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An artificial intelligence based improved classification of two-phase flow patterns with feature extracted from acquired images

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Flow pattern recognition is necessary to select design equations for finding operating details of the process and to perform computational simulations. Visual image processing can be used to automate the interpretation of patterns in two-phase flow. In this paper, an attempt has been made to improve the classification accuracy of the flow pattern of gas/ liquid two- phase flow using fuzzy logic and Support Vector Machine (SVM) with Principal Component Analysis (PCA). The videos of six different types of flow patterns namely, annular flow, bubble flow, churn flow, plug flow, slug flow and stratified flow are re- corded for a period and converted to 2D images for processing. The textural and shape features extracted using image processing are applied as inputs to various classification schemes namely fuzzy logic, SVM and SVM with PCA in order to identify the type of flow pattern. The results obtained are compared and it is observed that SVM with features reduced using PCA gives the better classification accuracy and computationally less intensive than other two existing schemes. This study results cover industrial application needs including oil and gas and any other gas-liquid two-phase flows.

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An artificial intelligence based improved classification of two-phase flow patterns with feature extracted from acquired images

  1. 1. Research article An artificial intelligence based improved classification of two-phase flow patterns with feature extracted from acquired images C. Shanthi n , N. Pappa Department of Instrumentation Engineering, MIT Campus, Anna University, Chennai, India a r t i c l e i n f o Available online 13 February 2017 Keywords: Gas-liquid flow pattern Image processing Fuzzy logic Support vector machine Principal component analysis a b s t r a c t Flow pattern recognition is necessary to select design equations for finding operating details of the process and to perform computational simulations. Visual image processing can be used to automate the interpretation of patterns in two-phase flow. In this paper, an attempt has been made to improve the classification accuracy of the flow pattern of gas/ liquid two- phase flow using fuzzy logic and Support Vector Machine (SVM) with Principal Component Analysis (PCA). The videos of six different types of flow patterns namely, annular flow, bubble flow, churn flow, plug flow, slug flow and stratified flow are re- corded for a period and converted to 2D images for processing. The textural and shape features extracted using image processing are applied as inputs to various classification schemes namely fuzzy logic, SVM and SVM with PCA in order to identify the type of flow pattern. The results obtained are compared and it is observed that SVM with features reduced using PCA gives the better classification accuracy and computationally less intensive than other two existing schemes. This study results cover industrial ap- plication needs including oil and gas and any other gas-liquid two-phase flows. & 2017 ISA. Published by Elsevier Ltd. All rights reserved. 1. Introduction Gas/liquid flow is a type of flow encountered in applications such as oil and gas, power plant and chemical industries etc. Many researchers have carried out flow pattern identification work on two-phase flow in pipelines [1–3]. The detection of two-phase flow patterns is mainly performed by either visual observation or flow signals processing. The flow signals include pressure and velocity variations within the flow. Visual observation is the most preferred method for gas-liquid flow pattern identification. This method of flow pattern classification purely depends upon in- dividual's interpretation of images. Hence there are numerous methods for classification followed for these