Table 3 - uploaded by Hamid Reza Rahbari
Content may be subject to copyright.
Source publication
Natural gas (NG) is a mixture of 21 elements and widely used in the industries and domestics. Knowledge of its thermodynamic properties is essential for designing appropriate process and equipments. In this study, the detailed numerical procedures for computing most thermodynamic properties of natural gas are discussed based on the AGA8 equation of...
Contexts in source publication
Context 1
... 2 shows mole percent of natural gases used in density calculation. Table 3 shows the range of temperature and pressure, and AAPD (%). Considering the values in Table 3, one could conclude that the density calculation has high accuracy with an overall AAPD of 0.0831%. ...
Similar publications
Natural gas is a mixture of 21 components and it is widely used in industries and homes.
Knowledge of its thermodynamic properties is essential for designing appropriate processes and
equipment. This paper presents simple but precise correlations of how to compute important
thermodynamic properties of natural gas. As measuring natural gas compositi...
Citations
... The design of the processes involved in the production, transportation, and storage of natural gas and H2NG mixtures, with special mention being given to the custody transfer applications, rely on the accuracy of the volumetric and calorific thermophysical properties obtained from the reference thermodynamic models and equations of state [14,15]. Reference equations of state have also been used to develop alternative methods for estimating the thermophysical properties of natural gas related mixtures without the need for costly on-line measurements of mixture composition [16][17][18]. Two of the most commonly used reference equations of state for natural-gas related mixtures are the AGA8-DC92 EoS [19], developed by the American Gas Association (AGA), and the GERG-2008 EoS [20,21], from the Groupe Européen de Recherches Gazières (GERG), that are both explicit in the Helmholtz energy. ...
... The pressure of the fluid is determined by two quartz crystal transducers: one for the low-pressure range from (0-3) MPa (Digiquartz 2300A-101, Paroscientific Inc.) and the other for the higher pressures in the range between (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) MPa (Digiquartz 43 KR-HHT-101, Paroscientific Inc.). The estimated expanded (k = 2) uncertainty is U(p) = (7.5⋅10 ...
The injection of hydrogen into the natural-gas grid is an alternative during the process of a gradual decarbonization of the heat and power supply. When dealing with hydrogen-enriched natural gas mixtures, the performance of the reference equations of state habitually used for natural gas should be validated by using high-precision experimental thermophysical data from multicomponent reference mixtures prepared with the lowest possible uncertainty in composition. In this work, we present experimental density data for an 11-compound high-calorific (hydrogen-free) natural gas mixture and for two derived hydrogen-enriched natural gas mixtures prepared by adding (10 and 20) mol-% of hydrogen to the original standard natural gas mixture. The three mixtures were prepared gravimetrically according to ISO 6142–1 for maximum precision in their composition and thus qualify for reference materials. A single-sinker densimeter was used to determine the density of the mixtures from (250–350) K and up to 20 MPa. The experimental density results of this work have been compared to the densities calculated by three different reference equations of state for natural gas related mixtures: the AGA8-DC92 EoS, the GERG-2008 EoS, and an improved version of the GERG-2008 EoS. While relative deviations of the experimental density data for the hydrogen-free natural gas mixture are always within the claimed uncertainty of the three considered equations of state, larger deviations can be observed for the hydrogen-enriched natural gas mixtures from any of the three equations of state, especially for the lowest temperature and the highest pressures.
... MPa. Farzaneh-Gord and Rahbari [14] have presented a numerical method based on AGA8-DCM and fundamental thermodynamic relations to calculate thermodynamic properties of natural gas mixtures. Comparing with experimental data, the average absolute deviation percentage (AAD%) was obtained 0.08%, 0.87%, 1.13%, 1.93%, and 0.133% for ρ, C p , C V , μ JT , and u sound , respectively. ...
... The partial derivative terms in Eq. (14) are calculated from AGA8-GCM (i.e. Eq. (1)) as the following: ...
... following relation can be written as [14]: ...
