Calibrating and operating Coriolis flow meters with respect to process effects

Mills, Chris

doi:10.1016/j.flowmeasinst.2019.101649

Cited by 20 publications

(4 citation statements)

References 10 publications

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“…where ϵx is a random variable and depends on the value x of X [8]. • Classification refers to using data characteristics to separate into a certain number of classes [9].…”

Section: Machine Learningmentioning

confidence: 99%

“…Another algorithm that has been described by Muller, Kernelized support vector machine (well known as SVM), as "an extension that allows for more complex models that are not defined simply by hyperplanes in the input space" [11]. While in this research we implemented also neural network that has the idea to model high-dimensional discrete distributions has already been found useful to learn the joint probability of a set of random variables [12] and SGD that has descirebed by Neopolitan as "stochastic approximation of gradient descent for the case when the objective function is the sum of differentiable functions" [8]. Actual industrial process measurement could be simulated by developing the measurement skid system.…”

Section: Machine Learningmentioning

confidence: 99%

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Statistical and machine learning application approach to predict mass flowrate

Rahmali,

Prajitno,

Surpan

et al. 2023

J. Phys.: Conf. Ser.

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Flow measurement is one of the critical points in the industrial process measurement. Mass flow measurement, as an alternative flow measuring method, is usually chosen to prevent temperature effects to the density that would be impacted directly to the flow measurement. One of the most popular direct mass flow measurements is Coriolis Mass Flowmeter (CMF). CMF which has an accuracy of mass flow measurement up to 0.1% of reading, recently became widely used for valuable fluids mass flowrate measurement. The challenge of the flow measurement will not stop in the selection and the smart capabilities of the flow instrument itself; another issue that appears during process measurement is the absence of the installed CMF as a mandatory installed, local pressure measurement could be a physical parameter that could be used to predict the mass flowrate using statistical and machine learning algorithms. This virtual flow measurement method will be an approach to face the absence of physical flowmeter issues, especially in critical or remote operation processes.

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“…where ϵx is a random variable and depends on the value x of X [8]. • Classification refers to using data characteristics to separate into a certain number of classes [9].…”

Section: Machine Learningmentioning

confidence: 99%

Section: Machine Learningmentioning

confidence: 99%

Statistical and machine learning application approach to predict mass flowrate

Rahmali,

Prajitno,

Surpan

et al. 2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…Manfaat yang dapat diambil dari pembuatan control water flow smart metering adalah meningkatkan efisiensi waktu dan tenaga serta dapat mengatur penggunaan air, mempermudah transaksi pembayaran air berlangganan, mencegah terjadinya telat pembayaran tagihan air berlangganan yang disebabkan petugas pencatatan meter air konvensional yang tidak dapat mencatat meteran dikarenakan meteran air tidak dapat di akses oleh petugas [3].…”

Section: Intisari-unclassified

Kontrol Water Flow Smart Metering pada Pemakaian Air PDAM (Perusahaan Daerah Air Minum) Menggunakan Nomor Token

Syahputra¹,

Rifai²,

Adhisuwignjo³

2021

elkolind

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“…An example is the widespread acceptance of calibrating a flow meter with air for use on gases other than air (see gas meter standards [3], [4]). Since fluid properties and process conditions in the actual application are different from those during calibration, a discussion arises on the degree of transferability of the calibration to the conditions in which the flow meter is used, and the extent to which alternative fluid calibrations lead to additional measurement uncertainty in the application (see e.g., [5], [6] for the Liquefied Natural Gas (LNG) flow meter application or [7] for a discussion on effects from temperature, pressure, and fluid viscosity on Coriolis Flow Meters (CFMs)). Logically, two approaches can be followed in an attempt to resolve the discussion.…”

Section: Introductionmentioning

confidence: 99%

Establish traceability for liquefied hydrogen flow measurements

Schakel¹,

Gugole²,

Standiford³

et al. 2023

Proceedings of the 19th International Flow Measurement Conference 2022 on Flow Measurement - FLOMEKO 2022

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The EU aims to be climate-neutral by 2050 and usage of liquid hydrogen (LH2) for transportation is expected to grow fast. With the expected uptake, traceability in custody transfer is required. Existing metrological infrastructure can be used to provide traceability with basic calibrations performed typically under ambient conditions. However, due to the very challenging LH2 process conditions, with temperatures as low as 20 K, there is a need to determine the flow measurement uncertainty at these process conditions. Within the Joint Research Project (JRP) 20IND11 "Metrology infrastructure for high-pressure gas and liquified hydrogen flows" (MetHyInfra) [1], traceability for liquefied hydrogen flow measurements is developed by a three-pronged approach: (I) assessment of transferability of water and LNG calibrations to LH2 conditions; (II) cryogenic Laser Doppler Velocimetry (LDV) adapted to LH2 flow applications; (III) assessment of transferability of water, liquefied nitrogen, and liquefied helium calibrations in the vaporisation method to LH2 conditions. In this paper the initial MetHyInfra project results are presented comprising: (I) description of LH2 flow meters, water and LNG calibration results, analytical model prediction statements of uncertainty at LH2 conditions when calibration is performed under ambient conditions, finite element numerical modelling analysis of various thermal effects affecting CFMs at LH2 conditions, (II) design modifications of cryogenic LDV to ensure operability at LH2 conditions, (III) description of the vaporisation standard. It was found that obtaining a definite quantitative number of liquefied hydrogen flow measurement uncertainty from the analytical model is challenging for a variety of reasons.

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Calibrating and operating Coriolis flow meters with respect to process effects

Cited by 20 publications

References 10 publications

Statistical and machine learning application approach to predict mass flowrate

Statistical and machine learning application approach to predict mass flowrate

Kontrol Water Flow Smart Metering pada Pemakaian Air PDAM (Perusahaan Daerah Air Minum) Menggunakan Nomor Token

Establish traceability for liquefied hydrogen flow measurements

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