flows. In a horizontal pipe, phase separation occurs when gravity acts perpendicular to the tube axis. Typical flow patterns observed in industries are annular flow, bubbly flow, churn flow, plug flow, slug flow and stratified flow. Annular flow is a flow pattern where liquid flows on the wall of the pipe and gas flows in the centre. In bubbly flow, liquid is continuous and there is a dispersion of bubbles within the liquid. Churn flow is a kind of flow pattern where the liquid is unstable or oscillating. In Plug flow pattern, the bubbles have coalesced to make a larger bubble which approaches the diameter of the pipe. Slug flow finds its application in oil industry in pipelines carrying oil and natural gas. In stratified pattern, the horizontal interface is smooth. Hence the liquid and gas are completely stratified in this regime. There are difficulties on flow pattern maps with most of the existing literature. These maps are dimensional dependent and therefore apply only to the specific pipe sizes and fluids employed by the investigator. It is difficult to have a generalized flow pattern map for different set of fluids and pipe sizes. Because one transi- tion might occur at a Weber number whereas another boundary may occur at a Reynolds number. Hence, there exist no universal dimensionless flow pattern maps for fluids. Fig. 1 shows the oc- currence of different flow patterns for the flow of air/water two- phase flow in a horizontal 5 cm pipe. The flow regime identification of gas/liquid flow in vertical pipe was carried out using feature extraction based Support Vector Machine and Neural network [4]. The two-phase gas-liquid flow pattern in an upriser pipe of an airlift pump has been investigated using image processing techniques.Four patterns bubbly, slug, churn and annular flow were recognized. The performance of the airlift pump in different submergence ratios was measured [5] and compared with the flow pattern map of Hewitt and Roberts [6]. Based on the textural features and SVM, flow classification was proposed in [7]. The flow parameters are measured by image analysis and the uncertainty associated with the measured para- meters also calculated [8]. Flow pattern identification was carried out using distance evaluation method and Support Vector Machine Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/isatrans ISA Transactions http://dx.doi.org/10.1016/j.isatra.2016.10.021 0019-0578/& 2017 ISA. Published by Elsevier Ltd. All rights reserved. n Corresponding author. E-mail address: cgshanthi@gmail.com (C. Shanthi). ISA Transactions 68 (2017) 425–432
  2. 2. [9]. Fuzzy Neural Network was used to identify the flow pattern of bubbly, slug and plug flow [10]. Experimental study was carried out to identify the flow patterns in the wake of a Taylor bubble rising through vertical columns of stagnant and flowing liquids [11]. The flow images are visualized and the patterns were clas- sified using different techniques [12–17]. Gas/liquid two-phase flow pattern identification is done in order to improve the flow performance and flow parameter measurement. Three major flow patterns namely, bubbly, slug and churn were identified using the data collected from Electrode Resistance Tomography (ERT) sys- tem. 1D and 2D wavelet transform has been used to extract fea- tures from ERT data [18]. Acoustic Emission (AE) system combined with artificial neural network was used to recognize the flow patterns in an air-water vertical two-phase flow column [19]. A study of the evolution of slug flow parameters was carried out in vertical pipe. The local void fraction was measured using fibre optic probes [20]. A flow regime map for the flow of air-water mixture in a horizontal pipe has been discussed [21]. In the pre- sent work, a fuzzy logic system and SVM with PCA is proposed to identify the gas-liquid flow patterns. 