The Identification of Gas-liquid Co-current Two Phase Flow Pattern in a Horizontal Pipe Using the Power Spectral Density and the Artificial Neural Network (ANN)

This paper presents a new method of the flow pattern identification on the basis of the analysis of Power Spectral Density (PSD) from the pressure difference data of horizontal flow. Seven parameters of PSD curve such as mean (K1), variance (K2), mean at 1-3 Hz (K3), mean at 3-8 Hz (K4), mean at 8-13 Hz (K5), mean at 13-25 Hz (K6) and mean at 25-30 Hz (K7) were used as training vector input of Artificial Neural Networks (ANN) in order to identify the flow patterns. From the obtained experimental of 123 operating conditions consisting of stratified flow, plug and slug, ANN was trained by using 100 data operation and 23 tested data. The results showed that the new method has a capability to identify the flow patterns of gas-liquid two phase flow with a high accuracy

Non-invasive classification of gas–liquid two-phase horizontal flow regimes using an ultrasonic Doppler sensor and a neural network

The identification of flow pattern is a key issue in multiphase flow which is encountered in the petrochemical industry. It is difficult to identify the gas–liquid flow regimes objectively with the gas–liquid two-phase flow. This paper presents the feasibility of a clamp-on instrument for an objective flow regime classification of two-phase flow using an ultrasonic Doppler sensor and an artificial neural network, which records and processes the ultrasonic signals reflected from the two-phase flow. Experimental data is obtained on a horizontal test rig with a total pipe length of 21 m and 5.08 cm internal diameter carrying air-water two-phase flow under slug, elongated bubble, stratified-wavy and, stratified flow regimes. Multilayer perceptron neural networks (MLPNNs) are used to develop the classification model. The classifier requires features as an input which is representative of the signals. Ultrasound signal features are extracted by applying both power spectral density (PSD) and discrete wavelet transform (DWT) methods to the flow signals. A classification scheme of '1-of-C coding method for classification' was adopted to classify features extracted into one of four flow regime categories. To improve the performance of the flow regime classifier network, a second level neural network was incorporated by using the output of a first level networks feature as an input feature. The addition of the two network models provided a combined neural network model which has achieved a higher accuracy than single neural network models. Classification accuracies are evaluated in the form of both the PSD and DWT features. The success rates of the two models are: (1) using PSD features, the classifier missed 3 datasets out of 24 test datasets of the classification and scored 87.5% accuracy; (2) with the DWT features, the network misclassified only one data point and it was able to classify the flow patterns up to 95.8% accuracy. This approach has demonstrated the success of a clamp-on ultrasound sensor for flow regime classification that would be possible in industry practice. It is considerably more promising than other techniques as it uses a non-invasive and non-radioactive sensor

Pressure Drop in Vertical Multiphase Flow using Neuro Fuzzy Technique; A Comparative Approach

The sole objective of this study is to develop a model for estimating the pressure drop in vertical multiphase flow using one of the artificial intelligence techniques which is Neuro Fuzzy Systems with a good and acceptable accuracy that can work for a wide range of well flowing conditions that can replace the rigorous empirical and mechanistic correlations. In this study a number of 206 data sets collected from some fields in the Middle East were used to develop the Neuro Fuzzy Model. Many attempts have been done to estimate the pressure drop in vertical multiphase flow starting from the homogeneous models, the empirical models and the mechanistic models. But yet, none of the traditional correlations works well for the variety of well conditions that are found in the oil industry. Thus, the accuracy of the old pressure drop correlations cannot be raised to a generally accepted level. For this purpose, one of the artificial intelligence techniques (Neuro Fuzzy System) is used to have a significant reduction in the error involved with estimating the pressure drop. The Neuro Fuzzy Model was developed through 3 stages; Training, Validation, Testing. The developed Neuro Fuzzy Model has successfully achieved the lowest Average Absolute Percentage Error (AAPE%) of 2.92% that could overcome all the empirical and mechanistic correlations when tested against the same set of data. It can be concluded that Neuro Fuzzy system has overcame the performance of the models currently used in the industry

On the use of area-averaged void fraction and local bubble chord length entropies as two- phase flow regime indicators

