measurable electrical signal with a defined range that is generated by a sensor and received by a controller. The analog input changes continuously in a definable manner in relation to the measured property. The analog signals generated by some types of sensors must be conditioned by converting to a higher-level standard signal that can be transmitted over wires to the receiving controller. Analog inputs are converted to digital signals by the analog-to-digital (A/D) converter typically located at the controller. There are basically three types of analog input signals: voltage, current, and resistance. An analog output is a measurable electrical signal with a defined range that is generated by a controller and sent to a controlled device, such as a variable-speed drive or actuator. Changes in the analog output cause changes in the controlled device that result in changes in the controlled process. There

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are four common types of analog outputs: voltage, current, resistance, and pneumatic. Triggers An analog input trigger is defined as an event that initiates the logging of data to memory and/or a disk file. The logging status is indicated by the logging property. When a trigger occurs, the logging property is set to on. The destination for logged data is indicated by the logging mode property. You can log data to memory or a disk file. By default, the data are logged to memory. An analog input object can log data using an immediate trigger, a manual trigger, or a software trigger. The trigger type is indicated by the trigger type property. An immediate trigger is the default trigger type. An analog output trigger is defined as an event that initiates the output of data. An analog output object can output data using an immediate trigger or a manual trigger. An immediate trigger begins logging data immediately after the start command is issued. The number of data samples that the trigger will acquire is indicated by the samples per trigger property. A manual trigger begins logging data after you manually issue the trigger command. The data acquisition engine begins running as soon as the start command is issued. However, the data samples are not stored in the data acquisition engine until the trigger command is issued. Therefore, the number of samples available from the data acquisition engine is zero before the trigger command is issued. A software trigger begins logging data when a signal satisfying the specified condition is detected on one of the specified channels. The channel used as the trigger source is defined by the trigger channel property. The condition that must be satisfied for a trigger to occur is specified by the trigger condition property. This property can be set as one of the following values: rising, in which case the signal must be above the specified value and rising; falling, in which case the signal must be below the specified value and falling, leaving; in which case the signal must be leaving the specified range of values; and entering, in which case the signal must be entering the specified range of values. The specified value or range that the trigger condition must meet is indicated by the trigger condition value property. Triggers can be configured to occur repeated times. Trigger repeats are controlled by the trigger repeat property. When the trigger repeat property is set to its default value of 0, the trigger will occur once when the trigger condition is met. If the trigger repeat property is set to a positive integer, then the trigger is repeated the specified number of times when the trigger condition is met. If trigger repeat is set to infinite, then the trigger repeats continuously when the trigger condition is met, and the data acquisition can be stopped only with the stop command. With an immediate trigger, each trigger will occur immediately after the previous trigger has finished executing.

Figure 4. Schematic representation of the experimental setup.

places, the more likely the ground level wills be the same. When a connection is made between two grounds, the difference in levels can drive large currents, known as earth or ground loops. This can lead to errors when single-ended inputs are used. Single-ended inputs are sensitive to noise errors. Noise (unwanted signal contamination) is added because signal wires act as aerials, picking up environmental electrical activity. With single-ended inputs, there is no way of distinguishing between the signal and the noise. The ground and noise problems can be solved by differential inputs. With differential inputs, two signal wires run from each signal source to the data acquisition interface. One goes to a positive input, and one goes to a negative input. Two high-impedance amplifiers monitor the voltage between the input and the interface ground. The outputs of the two amplifiers are then subtracted by a third amplifier to give the difference between the positive and negative inputs, meaning that any voltage common to the two wires is removed. This can solve both of the problems caused by single-ended connections: It means that differences in grounds are irrelevant (as long as they are not too large for the amplifier to handle), and it also reduces noise, as twisting the wires together will ensure that any noise picked up will be the same for each wire. Experimental Setup and Techniques

Differential and Single-Ended Inputs Analog signals (temperature, strain, vibration, etc.) can be connected in either single-ended or differential mode. With single-ended inputs, you connect one wire from each signal source to the data acquisition interface. The measurement is the difference between the signal and the ground or earth at the data acquisition interface. This method relies on the signal source being grounded (earthed) and the signal source’s ground and the data acquisition interface’s ground having the same value. assume The ground is generally assumed as a constant 0.0 V, but in reality, the ground, or earth, is at a different levels in different places. The closer together the

