Two-phase flow metering of heavy oil using a Coriolis mass flow meter: A case study

The use of Coriolis mass flow metering for two-phase (gas/liquid) flow is an emerging theme of both academic research and industrial application. The key issues are maintaining flow-tube operation, and modelling and correcting for the errors induced in the mass flow and density measurements. Experimentally-derived data is used to illustrate that these errors vary most notably with gas void fraction (GVF) and liquid flow rate, but other factors such as flow-tube geometry and orientation, and fluid properties such as viscosity are also influential. While undoubtedly a universal two-phase flow correction model is the ultimate research goal, there is currently no obvious candidate to explain the range of behaviours observed. This paper describes and demonstrates an empirical methodology that has proven effective in developing good correction models for a given choice of Coriolis flow-tube and flow mixture.

A growing proportion of the world’s oil reserves may be described as “heavy”, implying high density and high viscosity. Of the various metering challenges heavy oil poses, one of the most significant is its ready entrainment of gas, and the difficulties entailed in separating gas from the oil. Accurate two-phase measurement of heavy oil is therefore an especially desirable technical goal.

Trials were carried out at the National Engineering Laboratory (NEL), Scotland on a 75 mm flowmeter using a high viscosity oil. Flowrates from 1 kg/s to 10 kg/s were examined, with gas void fraction (GVF) up to 80%. The resulting models were tested online in a commercial Coriolis mass flow meter and demonstrated good performance for both steady and slugging two-phase flows, with the corrected measurements typically within 1%–5% of the nominal mass flow and density.

Field trials in Venezuela have confirmed the performance of this two-phase solution.

While research continues into the development of a generic two-phase correction, this case study demonstrates that the current state of the art can provide, for economically important fluids, tailored models with good two-phase flow performance.     

Sensor calibration and compensation using artificial neural network

Artificial neural network (ANN) based inverse modeling technique is used for sensor response linearization. The choice of the order of the model and the number of the calibration points are important design parameters in this technique. An intensive study of the effect of the order of the model and number of calibration points on the lowest asymptotic root-mean-square (RMS) error has been reported in this paper. Starting from the initial value of the nonlinearity in the characteristics of a sensor and required RMS error, it is possible to quickly fix the order of the model and the number of calibration points required using results of this paper. The number of epochs needed to calibrate the sensor, and thereafter the epochs needed to recalibrate in event of sensitivity or offset drifts, are also presented to bring out the convergence time of the technique. More importantly, the advantages of the ANN technique over traditional regression based modeling are also discussed from the point of view of its advantage in hardware simplicity in microcontroller based implementation. Results presented in this paper would be of interest to instrumentation design engineers.     

Coriolis mass flow measurement at cryogenic temperatures

With the growing interest in liquefied natural gas (LNG) in the energy market, Coriolis mass flowmeters have been applied to many applications in the distribution of LNG. Since Coriolis flowmeters are normally calibrated at around room temperatures, measurements for LNG at cryogenic temperatures present a challenging condition. Firstly, a theoretical analysis for Coriolis mass flow sensors is provided considering the major changes of material properties (Young’s modulus and thermal expansion) at cryogenic temperatures. Then, a practical approach which can be used to correct the flow calibration factor obtained at a reference condition is presented. Finally, flow test results obtained from NIST’s cryogenic calibration facility are provided. Based on the results, it can be concluded that if a Coriolis flowmeter is calibrated at a reference condition and the flow calibration factor is corrected considering the non-linearity of Young’s modulus and thermal expansion change with temperature, it can still provide very accurate mass flow measurement even at cryogenic temperatures.     

Effect of pulsating flow on Coriolis mass flowmeters

The last ten years have seen an increasing use of Coriolis flowmeters in the measurement of small flows of liquids. The performance of Coriolis meters under pulsating flow conditions was not well known, however. This paper presents some investigations on such flowmeters in the presence of monofrequent and polyfrequent pulsating flows. The linearity of the flow characteristics is checked mathematically and the mechanical resonances of the measuring tubes are determined. Two U-tube type Coriolis meters are tested and the measuring error resulting from the pulsation of the flow is examined. The investigations are carried out with monofrequent pulsation by a special test rig flow and with geared and piston type pumps (polyfrequent excitation).     

微弯型科氏质量流量计测量气-液两相流研究

与普通U形和Δ形科氏质量流量计相比,微弯型科氏质量流量计固有频率更高、相位差更小,测量气-液两相流时误差更大。为此,设计气-液两相流实验方案,采用课题组研制的科氏质量流量变送器进行气-液两相流实验,采用BP人工神经网络对测量误差进行建模,得到误差模型,实现对气-液两相流测量误差的在线实时修正。实验结果表明,当密度降在0%~30%范围内变化时,通过在线修正,气-液两相流测量误差从原来的最大为-50%减小到-5%~3%以内,取得了很好的效果。     

Coriolis mass flow measurement of gas under normal conditions

This paper presents a new method of directly measuring the mass flow of gas using the well-known Coriolis principle, which has proved successful for mass flow measurement of liquids. The prototype consists of two U-shaped tubes, forming a device resembling very much a tuning fork, which is stimulated by electromagnetic actuators to perform autonomous bending oscillations. By this means the fluid is subjected to a radial velocity that, in combination with the axial velocity of the flow, induces harmonic Coriolis forces of the same frequency. This causes the U-shaped tube to perform torsional oscillations that superimpose on the bending oscillations. Both oscillations can be detected via electromagnetic transducers.

