Gas-liquid Two-phase Flow Measurement Using Coriolis Flowmeters Incorporating Artificial Neural Network, Support Vector Machine and Genetic Programming Algorithms

Coriolis flowmeters are well established for the mass flow measurement of single phase flow with high accuracy. In recent years attempts have been made to apply Coriolis flowmeters to measure two-phase flow. This paper presents data driven models that are incorporated in Coriolis flowmeters to measure both the liquid mass flowrate and the gas volume fraction of a two-phase flow mixture. Experimental work was conducted on a purpose-built two-phase flow test rig on both horizontal and vertical pipelines for a liquid mass flowrate ranging from 700 kg/h to 14500 kg/h and a gas volume fraction between 0 and 30%. Artificial Neural Network (ANN), Support Vector Machine (SVM) and Genetic Programming (GP) models are established through training with experimental data. The performance of BP-ANN (Back Propagation - ANN), RBF-ANN (Radial Basis Function - ANN), SVM and GP models is assessed and compared. Experimental results suggest that the SVM models are superior to the BP-ANN, RBF-ANN and GP models for two-phase flow measurement in terms of robustness and accuracy. For liquid mass flowrate measurement with the SVM models, 93.49% of the experimental data yield a relative error less than ±1% on the horizontal pipeline whilst 96.17% of the results are within ±1% on the vertical installation. The SVM models predict gas volume fraction with a relative error less than ±10% for 93.10% and 94.25% of the test conditions on horizontal and vertical installations, respectively

Application of Soft Computing Techniques to Multiphase Flow Measurement: A Review

After extensive research and development over the past three decades, a range of techniques have been proposed and developed for online continuous measurement of multiphase flow. In recent years, with the rapid development of computer hardware and machine learning, soft computing techniques have been applied in many engineering disciplines, including indirect measurement of multiphase flow. This paper presents a comprehensive review of the soft computing techniques for multiphase flow metering with a particular focus on the measurement of individual phase flowrates and phase fractions. The paper describes the sensors used and the working principle, modelling and example applications of various soft computing techniques in addition to their merits and limitations. Trends and future developments of soft computing techniques in the field of multiphase flow measurement are also discussed

Dynamic measurement of gas volume fraction in a CO2 pipeline through capacitive sensing and data driven modelling

Gas volume fraction (GVF) measurement of gas-liquid two-phase CO2 flow is essential in the deployment of carbon capture and storage (CCS) technology. This paper presents a new method to measure the GVF of two-phase CO2 flow using a 12-electrode capacitive sensor. Three data driven models, based on back-propagation neural network (BPNN), radial basis function neural network (RBFNN) and least-squares support vector machine (LS-SVM), respectively, are established using the capacitance data. In the data pre-processing stage, copula functions are applied to select feature variables and generate training datasets for the data driven models. Experiments were conducted on a CO2 gas-liquid two-phase flow rig under steady-state flow conditions with the mass flowrate of liquid CO2 ranging from 200 kg/h to 3100 kg/h and the GVF from 0% to 84%. Due to the flexible operations of the power generation utility with CCS capabilities, dynamic experiments with rapid changes in the GVF were also carried out on the test rig to evaluate the real-time performance of the data driven models. Measurement results under steady-state flow conditions demonstrate that the RBFNN yields relative errors within ±7% and outperforms the other two models. The results under dynamic flow conditions illustrate that the RBFNN can follow the rapid changes in the GVF with an error within ±16%

Investigations into the behaviours of Coriolis flowmeters under air-water two-phase flow conditions on an optimized experimental platform

