Flow Rate Estimation Research Articles

Passive Probe: Mechanically-Modulated Field Sensing for Motion Tracking and Flow Estimation

The internal mechanical motions of a dynamic system can serve as the basis for developing “free” sensors that probe operating state and potentially diagnostic health. Specifically, the interaction of a moving mechanical structure with an applied, nonintrusive field, e.g., an electric or magnetic field, can produce a signal that creates a sensor with little additional hardware. In this paper, bellows-and-diaphragm natural gas (BDNG) meters provide an illustrative example for three different approaches for transforming a metering mechanism and its consumption totalizer into a high quality flow meter. Detailed flow information provides a data-stream for nonintrusively monitoring the real-time operation of loads – in this example case, loads that consume natural gas such as burners, heaters, and engines. The flow information can also be used for fault detection and diagnostics, e.g., for finding leaks or correlating faulted operation with respect to the operation of electrical actuators or other flows of consumables like water. This paper examines three BDNG meters and presents a methodology for inexpensively adding digital flow-rate measurement capabilities to each meter. The methods developed here can in principle be applied to any dynamic machine for consumption estimation or fault detection. They can also be used with other nonintrusive field stimuli including light and sound. Mechanical retrofit techniques are described, and exemplary signal processing and estimation chains including compensation, flow rate estimation, and post processing are presented.

Intelligent Production Monitoring with Continuous Deep Learning Models

Summary Monitoring of production rates is essential for reservoir management, history matching, and production optimization. Traditionally, such information is provided by multiphase flowmeters or test separators. The growth of the availability of data, combined with the rapid development of computational resources, enabled the inception of digital techniques, which estimate oil, gas, and water rates indirectly. This paper discusses the application of continuous deep learning models, capable of reproducing multiphase flow dynamics for production monitoring purposes. This technique combines the time evolution properties of a dynamical system and the ability of neural networks to quantitively describe poorly understood multiphase phenomena and can be considered as a hybrid solution between data-driven and mechanistic approaches. The continuous latent ordinary differential equation (Latent ODE) approach is compared with other known machine learning methods, such as linear regression, ensemble-based model, and recurrent neural network (RNN). In addition, the framework of virtual flowmetering (VFM) is analyzed from the point of multiphase measurement technology. In this work, the application of Latent ODEs for the problem of multiphase flow rate estimation is introduced. The considered example refers to a scenario in which the topside oil, gas, and water flow rates are estimated using the data from several downhole pressure sensors. The predictive capabilities of different types of machine learning and deep learning instruments are explored using simulated production data from a multiphase flow simulator. The results demonstrate the satisfactory performance of the continuous deep learning models in comparison with other machine learning methods in terms of accuracy, where the normalized root-mean-squared error (RMSE) and mean absolute error (MAE) of prediction below 5% were achieved. While Latent ODE demonstrates the significant time required to train the model, it outperforms other methods for irregularly sampled time series, which makes it especially attractive to forecast values of multiphase rates.

Machine Learning Approaches to Estimate Flow Rate of Drip Irrigation Emitter Based on Structure Parameters and Pressure

The agricultural water-saving irrigation systems need to be adapted to local conditions, and irrigation emitters with different flow rates need to be designed to meet actual needs. In this study, machine learning approaches were used to predict the flow rate of the labyrinth emitters. The structural parameters of the emitter [number of channel units (N), channel unit length (L), channel width (W), tooth height (H) and channel depth (D)] and working pressure (P) were considered as independent variables. The applied machine learning models included k-nearest neighbor (KNN), multi-layer perceptron (MLP), support vector machine (SVM) and radial basis function artificial neural network (RBF-ANN). The accuracy of the machine learning model was evaluated by the mean square error (MSE) and coefficient of determination (R 2). The results show that the MSE and R2 of the RBF-ANN model were higher than the corresponding parameters of the other three models, indicating the RBF-ANN model can predict the flow rate of the labyrinth emitter more accurately. The research provides a reference for the rapid design of emitters with different flow rate. It is helpful to realize the automation of agricultural irrigation, and then realize the construction of a smart agricultural irrigation system.