2. Experimental setup The experimental setup of liquid-gas flow measurement is gi- ven in Fig. 2. Atmospheric air is compressed and passed through the air flow meter (Process Line size:2”,Flow rate: 0.1–16 m3 /h.) with the regulated valve in the air line. At the same time water from overhead tank is regulated and is sent through the flow meter (Process Line size:2”,Flow rate: 1–80 m3 /h.) to the water line. At the T-joint air and water gets mixed together and is al- lowed to flow through the transparent glass pipe. Depending upon the different air flow rates, different flow patterns are obtained with constant water flow rate of 150lpm and are tabulated in Table 1. Since there is possibility of liquid pressure to be higher than the air pressure, there is a non-return valve connected to the air flow side to prevent the liquid from entering the air flow side. The rotameter for measuring liquid flow rate is acrylic type. Both flow meters are at ambient temperature and have no pressure loss. Digital camera Nikon Coolpix p5.10 is used for capturing the videos at a speed of 25 frames per second of liquid/gas two-phase flow patterns. The six different types of flow patterns identified in this paper are given in Fig. 3. 3. Image processing Inferential measurement based on image analysis is a powerful technique to identify the flow patterns of gas-liquid flow. There are a number of techniques in digital image processing which can be used to obtain desired results. The images of the six different flow patterns taken under study are acquired. The frames are loaded in such a way that they are available continuously one after the other for the successive processing techniques. The frames of Red Green Blue (RGB) images of the six different types of flow patterns loaded are shown in Fig. 2. The RGB images are converted to gray scale images (0 to 255) using weighted or luminosity method. This method converts RGB to gray images by eliminating the hue and saturation while retaining the luminance. Gray images are then converted into black and white images by global thresholding that classifies pixels into two categories (black and white). Binary images contain numerous imperfections. In parti- cular, binary regions produced by thresholding are distorted by Fig. 1. Flow pattern map for the gas /liquid flow in a horizontal 5 cm pipe. Fig. 2. Experimental set up. (a) Schematic diagram of the experimental set up. (b) Photo of the experimental set up. Table 1 Nominal air flow rates for flow patterns. S.No. Type of flow pattern Flow rate of Air (lpm) 1. Bubbly flow 20 2. Plug flow 22 3. Slug flow 30 4. Churn flow 35 5. Annular flow 40 6. Stratified flow 45 C. Shanthi, N. Pappa / ISA Transactions 68 (2017) 425–432426
  3. 3. noise and texture. Morphological image processing operations (Erosion and dilation) are used to remove these imperfections. 4. Fuzzy logic Fuzzy logic converts linguistic strategy to automatic control strategy based on expert knowledge. The fuzzy logic system comprises of four components; Fuzzification, Knowledge Base, Decision making logic and Defuzzification. From the morpholo- gically processed images, the maximum and minimum object width is calculated by finding the longest and the shortest dis- tance between two white pixels. The obtained values for the re- corded images acquired as video for 5 sec. time period are given in Table 2. These values are used to design the fuzzy logic system. Fuzzification converts the input data to linguistic variables. In this work, fuzzification is performed for the maximum and minimum widths denoted by W1 and W2. Triangular membership functions are used. Fig. 4. shows the input membership functions for W1 and W2. Five different linguistic variables namely Very Small (VS), Small(S), Medium (M), Large (L), Very Large (VL) are assigned for each membership function. The knowledge base is a rule base. The rules are expressed with syntax like: IFofuzzy proposition4THEN o fuzzy proposition4. The Knowledge base is modelled for the fuzzified values of W1 and W2. Twenty five rules are framed to identify the six different flow patterns. The rule base thus created is given in Table 3. Once the rules are framed a rule base matrix is obtained. From this