Gas-liquid two-phase flow is commonly encountered in many industrial processes due to production requirement or inevitable gas entrainment from various sources. Accurate liquid phase measurement under two-phase conditions is challenging but important as it is the key factor to reduce cost, improve safety or meet legal requirements. Coriolis flowmeters, owing to their high accuracy in metering single-phase flow, direct mass flow measurement and multivariable sensing nature, are widely used in industry. Recently developed Coriolis flowmeters can work under multiphase conditions, making it possible to achieve accurate multiphase flow measurement through model based error compensation or training based soft computing correction. This paper assesses the behaviours of Coriolis flowmeters under various two-phase conditions for modelling and soft computing algorithm improvement, including previously investigated factors (flowrate, gas volume fraction, flow tube geometry, flow converter, and process pressure) and new factors (flow regimes in terms of bubble size and distribution). Experimental work was conducted on 25 mm and 50 mm bore air-water two-phase flow rigs for liquid mass flowrates between 2500 kg/h and 35000 kg/h with gas volume fraction of 0-60%. With the influence of each factor identified through univariate analysis, comparisons between existing modelling theories and experimental error curves are established. In the meantime, the rig design and control are optimized to provide efficient and automated data acquisition in order to supply ample and high-quality data for the training of soft computing models as well as enhancing the understanding in theoretical modelling

Modelling oil and gas flow rate through chokes: A critical review of extant models

Oil and gas metering is primarily used as the basis for evaluating the economic viability of oil wells. Owing to the economic implications of oil and gas metering, the subject of oil and gas flow rate measurement has witnessed a sustained interest by the oil and gas community and the academia. To the best of the authors’ knowledge, despite the growing number of published articles on this subject, there is yet no comprehensive critical review on it. The objective of this paper is to provide a broad overview of models and modelling techniques applied to the estimation of oil and gas flow rate through chokes while also critically evaluating them. For the sake of simplicity and ease of reference, the outcomes of the review are presented in tables in an integrated and concise manner. The articles for this review were extracted from many subject areas. For the theoretical pieces related to oil and gas flow rate in general, the authors relied heavily upon several key drilling fluid texts. For operational and field studies, the authors relied on conference proceedings from the society of petroleum engineers. These sources were supplemented with articles in peer reviewed journals in order to contextualize the subject in terms of current practices. This review is interspersed with critiques of the models while the areas requiring improvement were also outlined. Findings from the bibliometric analysis indicate that there is no universal model for all flow situations despite the huge efforts in this direction. Furthermore, a broad survey of literature on recent flow models reveals that researchers are gravitating towards the field of artificial intelligence due to the tremendous promises it offers. This review constitutes the first critical compilation on a broad range of models applied to predicting oil and gas flow rates through chokes

Mass Flow Rate Measurement of Pneumatically Conveyed Solids Through Multimodal Sensing and Data-Driven Modeling

Online continuous measurement of mass flow rate of pneumatically conveyed solids is desirable in the monitoring and optimization of a range of industrial processes such as food processing, chemical engineering and power generation. This paper introduces a technique for the mass flow rate measurement of pneumatically conveyed solids based on multi-modal sensing and data driven modelling. The multi-modal sensing system is comprised of an array of ring-shaped electrostatic sensors, four arrays of arc-shaped electrostatic sensors and a differential-pressure transducer. Data driven models, including artificial neural network (ANN), support vector machine (SVM), and convolutional neural network (CNN), are established through training with statistical features extracted from the post-processed data from the sensing system. Statistical features are shortlisted based on their importance by calculating the partial mutual information between the features and the corresponding reference mass flow rate of solids. Experimental work was conducted on a laboratory-scale rig to train and test the models on both horizontal and vertical pipelines with particle velocity ranging from 10.1 m/s to 36.0 m/s and mass flow rate of solids from 3.2 g/s to 35.8 g/s. Experimental results suggest that the ANN, SVM and CNN models predict the mass flow rate of solids with a relative error within ±18%, ±14% and ±8%, respectively, under all test conditions. The predicted mass flow rate measurements with the ANN, SVM and CNN models are repeatable with a normalized standard deviation within 14%, 8% and 5%, respectively, under all test conditions

Measurement of cross-sectional velocity distribution of pneumatically conveyed particles in a square-shaped pipe through electrostatic sensing and Gaussian process regression