Virtual Multiphase Flow Meter using combination of Ensemble Learning and first principle physics based

Abstract This paper describes a Virtual Flow Meter (VFM) to estimate oil, gas and water flow rate by combining two distinct approaches i.e., data-driven Ensemble Learning algorithm and first principle physics-based transient multiphase flow simulator. The VFM uses a common real-time sensor readings and the estimated flow rates were then combined using a new combiner approach which provides confidence decay and historical performance factors to assign confidence and contribution weights to the base estimators, and then aggregates their estimates to deliver more accurate flow rate estimates. This technique was tested for over 6 months at an offshore oil facility having two oil wells. The technique successfully delivered a 50% improvement in measurement performance compared to stand-alone VFMs. This combiner technique will be of great benefit to surveillance engineers by providing additional real-time production monitoring in addition to acting as a verification tool for physical multiphase flow meters (MPFMs).

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.

A Systematic Review of Drone Based Road Traffic Monitoring System

Drone deployment has become crucial in a variety of applications, including solutions to traffic issues in metropolitan areas and highways. On the other hand, data collected via drones suffers from several problems, including a wide range of object scales, angle variations, truncation, and occlusion. To process and manipulate visual data from the drones, a variety of image processing algorithms have been employed, each with a distinct aim. Additionally, recent breakthroughs in the field of Artificial Intelligence, particularly deep learning, have attracted broad interest and are being applied to many domains in the framework of smart cities, including road traffic monitoring. The purpose of this study is to conduct a systematic review of drone-based traffic monitoring systems from a deep learning perspective. This work focuses on vehicle detection, tracking, and counting, since they are fundamental building blocks towards founding solutions for traffic congestion, flow rate and vehicle speed estimation. Additionally, drone-based datasets are examined, which face issues and problems caused by the diversity of features inherent of drone devices. The review analysis presented in this work summarizes the literature solutions provided and deployed so far and discusses future research trends in establishing a comprehensive traffic monitoring system in support of the development of smart cities.

Radial Passive Stability Analysis of Magnetically Levitated Impeller Position for the Development of Flow Rate Estimations of a Ventricular Assist Device

Radial Passive Stability Analysis of Magnetically Levitated Impeller Position for the Development of Flow Rate Estimations of a Ventricular Assist Device

Optical Sensor System for Chemical Flow Rate Monitoring with Direct Nozzle Injection

HighlightsA novel optical sensor system was developed to accurately measure simulated chemical (water with dye) flow rates observed with direct nozzle injection systems for agricultural sprayers.The average error in chemical flow rate estimates was only 2% across a typical range of injection rates.The prototype optical sensor system was capable of detecting dye dilutions to a level of 1:150,000.Abstract. Direct injection systems have the potential to provide several benefits to spray applicators (e.g., easing boom cleanout procedures); however, lag time and proper mixing continue to hinder adoption. Development efforts have typically focused on moving the chemical injection point closer to the nozzle to improve the application rate response. This poses an important challenge for proper metering of the chemical, i.e., accurate measurement of extremely low chemical flow rates. In this study, an optical sensor system was integrated with a typical carrier flowmeter and calibration methodology to measure chemical flow rates for a direct nozzle injection sprayer. The optical sensor used a light-emitting diode (LED) and photodiode pair to measure the absorbance of a dye mixed with a simulated chemical solution. Laboratory tests were conducted to evaluate the measurement accuracy of the system in which sensor output was compared directly to measurements from a spectrophotometer. Results indicated that the calibrated sensor system was able to estimate the simulated chemical flow rate with an average error of 2%. The sensor system was also capable of accurate measurements as the initial concentrations of dye mixed with the chemical solution varied. Keywords: Application equipment, Pesticides, Sprayers, Spraying equipment.