A schematic of the experimental setup is shown in Figure 4 and mainly comprises a long horizontal pipe, a pump, a rotameter, a pressure transmitter, a computer with a data acquisition card to acquire online data from the pressure transmitter, and a collection and supply tank. The pipe is of transparent perspex with a long straight structure having an inner diameter (i.d.) of 0.0254 of 0.0127 m, and the length of the pipe is 2 m. A high-capacity tank is provided to collect the recycled liquid from the outlet of the pipe. A centrifugal pump is provided to feed the process liquid. A rotameter is installed to measure total inlet volumetric flow rate. The volumetric flow rate of feed liquid can be maintained by regulating the flow


through a globe valve between the centrifugal pump and the tank. Arrangements were made for operation with both 0.0254and 0.0127-m-i.d. pipes when required. During collection of data for the 0.0254-m-i.d. pipe, the inlet to 0.0127-m-i.d. pipe must be closed (and vice versa) with the help of gate valves V5 and V7. To restrict the back-flow, nonreturn valves V6 and V8 are provided. An air compressor is provided to feed air into the pipeline. A rotameter is installed to measure the total inlet volumetric flow rate. The volumetric flow rate of air feed can be maintained by regulating the flow through a globe valve between the air compressor and the rotameter. Similarly to the liquid flow, arrangements were made for operation with both 0.0254- and 0.0127-m-i.d. pipes when required. During collection of data for the 0.0254-m-i.d. pipe, the inlet to 0.0127-m-i.d. pipe must be closed (and vice versa) with the help of gate valves V1 and V3. To restrict the back-flow, nonreturn valves V2 and V4 are provided. A pressure transmitter is connected to the pipe at the test section (b1 or b2) to acquire the pressure signal continuously, and these data are continuously acquired in real time using the National Instruments data acquisition card to analyze the different flow regimes. Basically, the signals were transported to a sensor by a thin poly(propylene) tube, which eliminated other noises, to a computer with minimum length of pipe to prevent transportation lag and loss of data. The rotameters were calibrated, and the flow rates of water and air were adjusted to form specific flow regimes (bubbly flow, stratified smooth flow, stratified wavy flow, slug flow). Valves V7 and V3 were opened as valves V1 and V5 were simultaneously closed. The pressure transmitter was switched on and connected to the 0.0127-m-i.d. perspex pipe. Then, Matlab was opened, and the program was run to observe the signal. The pressure transmitter sensed the pressure in the pipe at the test section, which is related to the pressure inside the pipe, and thus information about the flow regime was conveyed to the computer by the connected data acquisition card. The Matlab code processed the real-time data. At the beginning of data processing, Camtasia Studio software was opened and used to record the real-time data and graph. After the signal had been saved, the experiment was continued by adjusting the flow regime and collecting the signal data. Different signals were stored for different flow regimes. Based on the knowledge acquired, signals of an unknown flow regime can be identified after carrying out an experiment with air and water flowing in a horizontal pipe. In such as experiemnt, the Matlab code processes the real-time data by the cross-correlation method. It correlates every time with each trigger. The unknown signal is correlated with each signal stored in the program, and an output is generated. The flow regime having the maximum correlation value is matched with the unknown flow regime. This flow regime is the desired flow regime.

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Figure 5. Algorithm for predicting flow regimes.

pressure transmitter connected to the pipe, and the sink was a channel associated with an analog input object. Data Processing and Analysis In many experiments in physical science, the true signal amplitudes (y-axis values) change rather smoothly as a function of the x-axis values, whereas many kinds of noise get interrupted. The result is rapid, random changes in amplitude from point to point within the signal. Noise is considered to be any measurement that is not part of the phenomenon of interest. Noise can be generated within the electrical components of the input amplifier (internal noise), or it can be added to the signal as it travels down the input wires to the amplifier (external noise). The first step is to eliminate the noise; this is achieved by performing a smoothing operation. In smoothing, the data points of a signal are modified so that individual points that are higher than the immediately adjacent points (presumably because of noise) are reduced, and points that are lower than the adjacent points are increased. This naturally leads to a smoother signal. The simplest smoothing algorithm is the simple moving average; it is simply the unweighted mean of the previous n data points. For example, a simple moving average with a smooth width of 10 is given by SMA )

pM + pM-1 + · · · +pM-9 10

SMApresent ) SMAprevious -

pM+1 pM-n+1 + n n

(1)