The amplitude of the torsional oscillation induced by the Coriolis forces is very small as the density of gas is very low. It can be amplified by tuning the eigenfrequencies of torsion and bending in a control loop. This results in an amplification of the torsional amplitude by a factor of 10², allowing the mass flow of gas to be measured under normal conditions.     

On-line viscosity measurement using Coriolis mass flowmeters

Industrial viscometers are available in large variety to measure for both Newtonian and non-Newtonian fluids. A simple system which uses the Coriolis mass flowmeter as a capillary tube has a growing range of applications. Employing the capillary principle, the rheological properties of time-independent Newtonian fluids and some non-Newtonian fluids can be successfully measured. The method applies readily available and proven components, namely mass flowmeters and differential pressure transducers. Basic viscosity calibration can be achieved with suitable software. To add viscosity measurement capability to an existing flowmeter site in most cases requires only the installation andd connection of a suitable differential pressure transducer.     

磨料水射流切割质量的神经网络预测

磨料水射流切割质量影响因素较多,难以建立有效的理论模型,结合实验结果,建立磨料水射流切割质量的神经网络预测模型。结果表明,对于所给定切割参数,该模型能快速、准确、可靠地预测出切割质量。     

A bicriterian flow shop scheduling using artificial neural network

This paper considers the sequencing of jobs that arrive in a flow shop in different combinations over time. Artificial neural network (ANN) uses its acquired sequencing knowledge in making the future sequencing decisions. The paper focuses on scheduling for a flow shop with ‘m’ machines and ‘n’ jobs. The authors have used the heuristics proposed by Campbell et al.(1970, A heuristic algorithm for n-jobs m-machines sequencing problem) to find a sequence and makespan (MS). Then a pair wise interchange of jobs is made to find the optimal MS and total flow time (TFT). The obtained sequence is used for giving training to the neural network and a matrix called neural network master matrix (NNMM) is constructed, which is the basic knowledge of the neurons obtained after training. From the matrix, interpretations are made to determine the optimum sequence for the jobs that arrive in the future over a period of time. The results obtained by the ANN are compared with a constructive heuristics and an improvement heuristics. The results show that the quality of the measure of performance is better when ANN approach is used than obtained by constructive or improvement heuristics. It is found that the system’s efficiency (i.e., obtaining the optimal MS and TFT) increases with increasing numbers of training exemplars.     

Prediction of burr height formation in blanking processes using neural network

Productivity and quality in sheet metal blanking processes part can be assessed by the burr height of the sheared edge after blanking. This paper combines predictive finite element approach with neural network modelling of the leading blanking parameters in order to predict the burr height of the parts for a variety of blanking conditions.Experiments on circular blanking operation has been performed to verify the validity of the proposed approach.The numerical results obtained by finite element computation including damage and fracture modelling and tool wear effects were utilized to train the developed simulation environment based on back propagation neural network modelling.A trained neural network system was used in predicting burr height of the blanked parts versus tool wear state and punch-die clearance.The comparative study between the results obtained by neural network computation and the experimental ones gives good results.     

Prediction of ship fuel consumption by using an artificial neural network

Journal of Mechanical Science and Technology - A smart ship collects various data with large volume, such as voyage, machinery, and weather data. Thus, big data analysis for smart ships is an...     

Modeling of Coriolis mass flow meter of a general plane-shape pipe

The vibration of a general plane tube with a flowing fluid, which is the measuring element in a Coriolis mass flow meter (CMF), is studied. The dynamic stiffness matrix method is used to model such a tube. The tube is divided into straight and circular elements. The elements dynamic stiffness matrices are derived from the equations of motion. By assembling the elements matrices into a global matrix the natural frequencies are obtained. The mode shapes are obtained by applying the boundary conditions at the supports and the compatibility conditions at the nodes. The effects of the flow velocity on the natural frequency and the relative phase difference are modeled. The method is applied to different tube shapes. The results are compared to the published data which reveals good agreement.     

Uncertainties associated with finite element modelling of Coriolis mass flow meters

The problem of computing accurate Coriolis distortion modes in mass flow meters is discussed. This is illustrated by several numerical results, and it is tentatively suggested that the problem is due mainly to computer rounding error rather than any fundamental weakness in the finite element method or the eigensolvers. An empirically evaluated successful method, using a shifted inverse iteration, for computing high quality results is given.     