Variable Exposure Portable Perfusion Monitor Using Commercial Vision Processing System-on-Modules (SOMs)

Laser speckle imaging (LSI) has been widely used to image blood flow in biomedical/clinical research. Commercially available perfusion monitoring systems are bulky, expensive and lack portability. Portable devices that perform LSI using custom Digital Signal Processors (DSPs), and smartphones have been proposed to perform single exposure LSI to monitor blood flow. While these can perform LSI, it is difficult to estimate flow rates using single exposure LSI. In this study, we use NVIDIA’s Xavier NX embedded vision processing platform to perform variable exposure LSI to evaluate flow rates in a 3D printed flow phantom. A low-cost Raspberry Pi HQ camera was used to acquire LSI images and the increased sensitivity of the camera allowed images to be captured with exposure times < 1ms. The higher performance of the Xavier NX platform allowed for video-rate LSI imaging (25 ± 2 FPS for spatial and 5 ± 2 FPS for temporal variance images respectively) of the phantom and using variable exposure datasets, slow (2mm/s-6mm/s), medium (6mm/s-12mm/s), and fast (>12mm/s) flow rates were distinguished. Such a platform can extend the use of portable LSI devices in peri-operative, post-operative and other point-of-care applications as this platform can be commercialized into a tablet-like form factor for convenient portable operation.

КОМПОНОВОЧНО-КОНСТРУКТИВНОЕ РЕШЕНИЕ КОЧЕТОВСКОГО РЫБОХОДНО-НЕРЕСТОВОГО КАНАЛА, ПРОТРАССИРОВАННОГО ПО РУСЛУ Р. БАРСОВКИ

Purpose: development of a layout and design solution for the Kochetovsky fish passage and spawning channel, traced along the Barsovka riverbed. Materials and methods. The basis for the channel layout and design solution development was the data of hydrological studies and plans for topographic survey of the area, recommendations for fish breeding and biological substantiation of the channel, materials for the development of projects and the use of fish passage and spawning channels as part of the Nizhne-Don waterworks. The generally accepted methods were used at collecting and processing the initial information, and the approaches and methods for the search design of waterworks were used when developing structures. Results and discussion. Taking into account the natural conditions of the floodplain section of the Don river near waterworks and the presence of a specially protected area here, the option of tracing the main part of the canal tract along the Barsovka riverbed, which was the Don river watercourse before the construction of the Kochetovsky waterworks was considered. The conditions for channel creation and operation are characterized by the magnitude of the calculated difference in water levels and are determined by the need to create conditions for the anadromous, semi-anadromous and other aquatic fish movement from the downstream of the hydroelectric complex to the upstream and (or) their spawning in the channel tract. As a result of research and development, it was proposed to construct a channel with a length of 8540 m, a trapezoidal cross section, with an estimated water flow depth of 2.6 m, a bottom width of 30 m and a 1:3 slope. With an average slope of the channel bottom of 0.000338 with a stone-gravel-pebble coating of its channel and an estimated flow rate of 90 cubic m/s, the average velocities in the channel tract are 0.95 ± 0.5 m/s. The channel includes an inlet and outlet heads, a tract with fish recreation areas and a spawning substrate for litho- and phytophilic fish. Conclusion. A layout and design solution for a fish passage and spawning channel which is proposed for arrangement at the Kochetovsky waterworks on the Don river has been developed.

Recurrent Neural Network based Soft Sensor for flow estimation in Liquid Rocket Engine Injector calibration

Injector is the critical element in the Liquid Rocket Engine (LRE), to ensure proper mixing of propellants (fuel and oxidizer) in the thrust chamber for achieving the optimum thrust. LRE injector is calibrated in order to deliver required flow rates of propellants by sizing the orifices through simple injector water calibration (IWC) techniques. In LRE-IWC process, a huge 6” turbine flow meter (TFM) is employed for the flow-rate measurement. In order to achieve and maintain the required accuracy and precision in the LRE-IWC process, periodical calibration of TFM is mandatory. It involves tremendous time, cost and human effort. Soft sensors can provide an economical and effective alternative solution for TFM flow-rate measurement. The objective of the proposed work is to develop and implement a recurrent neural network based soft sensor (RNN-SS) for TFM flow-rate measurement. In the LRE-IWC process, experimental flow trials were carried out for different flow patterns, and the necessary measurement data were generated for the soft sensor design. The designed RNN-SS was trained by tuning various hyper parameters to replace the TFM, using three related measurement parameters acquired during the experimental trials. The precise TFM flow-rate estimation was achieved by the designed RNN-SS, with a worst-case mean absolute percentage error (MAPE) of 1.91% for the experimental flow patterns considered, with good repeat-ability. The proposed RNN-SS model for TFM flow-rate estimation gives a MAPE of 0.58%, for the required flow-pattern which is well suited for practical use.