matrix it can be seen that each output depends on number of rules. This gives rise to ambiguity which can be avoided by deci- sion making. In decision making all the possible combinations of inputs for each output are grouped together to form a single rule. Thus decision making is the process of obtaining a single truth value. The number of rules obtained as a result of decision making is equal to the number of output. Decision making is the process of considering the input value and all rules including the aggregation of their output into a single output value. Thus six set of rules were formulated for the six different flow patterns as shown in Table 4. Defuzzification results a crisp, non-fuzzy parameter from an in- ferred fuzzy value. In this paper, the centre of area method is considered as the defuzzification strategy. Defuzzification is per- formed for each membership function to convert the values of the linguistic variables into crisp values. Fig. 5 shows the defuzzified output. The fuzzy logic system is validated with 20 frames of Fig. 3. Sample images extracted from video for six different flow patterns. Table 2 Ranges of Maximum and Minimum object widths (W1 and W2). S.No. Type of flow pattern Maximum object width W1 ( pixels) Minimum object width W2 ( pixels) 1. Bubbly Flow 10–38 5–15 2. Plug Flow 36–60 26–38 3. Slug Flow 70–92 35–48 4. Churn Flow 58–74 28–38 5. Annular Flow 60–90 15–25 6. Stratified Flow 40–60 40–60 Fig. 4. Input membership functions. (a). Membership functions for Maximum width (W1). (b). Membership functions for Minimum width (W2). Table 3 Formulated rule base for different flow patterns. W2 W1 Very Small Small Medium Large Very Large Very Small Bubbly Bubbly Annular Annular Annular Small Bubbly Bubbly/Slug Annular Annular Annular Medium Bubbly Plug Stratified Churn Slug Large Bubbly Plug/ Slug Stratified Churn Slug Very Large Bubbly Slug Stratified/ Annular Churn/slug Slug C. Shanthi, N. Pappa / ISA Transactions 68 (2017) 425–432 427
  4. 4. images of different flow patterns and the classification efficiency of the system is tabulated in Table 7. There is some difficulty in identifying few images due to certain changes in conditions while capturing the images. 5. Feature extraction Feature extraction involves simplifying the amount of resources required to describe a large set of data accurately. In this paper, 120 frames of images for the six different types of flow patterns are considered. Seventeen textural features and seven shape features have been extracted from the preprocessed images. Hence, totally 24 different features have been extracted and the average of each feature value over the 120 images are computed. 5.1. Textural feature extraction The texture of an image is determined by the way the gray levels are distributed in the region. It contains important in- formation about structural arrangement of surfaces and their re- lationship with surrounding environment. Although it is easy for human observers to recognize and describe in empirical terms, texture has been extremely refractory to precise definition and to analysis by digital computers. Since the textural properties of images appear to carry useful information for discrimination purpose, it is important to develop features for textures. In this paper, textural feature with first order statistics was considered. These features depend only on individual pixel values and not on the interaction or co-occurrence of neighboring pixel values. The first order statistics involves histogram features and statistical gray level features. Seventeen textural features such as average gray level, max- imum gray level, minimum gray level, standard deviation, var- iance, etc. are extracted from the gray scale images of different flow patterns. The average value of the extracted textural features is listed in Table 5. 