(2)

Software Development

Correlation of Two Sequences

The programming was done using Matlab 7.4. The data were acquired using a National Instruments PCI-6221 data acquisition card. To acquire data from the data acquisition card, a data source, a data sensor, and a data sink are needed. Here, the data source was the voltage input to the data acquisition card coming from the pressure inside the pipe. The sensor was a

A mathematical operation known as correlation is basically used to compare two signals. It occupies a significant place in signal processing. It has applications in radar and solar systems where the location of the target is measured by comparing incoming and reflected signals. Additional applications of correlation are in image processing and control

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Figure 6. Real-time graph showing the pressure transmitter connected to a 0.0127-m pipe at a feed velocity of 5.25 m/s.

Figure 7. Real-time (a) image and (b) graph showing stratified smooth flow, at a liquid velocity of 5.25 m/s and a gas velocity of 0.263 m/s.

engineering, among others. Correlation is a measure of the degree to which two signals are similar. The correlation of two signals is divided into cross-correlation and autocorrelation. The cross-correlation between a pair of signals x(n) and y(n) is given by ∞

Yxy(l ) )

∑

x(n) y(n - l)

If we wish to fix y(n) and to shift x(n), then the correlation of two sequences can be written as ∞

∞

Yyx(l) )

∑

y(n) x(n - l) )

∑

y(n + l) x(n)

(4)

n)-∞

n)-∞

If the time shift is l ) 0, then we obtain

l ) 0, (1, (2, (3, ...

∞

n)-∞

(3) The index l is the shift (lag) parameter. The order of subscripts xy indicates that x(n) is the reference sequence that remains unshifted in time whereas the sequence y(n) is shifted by l units in time with respect to x(n).

Yxy(0) ) Yyx(0) )

∑

x(n) y(n)

(5)

n)-∞

The autocorrelation of a sequence is the correlation of a sequence with itself. The autocorrelation of a sequence x(n) is defined as

Ind. Eng. Chem. Res., Vol. 49, No. 6, 2010 ∞

Yxx(l) )

∑

x(n) x(n - l)

(6)

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start button was pressed and stopped when the stop button was pressed.

n)-∞

Results and Discussion or equivalently ∞

Yxx(l) )

∑

x(n + l) x(n)

(7)

n)-∞

If the time shift is l ) 0, then we have ∞

Yxx(0) )

∑

x2(n)

(8)

n)-∞

The entire algorithm used to compute the cross-correlation of sequences x(n) and y(n) is shown in Figure 5. It shows the step-by-step procedure of the program once the raw data have been obtained. It differentiates one-phase flow and twophase flow according to the smoothed data. One can understand the signal processing and analysis of the program and also how the flow regimes can be easily differentiated. For visualizations of the acquired data and variations taking place therein, three different graphs were plotted in a single window. The first graph indicates the raw data as a function of the number of samples, the second shows the smoothed data as a function of the number of samples, and the third shows the slope as a function of the number of samples. To make the system more user-friendly, a start/stop button was added so that data logging would take place as soon as the