Estimation of Respiratory Nasal Pressure and Flow Rate Signals Using Different Respiratory Sound Features

BackgroundRespiratory sounds are associated with the flow rate, nasal flow pressure, and physical characteristics of airways. In this study, we aimed to develop the flow rate and nasal flow pressure estimation models for the clinical application, and find out the optimal feature set for estimation to achieve the optimal model performance. MethodsRespiratory sounds and flow rate were acquired from nine healthy volunteers. Respiratory sounds and nasal flow pressure were acquired from twenty-three healthy volunteers. Four types of respiratory sound features were extracted for flow rate and nasal flow pressure estimation using different estimation models. Effects of estimations using these features were evaluated using Bland-Altman analysis, estimation error, and respiratory sound feature calculation time. Besides, expiratory and inspiratory phases divided estimation errors were compared with united estimation errors. ResultsThe personalized logarithm model was selected as the optimal flow rate estimation model. Respiratory nasal flow pressure estimation based on this model was also performed. For the four different respiratory sound features, there is no statistically significant difference in flow rate and pressure estimation errors. LogEnvelope was, therefore, chosen as the optimal feature because of the lowest computational cost. In addition, for any type of respiratory sound feature, no statistically significant difference was observed between divided and united estimation errors (flow rate and pressure). ConclusionRespiratory flow rate and nasal flow pressure can be estimated accurately using respiratory sound features. Expiratory and inspiratory phases united estimation using respiratory sounds is a more reasonable estimation method than divided estimation. LogEnvelope can be used for this united respiratory flow rate and nasal flow pressure estimation with minimum computational cost and acceptable estimation error.

Joint Use of in-Scene Background Radiance Estimation and Optimal Estimation Methods for Quantifying Methane Emissions Using PRISMA Hyperspectral Satellite Data: Application to the Korpezhe Industrial Site

Methane (CH4) is one of the most contributing anthropogenic greenhouse gases (GHGs) in terms of global warming. Industry is one of the largest anthropogenic sources of methane, which are currently only roughly estimated. New satellite hyperspectral imagers, such as PRISMA, open up daily temporal monitoring of industrial methane sources at a spatial resolution of 30 m. Here, we developed the Characterization of Effluents Leakages in Industrial Environment (CELINE) code to inverse images of the Korpezhe industrial site. In this code, the in-Scene Background Radiance (ISBR) method was combined with a standard Optimal Estimation (OE) approach. The ISBR-OE method avoids the use of a complete and time-consuming radiative transfer model. The ISBR-OEM developed here overcomes the underestimation issues of the linear method (LM) used in the literature for high concentration plumes and controls a posteriori uncertainty. For the Korpezhe site, using the ISBR-OEM instead of the LM -retrieved CH4 concentration map led to a bias correction on CH4 mass from 4 to 16% depending on the source strength. The most important CH4 source has an estimated flow rate ranging from 0.36 ± 0.3 kg·s−1 to 4 ± 1.76 kg·s−1 on nine dates. These local and variable sources contribute to the CH4 budget and can better constrain climate change models.

Theoretical and experimental investigation for developing a gas-liquid two-phase flow meter

Despite the intricacy, inline metering of two-phase flow has a significant impact in multitudinous applications including fusion reactors, oil, nuclear, and other cryogenic systems. Since measurement of individual flow rate is prominent in various systems, it warrants the establishment of a flow meter system that can monitor the mass flow rates of liquid. In this regard, an approach was taken towards the development of a two-phase flow meter system in the present study. The concept involves two-phase flow through narrow parallel rectangular channels resulting in laminar, stratified flow with a slope at the liquid-vapor interface. The height of the liquid column at specific channel locations is measured for determining the flow rate. However, the geometric configurations of the channels and fluid properties are pivotal in ensuring accurate measurement. Consequently, theoretical and experimental studies are performed to investigate the correspondence between flow rate and change in liquid height. Based on the governing equations, a theoretical model is established using MATLAB®. The model investigated the intricate influence of various flow and fluid properties in the estimation of the mass flow rate. The experimental investigation was done with various conditions under different liquid and vapor volume flow rates for validating the proposed supposition and the theoretical model. Both the theoretical and experimental analyses showed fair correspondence. The proposed system estimated the mass flow rate within a tolerance of ±10% and showed potential towards the development of the cryogenic two-phase flow meter.