5.2. Shape feature extraction Efficient shape features must possess certain essential proper- ties. They should be identifiable because shapes which are found perceptually similar by human have the same feature different from others. The rotation, location and scaling changing of shape must not affect the extracted features. Seven different shape fea- tures are extracted from the images of the different flow patterns. The shape features such as circularity, maximum bubble size, area of the bubbles, etc. are extracted and average value for 120 images are listed in Table 6. 6. Pattern classification using SVM Support Vector Machine (SVM) is a supervised learning algo- rithm suitable for classification. SVMs give good performance in many real time applications. Sequential Minimum Optimization (SMO) is the SVM training algorithm [22] used in this paper. SMO solves the Lagrange multipliers analytically. The output of SVM is computed from Lagrange multipliers. ∑ α= ( ̅ ̅)– = y k x xu d, j N j j j 1 , Table 4 Fuzzy based decision making for different flow patterns. S.No. Flow pattern Decision 1. Bubbly flow W2 is Small or Medium or Large or Very Large and W1 is Very Small or Small 2. Plug flow W2 is Medium or Large or Very Large and W1 is Small 3. Slug flow W2 is Medium or Large or Very Large and W1 is Very Large 4. Churn flow W2 is Medium or Large or Very Large and W1 is Large 5. Annular flow W2 is Very Small or Small and W1 is Large or Very Large 6. Stratified flow W2 is Medium or Large or Very Large and W2 is Medium Fig. 5. Membership functions for different types of flow patterns. Table 5 Average value of textural features extracted for the recorded video. Features Flow pattern Bubbly flow Plug flow Slug flow Churn flow Annular flow Stratified flow 1. Average gray level of the image 252.66 204.21 253.88 204.09 173.66 253.91 2. Maximum gray level 243.11 230.43 250.02 235.01 245.31 253.20 3. Minimum gray level 12.70 50.66 13.52 48.92 48.08 30.04 4. Variance 260.84 257.06 250.00 252.73 255.01 258.08 5. Standard deviation 16.12 16.03 15.81 15.87 15.97 16.06 6. Skewness 0.0002 0.0016 0.0015 0.0012 0.0020 0.0011 7. Kurtosis 0.0071 0.0097 0.0021 0.0105 0.0074 0.0213 8. Modified standard deviation 24.54 24.13 23.79 24.07 24.22 24.59 9. Modified skew 0.0012 0.0014 0.0001 0.0014 0.0014 0.0014 10. Entropy 6.71 6.59 7.20 7.30 6.48 7.55 11. Standard deviation of histogram features 74.13 50.78 105.92 96.16 52.71 129.42 12. Range 175.06 122.02 231.09 193.02 126.51 251.72 13. Smoothness 0.998 0.9996 0.9998 0.999 0.9997 1.000 14. Average histogram 115.08 89.5 118.02 93.00 98.00 124.5 15. Uniformity 40.29 19.27 44.09 21.62 25.29 15.77 16. Local entropy 6.02 5.94 6.08 5.91 5.93 5.82 17. Third moment 5.14 5.00 5.36 5.09 5.02 5.56 C. Shanthi, N. Pappa / ISA Transactions 68 (2017) 425–432428
  5. 5. where k is a Kernel function that measures the similarity of ̅ ̅x and xj. The Lagrange multipliers are computed through a quad- ratic program. The dual objective function ψ is given by ( )∑ ∑ ∑ψ α α α α α( ̅) = ̅ ̅ − ≤ ≤ ∀ = = = y y k x x C 1 2 0 , i N j N i j i j i j i N i i i 1 1 , 1 ∑ α = ( )= y 0 1i N i i 1 where N - Number of training samples. C is the trade-off between training error and generalization factor. The Karush- Kuhn- Tucker (KKT) conditions for Eq. (1), α α α= ⇔ ≥ < < ⇔ = = ⇔ ≤y u C y u C y u0 1,0 1 and 1i i i i i i i i i where ui is the output of the SVM for ith training. The SMO algorithm first computes second Lagrange multiplier α2. If the target y1 ♯ y2, then α α α α= ( − ) = ( + − )CL max 0, , H min C,2 1 2 1 If y1 ¼Target y2, then α α α= ( + − = ( + )H KL max 0, C, min C,2 1 2 1 The second derivative of the objective function can be written as η= ( ̅ ̅ ) + ( ̅ ̅ )– (( ̅ ̅ )K x x K x x x x2K1, 1 2, 2 1, 2 Under normal conditions, the objective function will be positive and η will be greater than zero. Now, SMO computes the minimum along the direction of the constraint. α α η = + ( − )y E ENew 2 2 2 1 2 Where Ei ¼ui –yi is the ith sample training error. ⎧ ⎨ ⎪⎪ ⎩ ⎪ ⎪ α α α α α = ≥ < < ≤ H if H if L H L if L New Clipped New New New New 2 , 2 2 2 2 Consider S¼ αy y and1 2 1 is computed from αNew clipped 2 , ( )α α α α= + −sNew New clipped 1 1 2 2 , SMO algorithm will be suitable even when