Precise computation of thermodynamic properties of natural gas requires applying an accurate Equation of State (EOS) along with component analysis of natural gas. This procedure is a time consuming process which demands expensive apparatuses. In this study, a rigorous model by firstly applying a gross standard EOS called AGA8-GCM is used to predict compressibility factor (Z) and density (ρ). The required input data for this model are operating temperature and pressure, specific gravity at reference conditions, and merely the amount of nitrogen and carbon dioxide in the natural gas. Then, Genetic Algorithm (GA) is employed to estimate component analysis to increase the accuracy of following calculations. Finally, the thermodynamic properties equations, in which partial derivatives were obtained by AGA8-GCM equation is used to calculate other thermodynamic properties: the speed of sound (usound), heat capacity at constant volume and pressure (CV, CP), Joule-Thomson coefficient (μJT), isentropic exponent (κ), internal energy (U), enthalpy (H), and entropy (S). In order to validate the model, the calculated data were compared to experimental ones collected from literature by the Average Absolute Deviation percentage (AAD%). The AAD% amounts for Z, ρ, usound, CV, CP, μJT, U, H, and S were obtained 0.025%, 0.063%, 0.51%, 0.94%, 1.22%, 3.06%, 0.064%, 0.62% and 2.46%, respectively. The pure methane data is utilized for CV, U, H, and S evaluation as for the lack of empirical data. Furthermore, comparing calculated data with that of AGA8-DCM (Detail Characterization Method), requiring detailed composition of natural gas, shows reliability of this model in the custody transfer region. Investigating the model uncertainty showed a figure of about ±0.1% for both the compressibility factor and density. However, as for the other thermodynamic properties, this figure was usually higher (<0.8%).
... The speed of sound is an important thermodynamic quantity extensively used to characterize systems containing oil and gas components and fluids in single-and two-or multiphase states in a broad range of temperatures and pressures [1][2][3][4][5]. Measurements of the thermodynamic properties for fluid mixtures often deal with an estimate of the speed of sound, since the latter helps determine the density; the component composition; and, as a consequence, the molecular weight of the mixture [6][7][8]. This thermodynamic property is of particular interest from a theoretical viewpoint for those researchers who are testing the capabilities of thermodynamic models, including equations of state (EOS's), for an accurate evaluation of sound speed; this quantity is expressed via the second-order derivative of the Helmholtz energy with respect to volume [9]. ...
... Also, Marić [21,22] developed AGA8 EOS and calculated other thermodynamic properties of natural gas such as the Joule-Thomson and the heat capacity. In the recent studies, Farzaneh-Gord et al. [23], Farzaneh-Gord and Rahbari [24] developed AGA8 EOS and presented methods and procedures for calculating thermodynamic properties of NG such as the speed of sound, entropy, enthalpy, and internal energy. Farzaneh-Gord et al. [25] studied the sensitivity of natural gas flow measurement to AGA8 or GERG-2008 EOSs. ...
... The most powerful Backpropagation learning algorithm is Levenberg-Marquadt. Equation (24) shows the Levenberg-Marquadt formula for obtaining optimum weights and biases in neural networks [41]: ...
A R T I C L E I N F O Keywords: Natural gas flow measurement GERG-2008 EOS Artificial neural network Compressibility factor Speed of sound A B S T R A C T The current study presents an intelligent method for calculating natural gas compressibility factor. The method requires three easily measurable properties including pressure, temperature, and speed of sound as inputs. As sound speed could be measured with ultrasonic flow meters, temperature, and pressure with appropriate sensors, the real-time natural gas compressibility factor could be calculated easily. The presented method eliminates the high cost of determining compressibility based on measuring natural gas composition. Artificial Neural Network is employed to develop the method. The artificial neural network is trained in a way to accept pressure, temperature , and speed of sound as inputs. To train an artificial neural network, the 30,000 random datasets of natural gas compositions were utilized. To check the validity and accuracy of the developed artificial neural network, four different natural gas compositions are selected and the compressibility factor are compared with the GERG-2008 equation of state (as standard and accurate method for calculating natural gas properties) results. The results reported the average absolute percent deviation is less than 0.7% for the compressibility factor calculation by utilizing the proposed method.
... where _ m NG and h are the mass flow rate and enthalpy of the NG, respectively. The enthalpy can be calculated through the following thermodynamic relation and AGA-8 EoS [42]: ...