Two-phase flow pattern classification based on void fraction time series and machine learning

Two-phase flow is a complex phenomenon present in several industrial applications such as chemical reactors, power generation, and in the exploration, production, and transport of oil and natural gas. The classification of the flow pattern is a fundamental step in such applications, as it influences several derived parameters and sub-processes such as flow rate, void fraction, and pressure drop estimation. In this paper, we propose an objective approach for classifying flow patterns using time series of void fraction (from a wire-mesh sensor), signal processing and machine learning. As novel approach, the time series is modeled as a stochastic process of independent and identically distributed samples with probability density function described by a Gaussian mixture model. The estimated parameters of the mixture are then fed into a Support Vector Machine (SVM), yielding the flow pattern classification. Tests were performed with a vertical liquid–gas flow database from a 52.3-mm-diameter pipe and the results indicate a great potential for application in real systems. The average accuracy and F-score obtained was higher than 0.94 for different test sets, with standard deviation lower than 0.08 for accuracy a lower than 0.11 for F-Score, demonstrating the efficiency and generalization of the proposed method.

Two-phase nozzles performances CFD modeling for low-grade heat to power generation: Mass transfer models assessment and a novel transitional formulation

The use of two-phase nozzles for low-grade heat valorization by electricity production increases the energy recovery rate using Trilateral Flash or Wet to Dry cycles. A model benchmark for nozzle flow rate and efficiency estimation was conducted on experimental data from the literature. These data come from a series of tests made on geothermal energy production water two-phase nozzles. A new transitional bubble-to-droplet interfacial area density formulation (TA-BD model) is presented by the paper. It is compared to three models from literature. Two nozzles operating with different inlet and outlet conditions were modeled. The calibration flexibility and the robustness of the models are discussed in association with physical analysis. The paper shows how the models using single or redundant adaptation parameters fail to provide good results simultaneously on flow rate and efficiency. It appeared that the TA-BD model is the more flexible and robust. The Homogeneous Relaxation Model (HRM) model gives also good results. Furthermore, TA-BD model gives the lowest average discrepancies. Especially at the best efficiency point of the first test case, TA-BD model shows<1% discrepancy where the HRM model has 18% discrepancy in efficiency. The TA-BD model appeared to be easier to calibrate than the HRM model. Finally, regarding the proposed TA-BD model, the sensitivity to the geometry and operating conditions shows that the interfacial area density formulation could be completed to include the effect of nozzle's section profile, the effect of the inlet temperature on bubbles number density, and the effect of outlet pressure on the droplets number density.

Estimation of peak expiratory flow rate in young Indians.

INTRODUCTION: Peak expiratory flow rate (PEFR) is commonly used to monitor the progression of respiratory diseases as it gives good information about the status of airways. A good amount of research is going across the world to establish a local prediction equation. The joint task force of the American thoracic society and European Respiratory Society has promoted research in this regard. In India, data derived from the Caucasian population are still used for PEFR. OBJECTIVE: To verify the relationship between PEF levels and the variables age, sex, anthropometric and body surface area, and establish the regression equation for young Indian adults. METHODS: A cross-sectional observational study was conducted in 15-25 years aged 1000 subjects from the Metropolitan region of Mumbai. Pearson’s correlation coefficient was used to understand the relation of anthropometric parameters and PEFR. Multivariate regression analysis was done for establishing a prediction equation (Alpha 5%). RESULTS: Age and all anthropometric parameters were correlated with PEFR. The mean PEFR of the male population was 515 ml/sec, whereas, for females, it was 399 ml/sec, for PEFR highest correlation was observed with BSA (.696) followed by weight (.667), height (.630), age (.504) whereas BMI shown lowest correlation coefficient (.445). PEFR had the best significance with age, BSA, Height, and BMI. It had less significance with weight. In females, PEFR had the best significance with Height, weight, BMI, and Age. CONCLUSION: Gender-wise differences exist in PEFR. Hence gender-specific equations are needed for the estimation of PEFR.