η is negative, in such case ψ should be evaluated at the end of each line segment. ( ) α α= + − ( ̅ ̅ ) − ( ̅ ̅ )f y E b K x x s K x x1 1 1 1 1, 1 2 1, 2 ( ) α α= + − ( ̅ ̅ ) − ( ̅ ̅ )f y E b s K x x K x x2 2 2 1 1 , 2 2 2 , 2 α α= + ( − )L s L1 1 2 α α= + ( − )H s H1 1 2 ψ = + + ( ̅ ̅ ) + ( ̅ ̅ ) + (( ̅ ̅ )L f Lf L K x x L K x x K x x 1 2 1 2 SLLL 1 1 2 1 2 1, 1 2 2, 2 1 1, 2 ψ = + + ( ̅ ̅ ) + ( ̅ ̅ ) + ( ̅ ̅ )H f Hf H K x x H K x x S K x x 1 2 HHH 1 1 2 1 2 1, 1 2 2, 2 1 1, 2 The threshold ‘d’ is computed after every step, so that the KKT conditions are satisfied. SVM classification is attempted with the features such as standard deviation and entropy. A data set is formed by taking 20 frames for each type of flow pattern. Hence 120  2 matrix is taken as the data set. Here the rows represent the number of frames and the columns represent the features. Fig. 6(a)–(f) re- presents the output for each type of flow pattern using SVM without PCA. It can be seen from the figures that the results are appreciable in the case of slug, stratified and churn flow patterns. But in the case of bubble, plug and annular flow patterns, it can be seen that a clear demarcation is not obtained to separate the de- sired flow patterns from the other types. Hence principal com- ponent analysis is performed to improve the output qualitatively and quantitatively. 7. Pattern classification using SVM with PCA Principal Component Analysis (PCA) is an important technique used in data visualization and analysis. It is used to reduce the dimension of a large set of data. In other words, it is a tool to extract relevant information from complex data sets. Consider the first principal component U1 with maximum variance. Suppose that all centered observations are stacked into the columns of matrix X, ω=U XT 1 where ω ¼ [ ω1, ω2……….ωn] ( )( ) ω ω ω= =U X Svar var T T 1 Where S is the n  n covariance matrix of X. ( )var U1 can be made arbitrarily large by increasing the magnitude of ω. Therefore, choose ω to maximize ωT Sω while constraining ω to have unit length. ω ω ω ω=max S Subject to 1T T To solve this optimization problem a Lagrange multiplier α1 is introduced ω α ω ω α ω ω( ) = − ( − ) ( )L , S 1 2T T 1 Differentiating with respect to ω gives n equations ω α ω=S 1 Table 6 Average value of shape features extracted for the recorded video. Features Flow pattern (pixels) Bubbly flow Plug flow Slug flow Churn flow Annular flow Stratified flow 1. No. of bubbles in the image 15 2 1 10 0 0 2. Maximum size of the bubbles 40 38 92 74 90 60 3. No. of bubbles with size one pixel 3 0 0 1 0 0 4. Sum of areas of the bubbles 200 350 415 400 75 100 5. Radius of the circle that best fits the image 7.9 12 11.7 11.5 4.8 5.6 6. Circularity 0.99 0.77 0.97 0.96 1.04 1.01 7. Compactness 1.00 1.29 1.03 1.03 0.96 0.98 C. Shanthi, N. Pappa / ISA Transactions 68 (2017) 425–432 429
  6. 6. Fig. 6. Flow pattern using SVM without PCA. (f) Stratified flow Fig. 7. Flow pattern using SVM with PCA. C. Shanthi, N. Pappa / ISA Transactions 68 (2017) 425–432430
  7. 7. Multiplying both sides by ω ,T ω ω α ω ω α= −ST T 1 1 Var( )U1 is maximized if α1 is the largest eigenvalue of S. α and1 ω are an eigenvalue and an eigenvector of S. Differentiating Eq. (2) with respect to the Lagrange multiplier α1 gives the con- straint. ω ω = 1T The first principal component is given by the eigenvector with the largest associated eigenvalue of S. Similarly, all the principal components can be determined from the dominant eigenvectors of S. In co-variance method the mean of the data matrix is com- puted and the value is subtracted from each value in the matrix. The co-variance matrix is computed and the eigenvalues and ei- genvectors of this matrix are determined. The feature matrix is constructed and five principal components are obtained. The five principal components are maximum size of bubbles in the image, sum of the areas of the bubbles, standard deviation of histogram features, uniformity and average histogram. The first and second principal components are taken as input to the SVM classifier. A data set is formed using 20 frames for each type of flow pattern. Hence totally 120 frames are considered. The data set used in this case has a dimension of 120 Â 6. Here the rows represent the number of frames and the columns represent the principal components. Different values of first and second principal components are given as input to the SVM. The values are plotted accordingly and the different flow patterns are iden- tified as shown in Fig. 7.