科里奥利颗粒料质量流量计流量公式理论分析

本文通过对科里奥利固体颗粒料质量流量计的结构分析，认为该流量计无法获取实时的质量流量，并对公开的瞬时流量计算公式进行了理论分析，说明其理论上的缺陷，给出了详细的推证过程和更为精确的理论计算方法和措施。     

Experimental determination of the dynamic response of Coriolis mass flow meters

The dynamic response of Coriolis meters is significant in many applications, including fast control operations, e.g. short duration (seconds or less) batch-filling, dosing flows, and the potential for application to static gas turbine engine fuel flow control. The dynamic response of a meter is determined firstly by the dynamic response of the flow tube (as detected by the two motion sensors) and subsequently by the data sampling and signal processing algorithms used to extract the phase-difference to generate the user output. The flow tube dynamic response and meter indicated response (pulse output) were determined experimentally for a number of commercially available meters, by subjecting each meter to step changes in flow rate. The fastest steps achieved were of duration 4.5 ms. It has previously been shown that the meter flow tube response time, as extracted through phase-difference measurements, cannot be less than the duration of one drive cycle of the tube vibration. Correspondingly, flow tube dynamic response times in the range of 1.4–10 ms were observed (for meter drive frequencies (approximate) in the range of 700–100 Hz). As predicted by theory (straight tube) and finite element simulation, the flow tube step response also includes contaminating (noise) components associated with the Coriolis frequency. There are indications that this noise amplitude was increased by mechanical vibration effects induced by the flow step mechanism. As expected, the meter user output (pulse) indicated much slower step responses than those of their respective flow tubes. These outputs were characterised by a delay in the onset of the step and subsequent lengthening of the step duration which was associated with the output update rate. In some cases, the step noise was apparently eliminated in the user output and this effect was enhanced by the relatively slow update rate.     

Prediction of limiting dome height using neural network and finite element method

Using neural network to predict limiting dome height (LDH) based on the result of finite element analysis is a high efficiency work in spite of little error. Finite element results are presented with different working condition parameters, such as material thickness, punch speed, friction coefficient between punch, die and sheet metal, and blank holder force. Then, a two-layer back propagation network is developed to best fit this discrete engineering problem. Different number of neurons in the hidden layer, three commonly used training algorithms, and two performance functions are adopted and compared to choose the suitable one to minimize the error between the predictive value and the simulation results (one with ideal output). After comparison, the neuron number in the hidden layer is determined to be 12 and the appropriate learning algorithm is Levenberg–Marquardt algorithm. The difference between two performance algorithms is small. The mean square error between the predicted value and targeted one is less than 0.2%. Finally, five sheet metal forming processes under various working conditions are predicted and compared with the finite element method (FEM) result to verify the validity of this neural network model. The small difference indicates that this neural network can predict the LDH in a certain range of working conditions.     

High precision Coriolis mass flow measurement applied to small volume proving

This paper discusses Small Volume Provers (SVPs), used in the oil and gas industry to validate the performance of custody transfer meters. Recently Coriolis mass flow meters have been introduced for custody transfer; while these offer reduced maintenance requirements over traditional PD and turbine meters, proving Coriolis flow meters using SVP is challenging. This paper presents SVP results for a Coriolis meter which matches or exceeds the most stringent requirements for custody transfer. This is achieved in part by using a novel signal processing technique which reduces the dominant component of the measurement noise, associated with so-called Coriolis mode vibration, with negligible loss of dynamic response.     

Design of T-shaped tube hydroforming using finite element and artificial neural network modeling

Journal of Mechanical Science and Technology - Tube hydroforming (THF) is a frequently used manufacturing method in the industry, especially on automotive and aircraft industries. Compared with...     

Prediction of incremental sheet forming process performance by using a neural network approach

Although the incremental sheet forming process results are widely investigated from the literature review, much more efforts are required to increase the industrial applicability of the process; first of all, the material failure for complex shapes need to be clarified. According to this aim, in this work, a preliminary analysis is carried out to detect the factors that deeply affect the process performance when a part having a changing transverse section has to be manufactured. Subsequently, a neural network approach is utilized to implement a ??ready to use?? procedure to predict failure in complex shapes.     

Prediction of calcium concentration in human blood serum using an artificial neural network

A predictive method, based on artificial neural network (ANN) has been developed to study absorbance and pH effects on the equilibrium of blood serum. This strategy has been used to analyze serum samples and to predict the calcium concentration in blood serum. A dedicated data acquisition system is designed and fabricated using a LPC2106 microcontroller with light emitting diode (LED) as source and photodiode as sensor to measure absorbance and to calculate the calcium concentration. A multilayer neural network with back propagation (BP) training algorithm is used to simulate different concentration of calcium (Ca²⁺) as a function of absorbance and pH, to correlate and predict calcium concentration. The computed calcium concentration by neural network is quite satisfactory with correlations R² = 0.998 and 0.995, standard errors of 0.0127 and 0.0122 in validation and testing stages respectively. Statistical analysis are carried out to check the accuracy and precision of the proposed ANN model and validation of results produce a relative error of about 3%. These results suggest that ANN can be efficiently applied and is in good agreement with values obtained with the current clinical spectrophotometric methods. Hence, ANN can be used as a complementary tool for studying metal ion complexion, with special attention to the blood serum analysis.