Volumetric Flow Estimation in a Coronary Artery Phantom Using High-Frame-Rate Contrast-Enhanced Ultrasound, Speckle Decorrelation, and Doppler Flow Direction Detection.

The coronary flow reserve (CFR), relating to the volumetric flow rate, is an effective functional parameter to assess the stenosis in the left anterior descending (LAD) coronary artery. We have recently proposed to use high-frame-rate (HFR) contrast-enhanced ultrasound (CEUS) to estimate the volumetric flow rate using ultrasound (US) speckle decorrelation (SDC) without any assumptions about the velocity profile. However, this method still has challenges in imaging deep and small vessels, such as LAD. In this study, we proposed to address the challenges and demonstrate the feasibility of volumetric flow rate measurement in a coronary mimicking phantom with pulsatile flow using a 1-D array cardiac probe, vector Doppler, and an optimal probe rotation/tilting for flow direction detection. Both simulations and in vitro experiments were conducted to validate the proposed method. It is shown that in-plane velocities estimated by vector Doppler under a 10° probe tilting resulted in smaller percentage error (+5.2%) in flow rate estimates than that in US imaging velocimetry (-20.2%) although their relative standard deviations were very close, being 2.6 and 2.8 ml/min, respectively. The flow rate estimated by SDC without direction detection had an error higher than 70%. A 10° tilting of the probe had the best results in flow rate estimation compared to the 5° or 15° tilting. Realistic global motions in the LAD increased the flow rate estimation error from 5.2% to 14.2%. It is concluded that it is feasible to measure the volumetric flow rate in a coronary artery flow phantom with a conventional cardiac probe, using HFR acquisition, Doppler, and SDC analysis. Potentially, this technique could also be applied to investigate the volumetric flow rate in other small vessels similar to the LAD.

Prediction of Interlobe Leakages in Screw Compressors

The clearance gaps in twin-screw compressors are critical for their performance and reliable operation, as the leakage flows through these clearances influence the volumetric and adiabatic efficiencies. The amount of leakage flows depends on the clearance size and shape as well as various geometric and operating parameters. Usually, the isentropic nozzle equations along with appropriate flow coefficients are used for more accurate estimation of the leakage flow rates through the clearance gaps. Hence, a proper understanding of the flow coefficients and their relationship with the dimensionless parameters (such as pressure ratio, Reynolds number, and aspect ratios) is critical for an accurate prediction of the leakage flows. In the present study, considering the interlobe clearance gap in screw compressor in terms of rectangular openings, the interlobe leakage flow rates are estimated for various opening sizes and pressure conditions using isentropic nozzle equations and an iterative method. The flow coefficients are determined by comparing the experimental values obtained using a specialized test rig and the flow rates obtained from the analytical methods. The dimensionless parameters are varied to see their individual effect on the leakage mass flow rates and on the flow coefficients. The mean deviation from the experimental results when using an analytical iterative procedure (-8.5%) is substantially lower in comparison to the mean deviation (+26.8%) using the isentropic nozzle equations. The study validates that the iterative method can be preferred (for an interlobe leakage flow rate prediction) over the isentropic nozzle equation method.

Multiphase flowrate measurement with time series sensing data and sequential model

Accurate multiphase flowrate measurement is challenging but vital in the energy industry to monitor the production process. Machine learning has recently emerged as a promising method for estimating multiphase flowrates based on different conventional flow meters. In this paper, we propose a Convolutional Neural Network (CNN)-Long-Short Term Memory (LSTM) model and a Temporal Convolutional Network (TCN) model to estimate the volumetric liquid flowrate of oil/gas/water three-phase flow based on the Venturi tube. The volumetric flowrates of the liquid and gas phase vary from 0.1–10 m3/h and 7.6137–86.7506 m3/h, respectively. We collected time series sensing data from a Venturi tube installed in a pilot-scale multiphase flow facility and utilized single-phase flowmeters to acquire reference data before mixing. Experimental results suggest that the proposed CNN-LSTM and TCN models can effectively deal with the time series sensing data from the Venturi tube and achieve a good accuracy of multiphase flowrate estimation under different flow conditions. TCN achieves a better accuracy for both liquid and phase flowrate estimation than CNN-LSTM. The results indicate the possibility of leveraging conventional flow meters for multiphase flowrate estimation under various flow conditions.