(a)–(f). 8. Results and discussion The objective of this paper is to identify six different flow patterns such as bubble flow, slug flow, plug flow, stratified flow, annular flow and churn flow using fuzzy logic and SVM with PCA. The fuzzy logic based classifier, SVM classifier and PCA based SVM classifiers are tested with 120 frames (20 frames of each flow pattern) of six flow patterns. The classification accuracy is listed in Table 7. In fuzzy logic based classification, it is observed that the clas- sification accuracy is more than 80% in the case of bubble, plug and annular flow. From Table 7 it is observed that in the case of slug, stratified and churn flow the results obtained are not satisfactory. It is found that performance of SVM classification is improved when compared to the fuzzy logic classification except the strati- fied flow. In SVM with PCA, it is observed that good results are obtained for all the six types of flow patterns. It is finally observed that SVM with PCA is found suitable for the classification of all six types of flow patterns namely annular, bubble, churn, plug, slug and stratified flow, with less computational complexity. 9. Conclusion In this paper, an optimized gas-liquid flow pattern recognition system using fuzzy logic and SVM with PCA is presented. The vi- deos of various flow patterns are acquired with the help of a digital camera. The image based analysis is carried out to calculate the maximum and minimum object widths. The fuzzy logic system is designed based on the extraction of image features and the system is validated for various flow patterns. Also, flow regime identifi- cation of gas/liquid two-phase flow has been performed using feature extraction and SVM. In addition, a new method for feature selection is introduced. The results are improved by applying PCA in which five principal components are considered out of the 24 features. Hence, PCA reduces the complexity of the system. There is some difficulty in identifying the images due to certain changes in conditions while capturing the image. It is finally observed that SVM with PCA gives much better results compared to the identi- fication based on fuzzy logic and SVM. Hence, the proposed SVM with PCA can be used to optimize the feature selection and for efficient identification of flow patterns for industries with two- phase flows. Appendix Mean ∑ ∑= − = − mn x m x n1 0 1 0 1 (I(x,y) Variance ( )∑ ∑ ( ) −( − ) = − = − I x y mean,mn x m x n1 1 0 1 0 1 2 Standard deviation Variance Skewness ( ) ∑ ∑ ( ( ) − ) σ = − = − I x y Mean, mn X m y n1 0 1 0 1 3 3 Kurtosis ( ) ∑ ∑ ( ( ) − ) σ− = − = − I x y Mean, mn X m y n1 0 1 0 1 4 4 Modified standard deviation ∑ ∑ ( ( ) − ) ( ( ))= − = − I x y Mean P I i j, ,X m y n 0 1 0 1 2 Modified skew ( ) ∑ ∑ ( ( ) − ) ( ( )) σ = − = − I x y Mean P I i j, ,X m y n1 0 1 0 1 3 3 Smoothness − σ+ 1 1 1 2 Uniformity ∑ ( )= − p zi L i0 1 2 Third moment ∑ ( − ) ( )= − p z m p zi L i i0 1 3 Entropy Table 7 Classification Accuracy. Flow patterns Classification Accuracy (rounded to the nearest integer) Fuzzy Logic SVM SVM with PCA Bubbly flow 83 85 98 Plug flow 87 90 99 Slug flow 70 77 95 Churn flow 50 52 90 Annular flow 85 89 99 Stratified flow 66 63 92 C. Shanthi, N. Pappa / ISA Transactions 68 (2017) 425–432 431
