Effects of Natural Gas Compositions on CNG Fast Filling Process for Buffer Storage System

Farzaneh–Gord, Mahmood; Rahbari, Hamid Reza; Deymi–Dashtebayaz, Mahdi

doi:10.2516/ogst/2012010

Cited by 34 publications

(30 citation statements)

References 15 publications

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“…Other thermodynamic properties are then obtained from thermodynamic tables. Farzaneh-Gord et al (2007, 2008a, 2008b, 2012a, 2012b, 2013a have used the same method to analyse the filling process of an adiabatic cylinder. Here, the energy equation for the cylinder wall was also solved simultaneously to determine the inside wall temperature.…”

Section:  mentioning

confidence: 99%

“…It was also found that ambient temperature influences the filling process and storage capacity (Shipley, 2002). Farzaneh-Gord et al (2007, 2008a, 2008b, 2012b, 2012c, 2013b have also simulated the fast filling process of CNG in several studies. The results indicated that ambient temperature has considerable effects on the filling process and final cylinder conditions.…”

Section: Introductionmentioning

confidence: 99%

“…They also employed a theoretical analysis to study the effects of buffer and cascade storage tanks on the performance of a CNG fuelling station (Farzaneh-Gord et al, 2011). The effects of natural gas compositions on the fast filling process for buffer and cascade storage tanks have also been examined (Farzaneh-Gord et al, 2012b, 2012c. The storage tanks are treated as adiabatic in all of their studies.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Complete Modelling of the Filling Process of Hydrogen Onboard Vehicle Cylinders

Deymi–Dashtebayaz

Farzaneh–Gord²,

Nooralipoor³

et al. 2016

Braz. J. Chem. Eng.

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-Complete modelling of the filling process occurring in a hydrogen-fueled vehicle storage cylinder is examined. A simultaneous modelling of the flow and heat transfer within the cylinder and cylinder wall has not been considered in previous studies. Rapid filling may result to an unexpected temperature rise and breaching of the safety standards. In the present study, initially a correlation was developed based on a numerical simulation for predicting the heat transfer rate between in-cylinder flow and the cylinder inside wall. Then, a thermodynamic model was developed for predicting transient variations of temperature and pressure inside the cylinder and wall temperature during the filling. The model was applied to a type III onboard storage cylinder filling process. The numerical results are compared with previously measured values and showed good agreement. The results also show that a great portion of heat dissipation from the incylinder flow is stored in the cylinder wall. It is also found that ambient temperature during the refueling process has considerable effects on filling behavior in general and in particular on the final in-cylinder temperature and filled mass.

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Section:  mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Complete Modelling of the Filling Process of Hydrogen Onboard Vehicle Cylinders

Deymi–Dashtebayaz

Farzaneh–Gord²,

Nooralipoor³

et al. 2016

Braz. J. Chem. Eng.

Self Cite

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“…The heat lost from the on-board NGV cylinder to environment could be calculated as (11) Combining 5, 10 and 11, one could calculate the above equation as: (12) Or in the following form: (13) The above equation could be rearranged in the following form:…”

Section: First Law Analysismentioning

confidence: 99%

Optimizing Natural Gas Fueling Station Reservoirs Pressure Based on Ideal Gas Model

Farzaneh–Gord

Deymi–Dashtebayaz

2013

Polish Journal of Chemical Technology

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At CNG fuelling station, natural gas is usually stored in a cascade storage system to utilize the station more effi cient. The cascade storage system is generally divided into three reservoirs, commonly termed low, medium and high-pressure reservoirs. The pressures within these three reservoirs have huge effects on the performance of a CNG fuelling station and a fast fi lling process of natural gas vehicle's (NGV) cylinder. A theoretical analysis is developed to study the effects of the reservoirs pressures and temperatures on the performance of the CNG station. The analysis is based on the fi rst and the second law of thermodynamics, conservation of mass and ideal gas assumptions. The results show that as the reservoir temperature decreases, the fi ll ratio increases and the pressure within the fi lling station reservoirs has no effects on the fi ll ratio. The non-dimensional entropy generation and fi lling time profi les have opposite trends and as entropy generation decreases, the fi lling time increases. The optimized non-dimensional low and medium pressure-reservoir pressures are found to be as 0.24 and 0.58 respectively in thermodynamic point of view.

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“…5 shows scattered error according to Eq. 6 for these compositions, And also Fig 6. for Turkman gas [14] show relative error between AGA8 EOS and ANN results and it can be understood from all scattered figures that errors for this trained ANN are less than 0.5% that it guarantees the good training ANN. IV.…”

Section: Resultsmentioning

confidence: 99%

Prediction of Natural Gas Z-Factor by Using ANN Method

2018

AET-18,ACBMS-2018,SACDEE-18,BEHSSS-18,IPCEEE-18 May 1-2,2018 Istanbul (Turkey)

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Abstract-Calculation of the natural gas thermodynamic properties is one of the most important topics in natural gas industry. To calculate the natural gas thermodynamic properties, the compressibility factor must first be calculated. Accordingly, the aim of current investigation is to calculate the natural gas compressibility factor in different conditions with high accuracy. AGA8 EOS is one of the well-known and accurate EOSs in natural gas industry which historically is used for determining natural gas properties. Although, AGA8 equation of state could predict compressibility factor precisely but has a few limitations. In current investigation, an Artificial Neural Network (ANN) is used to overcome this limitation and also speed up calculations. The use of the ANN systems for predicting natural gas properties is a novel method in this field which limited researches have been done around it. The inputs of the ANN system are temperature, pressure and mole fraction of the natural gas components and the output is compressibility factor. For training the ANN system, more than 20000 datasets are collected from AGA8 EOS. This data number is higher than all previous studies in this field and guarantees the high accuracy of the calculations. The results show that the errors of the ANN predictions are less 1% at temperature and pressures that describes in AGA8 validation range which proves the accuracy of ANN model.

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Effects of Natural Gas Compositions on CNG Fast Filling Process for Buffer Storage System

Cited by 34 publications

References 15 publications

The Complete Modelling of the Filling Process of Hydrogen Onboard Vehicle Cylinders

The Complete Modelling of the Filling Process of Hydrogen Onboard Vehicle Cylinders

Optimizing Natural Gas Fueling Station Reservoirs Pressure Based on Ideal Gas Model

Prediction of Natural Gas Z-Factor by Using ANN Method

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