In this study, a thermodynamic model is developed for a natural gas pressure reduction station, which uses solar energy as an auxiliary energy source for preheating the natural gas. To increase the duration of solar energy usage per day and the consequent decrease in the fuel consumption of the heater, a novel design is presented in which preheating the gas to lower temperatures becomes possible through the use of multi-stage preheating and pressure reduction. Through this novel design, it becomes possible to utilize a single heater to preheat all stages, which reduces the costs dramatically. To investigate the effectiveness of the proposed design in different climate conditions, a comprehensive economic analysis is conducted based on fuel saving and carbon dioxide emission reduction. The results show that the return of capital is within 1–10 years considering different parameters, including: 1- daily time duration of solar energy usage by the station before implementation of the new design, 2- additional daily time duration of solar energy usage after implementation of the new design, and 3- number of preheating and pressure reduction stages. Finally, the effects of different parameters on the return of capital are discussed.
... At present, AGA8-92DC has been the most well-known and the most accurate methods in natural gas industry. Therefore, in reported researches, AGA8-92DC has usually been used to evaluate the accuracy of other correlations [6][7][8][9][10][11][12][13]. It was also utilized as data source of the training and testing objects [6]. ...
... In this study, AGA8-92DC was used to generate the training objects and test subjects [6][7][8][9][10][11][12][13]. In order to verify the accuracy and reliability of the model, 1300 samples were calculated using AGA8-92DC as the training objects (70%) and test subjects (30%). ...
With the development of the low-power flowmeters, it is urgent and crucial to calculate the gas compression factor (Z-factor) in real time. However, the traditional estimation methods for Z-factor such as AGA8-92DC, needed a long calculation time and were difficult to be applied to the low-power embedded-based gas flowmeters. The other empirical correlations also had large errors. To solve this problem, we proposed a novel model for quick calculation of gas Z-factor based on Group Method of Data Handling(GMDH) network. The accuracy and reliability of this model were verified under different Tpr and Ppr. Compared with other empirical correlations, our model has the lowest root mean sum of squares of the errors (RMSE) of 0.0066 and mean absolute percentage error (MAPE) of 0.5615%, which is only 1/13–2/5 of MAPE calculated by the other correlations. The results show that our model has higher accuracy. Moreover, our model avoids lots of complex exponential and logarithmic operations, so it is especially useful for real-time Z-factor calculation of the low-power flowmeters
... The correlations are also a simple way to compute thermodynamic properties. Farzaneh-Gord et al. [13] and Farzaneh-Gord and Rahbari [14,15] presented a computer program for computing the natural gas thermal properties based on AGA8 equation of state. The results are also validated against the measured values. ...
There are technical problems related to storage and transport of biogas gas that should be addressed before practical injection of these fuels into the existing natural gas networks. In addition, their different final applications resulting in the presence of various components and in various concentrations make the problem harder. Therefore, it is indispensable for designers of the pipeline network to know exactly what the thermodynamic properties of a gas mixture are, especially its density, which would vary a lot. In this work, a MLP (Multi-layer Perceptron) neural network is used for the development of the desired biogas properties predictor model. In order to train the network, the biogas thermodynamic properties created using ISO 21 20765-2 (2015) (where applicable) and experimental values are employed. Results are compared with the values estimated from the GERG2008 equations of state, which are the reference equations for natural gases and experimental values. The results indicate that the developed MLP model presents a high accuracy in the calculations over a wide range of biogas mixtures and input properties ranges for all the output properties including density, compressibility factor, isochoric heat capacity, isobaric heat capacity, isentropic exponent, internal energy, enthalpy, entropy, Joule-Thomson coefficient, and speed of sound. The Root Mean Square Error (RMSE) of the mentioned properties of test data are 0.00012536, 0.00016593, 0.0025213, 0.0016208, 0.00337, 0.0096329, 0.0099837, 0.0035625, 0.01055, and 0.00039428 respectively.
... Molar enthalpy of the NG is obtained from the following relationship [29]: ...
... Entropy of NG can be obtained from the following relationship [29]: ...