  8. 8. ∑ ( ) ( )= − p z zlogi L i i0 1 2 Average histogram ∑ ( )= − N iL i L1 0 1 Circularity ΠA p 4 2 Compactness Π p A4 2 In the above equations m and n denote the number of rows and columns in the image matrix, I(x,y) denotes a particular pixel value in the loaded image, p(Zi) is the histogram of intensity levels in a region, where I ¼ 1,2,3,….,L-1. Here L denotes the number of possible intensity levels. ‘A’ denotes the area of the circle that best fits a cluster of bubbles. References [1] Dinh TB, Choi TS. Application of Image processing techniques in air-water two phase flow. Mech Res Commun 1999;26:463–8. [2] Van Hout R, Barnea D, Shemer L. Evolution of statistical parameters of gas– liquid slug flow along vertical pipes. Int J Multiph Flow 2001;20:460–70. [3] Wilmarth T, Ishiit M. Two-.phase flow regimes in narrow rectangular vertical and horizontal channel. Int J Heat Mass Transf 1994;37:1749–58. [4] Shanthi C, Pappa N, Aswini J. Digital Image Processing Based Flow Regime Identification of Gas/Liquid Two-phase flow. Proc IFAC-DYCOPS 2013;10:409– 14. http://dx.doi.org/10.3182/20131218-3-IN-2045.00170. [5] Hanafizadeh P, Ghanbarzadeh S, Said MH. Visual technique for detection of gas–liquid two-phase flow regime in the airlift pump. J Pet Sci Eng 2011;75:327–35. [6] Hewitt GF, Roberts DN. , Studies of two-phase flow patterns by simultaneous – ray and flash photography, UKAEA Report AERE-M2159; 1969. [7] G. Huang G, Ji H, Huang Z, Wang B, Li H. Flow regime identification of mini pipe gas-liquid two phase flow based on textural feature series. In: Proceed- ings of IEEE instrumentation and measurement technology conference 06; 2011, doi: http://dx.doi.org/10.1109/IMTC.2011.5944345. [8] Mayor TS, Pinto AMFR, Campos JBLM. An image analysis technique for the study of gas–liquid slug flow along vertical pipes — associated uncertainty. Flow Meas Instrum 2007;18:139–47. [9] Bin S, Hong W. Identification method of gas-liquid two phase flow regime based on distance evaluation method. In: Proceedings of the sixth IEEE in- ternational conference on natural computation 3; 2010. p. 1266–70. [10] Shi L, Fuzzy recognition for gas-liquid two-phase flow pattern based on image processing. In: Proceedings of the 13rd IEEE international conference on control and automation; 2007. p. 1424–27. [11] Nogueira S, Riethmuller ML, Campos JBLM, Pinto AMFR. Flow patterns in the wake of a Taylor bubble rising through vertical columns of stagnant and flowing Newtonian liquids: an experimental study. Chem Eng Sci 2007;61:7199–212. [12] Mi Y, Ishii M, Tsoukalas LH. Flow regime identification methodology with neural networks and two-phase flow models. Nucl Eng Des 2001;204:87–100. [13] Embrechts MJ, Lahey RT, Yapo T. The application of neural network two phase flow regime recognition. In: Proceedings of the American pour conference; 1996. p. 860–64. [14] Monji H, Matsui G. Flow pattern recognition of gas-liquid two-phase flow using a neural network.In: Proceedings of third international conference on multiphase flow; 1998. p. 409–20. [15] Mi Y, Ishii M, Tsoukalas LH. Vertical Two-phase flow recognition using ad- vanced neural network. Nucl Eng Des 1998;454(1):409–20. [16] Soheil G, Pedram H, Saidi MH. Intelligent Image-Based Gas-Liquid two-phase flow regime Identification. ASME J Fluids Eng 2012;134:061302–10. [17] Shi L, Cai J, Zekui Z. Gas, Liquid two -phase flow pattern identification based on Image processing. J Zhejiang Univ 2005;39:1128–31. [18] Dong F, Liu XP, Deng X, Xu LJ, Xu LA. Identification of two-phase flow regimes in horizontal, inclined and vertical pipes. Meas Sci Technol 2001;12:1069–75. [19] Dong F, Xu YB, Hua L, Wang HX. Two methods for measurement of gas-liquid flows in vertical upward pipe using dual-plane ERT system. IEEE Trans Instrum Meas 2006;55:1576–86. [20] Yen G, Haiming Lu. Acoustic emission data assisted process monitoring. ISA Trans 2002;41:273–82. [21] Mandhane JM, Gregory CA. A flow pattern map for gas-liquid flow in hor- izontal pipes. Int J Multiph Flow 1974;1(4):537–54. [22] Platt JC. Training of support vector machines using sequential minimal opti- mization. advances in kernel methods: support vector learning.Cambridge: MIT Press; 1999. C. Shanthi, N. Pappa / ISA Transactions 68 (2017) 425–432432

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