Digital signal processing techniques have been used to recognize flow patterns in horizontal pipe lines. The horizontal perspex pipe was connected to a pressure transmitter for data acquisition at the desired gas and liquid flow rate. The pipe was kept full of water, and the signal was captured as shown in Figure 6, which contains three subgraphs. In all of the subgraphs, the x axis represents the number of samples. In the first and second subgraphs, the y axis refers to voltage. The first subgraph refers to raw data, and the second refers to smoothed data, whereas the third refers to slope. In the first and second subgraphs, voltage is negative as water alone is present (single-phase flow). The pipe was connected to the pressure transmitter after being filled with air and water, and the flow rates were adjusted to obtain a stratified smooth flow regime as shown in Figure 7a. From Figure 7a, the flow regime can be identified as stratified smooth flow at a water superficial velocity of 5.25 m/s and a gas superficial velocity of 0.263 m/s. The signal obtained for the stratified smooth flow was recorded and smoothed. The slope was determined for the smoothed data to differentiate the flow regimes. Figure 7b contains three subgraphs. In all of the subgraphs, the x axis represents the number of samples. In the first and second subgraphs, the y axis refers to voltage. The first subgraph refers to raw data, and the second refers to smoothed data, whereas the third refers to slope. When air was introduced into the pipe along with water, two-phase flows existed. The voltage changed from negative to positive when single-phase flow changed to two-phase flow.

Figure 8. Real-time (a) image and (b) signal of stratified wavy flow, at a water velocity of 5.25 m/s.

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Figure 9. Real-time (a) image and (b) graph showing elongated bubbly flow at a water velocity of 5.25 m/s.

Figure 10. Real-time (a) image and (b) graph showing slug flow at a feed velocity of 5.25 m/s and an air velocity of 2.625 m/s.

To get the wavy flow regime, the horizontal 0.0127-m-i.d. pipe was connected to the pressure transmitter, the pipe was filled

with air and water, and the flow rates were adjusted to get stratified wavy flow regime as shown in Figure 8a. The signal


obtained is plotted in Figure 8b and is similar to the previous trend for stratified flow. When air was introduced into the pipe along with water, two-phase flow existed. The voltage changed from negative to positive when single-phase flow changed to two-phase flow. From Figure 8a, the flow regime can be identified as stratified wavy flow at a water velocity of 5.25 m/s and an air velocity of 0.656 m/s. The signal obtained for the stratified smooth flow was recorded and smoothed. The slope was determined for the smoothed data to differentiate the flow regimes. An elongated bubbly flow regime was obtained at adjusted flow conditions as shown in Figure 9a. A continuous signal was monitored and analyzed for flow-regime prediction at a water velocity of 5.25 m/s. The slope was determined for the smoothed data to differentiate the flow regimes. It is interesting to note that, from the signal obtained, the slope was always decreasing in nature, and the magnitude was on the order of 5 × 10-3. In a similar process at adjusted gas and liquid superficial velocities, the slug-flow regime was obtained as shown in Figure 10a. In Figure 10b, the signals obtained and smoothed are presented with the generated slope. In the first and second subgraphs, the y axis refers to voltage. The first subgraph refers to raw data, and the second refers to smoothed data, whereas the third refers to slope. When air was introduced into the pipe along with water, two-phase flows existed. The voltage changed from negative to positive when single-phase flow changed to two-phase flow. From the above figure, the flow regime can be identified as slug flow at a water velocity of 5.25 m/s and an air velocity of 2.625 m/s. The signal obtained for the stratified smooth flow was recorded and smoothed. The slope was determined for the smoothed data to differentiate the flow regimes. Pressure difference is written as a function of voltage pin - pu ) 2 × 109x3 - 3 × 107x2 + 79220x + 14606

(9) where x is the voltage. There is no direct relationship between the pressure and the voltage. To correlate the data collected at various pressures, one needs to measure pressure differences at different flow rates using a manometer and simultaneously collect voltage values using a pressure transmitter. A plot of the values of voltage and pressure difference are shown in Figure 11. It can be seen from this figure that the pressure difference increased with increase in voltage, and at a voltage of 0.004, there was a change in pressure difference. A comparison of single and two-phase flow regimes under various flow conditions is presented in Table 1. Conclusions An experimental and theoretical investigation of horizontal gas-liquid two-phase flow for the identification of flow regimes using a new digital signal processing technique has been presented. A horizontal pipe equipped with a pressure transmitter and a data acquisition card was used to analyze the flow regimes. A Matlab code was written to obtain the signal from the acquired data. The code successfully identified the changes in the system, whenever there was a change in the system in terms of voltage and the corresponding change in voltage value is clearly displayed in the graph. The pipe connected to the pressure transmitter can measure the pressure continuously, and the data acquired in real time using the National Instrument data acquisition card can be used to analyze the flow regimes formed inside the perspex pipe. The results indicate that increasing the