One of the tools for optimizing energy systems is the design of the system output based on real (desired)
demand. Thermodynamic performance of natural gas pressure reduction stations are functions of inlet
conditions. In order to investigate the impacts of changes in inlet pressure and temperature on performance of a natural gas pressure reduction station, energy consumption and exergy destruction of a
natural gas pressure reduction station of 10,000 SCMH are evaluated for different inlet conditions. In
order to improve the station performance, a novel modification is proposed in the present research based
on the real demand of preheating, wherein thermodynamic operation of the regulator is modeled and
minimum pre-heating temperature of natural gas is calculated based on desirable temperature at the
regulator outlet (natural gas hydrate formation temperature). Indeed, once the temperature at the heater
outlet reaches the calculated minimum temperature, the heater is turned off. Compared to conventional
stations, the modified station exhibits at least 33% and 15% reductions in energy consumption and exergy
destruction, respectively. The results of investigating the performance of two sample stations also show
that by implementing the proposed modification, CO2 emission can be reduced by up to 80% or even
higher.
... Despite the emergence in the future of other EOS, first GERG-2008 [5], also adopted by the State Standards of the Russian Federation [6], the AGA8 EOS remains a sufficiently reliable criterion for solving problems associated with determining the thermodynamic and thermophysical parameters of natural gas. In particular, based on the AGA8 EOS, modern methods for calculating a number of natural gas properties are proposed, which are in complete agreement with the calculations using the GERG-2008 EOS [7][8][9]. The most attractive, accurate, and relatively simple calculation procedure is the method proposed by one of the authors of this article, tested on a vast array of specific data on natural gas, mainly in Iranian fields [7,8], as well as for transportation conditions in Iranian pipelines and gas distribution stations. ...
... In particular, based on the AGA8 EOS, modern methods for calculating a number of natural gas properties are proposed, which are in complete agreement with the calculations using the GERG-2008 EOS [7][8][9]. The most attractive, accurate, and relatively simple calculation procedure is the method proposed by one of the authors of this article, tested on a vast array of specific data on natural gas, mainly in Iranian fields [7,8], as well as for transportation conditions in Iranian pipelines and gas distribution stations. Since the calculations are carried out based on only data on temperature, pressure, and speed of sound, this allows us to regard the proposed approach as an express method. ...
Dear collegues, you can find full-text access to a view-only version of our paper by using the following SharedIt link:
https://rdcu.be/bCZCI
Abstract: Some calculation aspects of natural gas properties based on limited numbers of initial experimental parameters, namely temperature, pressure, and speed of sound, were considered. The application possibilities for a wide range of compositions, temperatures, and gas mixtures pressure, simulating natural gases of various fields using the previously proposed Farzaneh-Gord method were discussed. It has been shown, that this method, in reality, yields fairly accurate results, calculating molecular weight of natural gas and its density. We note that good calculation results of mass flow rate make it possible to recommend this method for practical express calculations.
... Londono et al. [12] reported simplified correlations for estimating the density of natural gas, while AlQuraishi and Shokir [3] reported a new equation for computing the density of hydrocarbon gases using Alternating Conditional Expectations (ACE) algorithm. Farzaneh-Gord et al. [13] and Farzaneh-Gord and Rahbari [14] developed novel correlations for calculating density of natural gas based on AGA8 EOS. ...
Two intelligent-based models which do not require complete gas compositions were used to estimate natural gas density correction factor using comprehensive datasets (almost 60,000 instances) originating from the AGA8-DCM (Detail Characterization Method) standard: (1) NGDC-ANN model (Natural Gas Density Calculator based on Artificial Neural Network) and (2) AGA8-GCMD model (Gross Characterization Method Developed by applying genetic algorithm technique). In the suggested models, only five input variables (specific gravity at base condition, operating temperature and pressure and molar composition of CO 2 and N 2 ) are applied. The experimental datasets obtained (68 instances) and literature (505 instances) are applied to validate the developed model showing a very good agreement between experimental and estimated data. Simplicity, improving accuracy, and satisfactory results of the suggested models over a wide range of operational conditions showed that these models would be excellent alternatives for the traditional standard methods, so that, the NGDC-ANN model prediction besides of its simplicity to use show the highest accuracy over a wide of operational range compared to similar models.