Online continuous measurement of the cross-sectional velocity distribution of pneumatically conveyed solids in a square-shaped pipe is desirable in monitoring and optimizing circulating fluidized beds, coal-fired power plants and exhaust pipes. Due to the limitation of non-invasive electrostatic sensors in spatial sensitivity, it is difficult to accurately measure the velocity of particles in large diameter pipes. In this paper, a novel approach is presented for the measurement of cross-sectional particle velocity distribution in a square-shaped pipe using sensors and Gaussian process regression (GPR). The electrostatic sensor includes twelve pairs of strip-shaped electrodes. Experimental tests were conducted on a laboratory test rig to measure the cross-sectional particle velocities in a vertical square pipe under various experimental conditions. The GPR model is developed to infer the relationship between the input variables of velocities and the cross-sectional velocity distribution of particles. Results obtained suggest that the electrostatic sensor in conjunction with the GPR model is a feasible approach to obtain the cross-sectional velocity distribution of pneumatically conveyed particles in a square-shaped pipe

Análisis experimental de flujo líquido-líquido en un tubo horizontal usando redes neuronales artificiales

El objetivo de este trabajo es la aplicación de una red neuronal artificial para la predicción de la fracción volumétrica (holdup) de flujo bifásico (aceite-agua) en un tubo en posición horizontal. Para este fin, la velocidad superficial del agua y el aceite se utilizaron como parámetros de entrada, entre tanto, la fracción volumétrica de estos dos fluidos se utilizaron como parámetros de salida para el entrenamiento y prueba de la red neuronal multicapa, el método utilizado fue retro propagación. Los datos experimentales (92 datos) se tomaron en el LEMI-EESC-USP y fueron utilizados para desarrollar el modelo de red neuronal artificial. Finalmente, se concluyó que los datos experimentales utilizados en la red neuronal se ajustan muy bien para una función de transferencia tagsig con 10 neuronas en la capa oculta evaluadas a partir del error porcentual absoluto medio de (AAPE= 3,95) y coeficiente de determinación ( = 0,975).The objective of this work was the application of an artificial neural network in prediction of holdup of two-phase flow (oil-water) in a pipe in horizontal position. To this end, the velocity superficial of water and oil were used as input parameters, meanwhile, the holdups of these two fluids were used as output parameters for the training and testing of the multilayer neural network, the method used was back-propagation. The experimental data (92 data) were taken at LEMI-EESC-USP and were used to develop the artificial neural network model. Finally, it was concluded that the experimental data used in the neural network agreed with the tagsig transfer function with 10 neurons in the hidden layer evaluated from the absolute percentage error of (AAPE= 3,95) and coefficient of determination ( = 0,975).

Mass Flow Rate Measurement of Solids in a Pneumatic Conveying Pipeline in Different Orientations

Extensive work has been undertaken for the mass flow rate measurement of solids in a horizontal or vertical pneumatic conveying pipe. However, flow regime of the two-phase flow is highly influenced by different orientations of the pipe, resulting in different characteristics of sensor signals and hence large errors in mass flow rate measurement using conventional methods. This paper presents a novel technique to measure the mass flow rate of pneumatically conveyed particles in different pipe orientations. A range of low-cost sensors, including an array of electrostatic sensors, a differential-pressure transducer, and an accelerometer, are integrated to form a sensing unit. Data-driven models, based on support vector machine (SVM), are developed to take the selected features from post-processed sensor data and infer the mass flow rate of solids in different pipe orientations. The partial mutual information algorithm is applied to quantify the importance of each feature. The firefly algorithm is used to optimize the selection of useful features and tune the learning parameters in SVM models. Experimental tests were conducted on a pneumatic conveying test rig circulating flour over the mass flow rate of solids from 3.2 g/s to 35.8 g/s in pipe orientations from 0° to 90°. Performance comparisons are made between the conventional SVM model and the optimised SVM models with the training data from horizontal orientation and different orientations, respectively. Results demonstrate that the relative error and repeatability of the measured mass flow rate of solids with the optimized SVM model are both improved to within ±12%