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Figure 11. Graph showing the pressure difference as a function of voltage. Table 1. Comparison of Single- and Two-Phase Flow Regimes flow regime

data values

slope

Single-Phase Flow water stratified smooth flow

negative positive, negative

0.02 0.02

Two-Phase Flow wavy stratified flow bubbly flow slug flow

positive, negative positive positive

0.05 0.005 0.02

superficial gas velocity at constant water superficial velocity gives rise to different flow regimes in the horizontal pipe. One can determine which flow regime has formed and can take appropriate actions to adjust the flow regimes, so that separation efficiency can be increased, as separation efficiency is related to the flow regime. Different flow regimes have different separation efficiencies. When the experiment was conducted by increasing the gas velocity keeping the liquid velocity constant, the Matlab window continuously showed which type of flow regime was present in the perspex pipe. By knowing the flow regime in the pipe, it is possible to easily control the separation efficiency. Whenever an additional phase that is air gets introduced, it is interesting to note that there is a significant change in the voltage value, which indicates that a change took place in the system and predicts the existence of two-phase flow. According to the smoothed data and the slope, it is easy to differentiate the flow regimes by coding. All flow regimes can be differentiated not only by coding but also by the crosscorrelation method. Thus, the digital signal processing technique is a unique method for identifying flow regimes for unknown systems and flow regimes that has tremendous potential in the petroleum refinery and chemical process industries. Notation ADC ) advanced data connector DAQ ) data acquisition ISA ) industry standard architecture PCI ) peripheral component interconnect RAM ) random-access memory (pin- pu) ) pressure difference between the inlet and the center (Pa) pin ) pressure at the inlet (Pa)

3010


pu ) pressure at the center (Pa) rin ) inlet pipe radius (m) u ) axial velocity (m/s) uin ) average velocity in inlet pipe (m/s) V ) radial velocity (m/s) x ) pressure transmitter data (V) µm ) effective viscosity of the suspension [kg/(m s)] Fl ) liquid density (kg/m3) Fm ) suspension density (kg/m3)

Literature Cited (1) Abramson, A. E. Investigation of annular liquid flow with co-current air flow in horizontal tubes. J. Appl. Mech. 1952, 19, 267–274. (2) Anderson, G. H.; Mantzouranis, B. G. Two-phase (gas-liquid) flow phenomenasI. Pressure drop and holdup for two-phase flow in vertical tubes. Chem. Eng. Sci. 1960, 12, 109–126. (3) Mishima, K.; Ishii, M. Flow regime transition criteria for upward two-phase flow in vertical tubes. Int. J. Heat Mass Transfer 1983, 27, 723– 737.

(4) Xie, T.; Ghiaasiaana, S. M.; Karrila, S. Artificial neural network approach for flow regime classification in gas-liquid-fiber flows based on frequency domain analysis of pressure signals. Chem. Eng. Sci. 2004, 59, 2241–2251. (5) Shim, J. W.; Chul, H. J. Analysis of pressure fluctuations in twophase vertical flow in annulus. Ind. Eng. Chem. Res. 2000, 6, 167–173. (6) Taitel, Y.; Dukler, A. E. A model for predicting flow regime transitions in horizontal and near-horizontal gas-liquid flow. AIChE J. 1976, 22, 47–55. (7) Aldorwish, Y.; Kim; S.; Ishii, M. Two-phase flow pattern identification using a fuzzy methodology. In 1999 International Conference on Information Intelligence and Systems (ICIIS’99); IEEE Press: Piscataway, NJ, 2002; p 155. (8) Selli, M. F.; Seleghim, P. Online Identification of Horizontal TwoPhase Flow Regimes through Gabor Transform and Neural Network Processing. Heat Transfer Eng. 2007, 28, 541–548.

ReceiVed for reView December 5, 2009 ReVised manuscript receiVed January 25, 2010 Accepted January 28, 2010 IE9019215

Development of Soft Sensor to Identify Flow Regimes in Horizontal

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