In this work, the use of the area-averaged void fraction and bubble chord length entropies is introduced as flow regime indicators in two-phase flow systems. The entropy provides quantitative information about the disorder in the area-averaged void fraction or bubble chord length distributions. The CPDF (cumulative probability distribution function) of void fractions and bubble chord lengths obtained by means of impedance meters and conductivity probes are used to calculate both entropies. Entropy values for 242 flow conditions in upward two-phase flows in 25.4 mm and 50.8 mm pipes have been calculated. The measured conditions cover ranges from 0.13 m/s to 5 m/s in the superficial liquid velocity jf and ranges from 0.01 m/s to 25 m/s in the superficial gas velocity jg. The physical meaning of both entropies has been interpreted using the visual flow regime map information. The area-averaged void fraction and bubble chord length entropies capability as flow regime indicators have been checked with other statistical parameters and also with different input signals durations. The area-averaged void fraction and the bubble chord length entropies provide better or at least similar results than those obtained with other indicators that include more than one parameter. The entropy is capable to reduce the relevant information of the flow regimes in only one significant and useful parameter. In addition, the entropy computation time is shorter than the majority of the other indicators. The use of one parameter as input also represents faster predictions

On the application of self-organizing neural networks in gas-liquid and gas-solid flow regime identification

One of the main problems associated with the transport and manipulation of multiphase flow is the existence of flow regimes, which have a strong influence on important parameters of operation. An example of this occurs in gas-liquid chemical reactors in which maximum coefficients of reaction can be attained by keeping a dispersed-bubbly flow regime to maximize the total interfacial area. Another example is the pneumatic conveying of solids in which the regimes are associated with safety and energy consumption. Thus, the ability to identify flow regimes automatically is very important, specially to maintain multiphase systems operating according to design conditions. This work assesses the use of a self-organizing map (neural network) adapted to the problem of regime identification in horizontal two-phase flows. In order to achieve extensive results, two different types of two-phase flows were considered: gas-solid and gas-liquid. Tests were made to verify the performance of the neural network model, using data collected at the experimental facilities of the Thermal and Fluid Engineering Laboratory of the University of São Paulo at São Carlos. Results show that the neural network is capable of correctly identifying the regimes. The error percentage is bigger when analyzing the same regime with flow rates different from the one used as training data emphasizing the importance of training signals choice

Development of a real-time objective gas-liquid flow regime identifier using kernel methods

Currently, flow regime identification for closed channels have mainly been direct subjective methods. This presents a challenge when dealing with opaque test sections of the pipe or at gas-liquid flow rates where unclear regime transitions occur. In this paper, we develop a novel real-time objective flow regime identification tool using conductance data and kernel methods. Our experiments involve a flush mounted conductance probe that collects voltage signals across a closed channel. The channel geometry is a horizontal annulus, which is commonly found in many industries. Eight distinct flow regimes were observed at selected gas-liquid flow rate settings. An objective flow regime identifier was then trained by learning a mapping between the probability density function (PDF) of the voltage signals and the observed flow regimes via kernel principal components analysis (KPCA) and multi-class Support Vector Machine (SVM). The objective identifier was then applied in real-time by processing a moving time-window of voltage signals. Our approach has: (a) achieved more than 90% accuracy against visual observations by an expert for static test data; (b) successfully visualized conductance data in 2-dimensional space using virtual flow regime maps, which are useful for tracking flow regime transitions; and, (c) introduced an efficient real-time automatic flow regime identifier, with only conductance data as input

Experimental investigations of two-phase flow measurement using ultrasonic sensors

This thesis presents the investigations conducted in the use of ultrasonic technology to measure two-phase flow in both horizontal and vertical pipe flows which is important for the petroleum industry. However, there are still key challenges to measure parameters of the multiphase flow accurately. Four methods of ultrasonic technologies were explored. The Hilbert-Huang transform (HHT) was first applied to the ultrasound signals of air-water flow on horizontal flow for measurement of the parameters of the two- phase slug flow. The use of the HHT technique is sensitive enough to detect the hydrodynamics of the slug flow. The results of the experiments are compared with correlations in the literature and are in good agreement. Next, experimental data of air-water two-phase flow under slug, elongated bubble, stratified-wavy and stratified flow regimes were used to develop an objective flow regime classification of two-phase flow using the ultrasonic Doppler sensor and artificial neural network (ANN). The classifications using the power spectral density (PSD) and discrete wavelet transform (DWT) features have accuracies of 87% and 95.6% respectively. This is considerably more promising as it uses non-invasive and non-radioactive sensors. Moreover, ultrasonic pulse wave transducers with centre frequencies of 1MHz and 7.5MHz were used to measure two-phase flow both in horizontal and vertical flow pipes. The liquid level measurement was compared with the conductivity probes technique and agreed qualitatively. However, in the vertical with a gas volume fraction (GVF) higher than 20%, the ultrasound signals were attenuated. Furthermore, gas-liquid and oil-water two-phase flow rates in a vertical upward flow were measured using a combination of an ultrasound Doppler sensor and gamma densitometer. The results showed that the flow gas and liquid flow rates measured are within ±10% for low void fraction tests, water-cut measurements are within ±10%, densities within ±5%, and void fractions within ±10%. These findings are good results for a relatively fast flowing multiphase flow