New correlations to predict natural gas viscosity and compressibility factor

Heidaryan, Ehsan; Moghadasi, Jamshid; Rahimi, Masoud

doi:10.1016/j.petrol.2010.05.008

Cited by 93 publications

(62 citation statements)

References 17 publications

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“…This mathematical procedure adds to the complexities involved in the engineering calculations [6]. Correlations, on the other hand, are much easier to use and provide the solution in a shorter time [4]; however, they sometimes impose additional complications on the problem, lead to errors and their accuracy, especially close to and beyond the experimental data used for the curve fitting is under question [7].…”

Section: Introductionmentioning

confidence: 99%

Using an artificial neural network to predict carbon dioxide compressibility factor at high pressure and temperature

Mohagheghian

Zafarian-Rigaki

Motamedi-Ghahfarrokhi

et al. 2015

Korean J. Chem. Eng.

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Carbon dioxide injection, which is widely used as an enhanced oil recovery (EOR) method, has the potential of being coupled with CO 2 sequestration and reducing the emission of greenhouse gas. Hence, knowing the compressibility factor of carbon dioxide is of a vital significance. Compressibility factor (Z-factor) is traditionally measured through time consuming, expensive and cumbersome experiments. Hence, developing a fast, robust and accurate model for its estimation is necessary. In this study, a new reliable model on the basis of feed forward artificial neural networks is presented to predict CO 2 compressibility factor. Reduced temperature and pressure were selected as the input parameters of the proposed model. To evaluate and compare the results of the developed model with pre-existing models, both statistical and graphical error analyses were employed. The results indicated that the proposed model is more reliable and accurate compared to pre-existing models in a wide range of temperature (up to 1,273.15 K) and pressure (up to 140 MPa). Furthermore, by employing the relevancy factor, the effect of pressure and temprature on the Z-factor of CO 2 was compared for below and above the critical pressure of CO 2 , and the physcially expected trends were observed. Finally, to identify the probable outliers and applicability domain of the proposed ANN model, both numerical and graphical techniques based on Leverage approach were performed. The results illustrated that only 1.75% of the experimental data points were located out of the applicability domain of the proposed model. As a result, the developed model is reliable for the prediction of CO 2 compressibility factor.

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Section: Introductionmentioning

confidence: 99%

Using an artificial neural network to predict carbon dioxide compressibility factor at high pressure and temperature

Mohagheghian

Zafarian-Rigaki

Motamedi-Ghahfarrokhi

et al. 2015

Korean J. Chem. Eng.

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“…Equation (6) is the Favre-averaged ideal gas equation used to model the continuous steam phase. Additionally, the compressibility factor, Z, is the ratio of the actual volume to the ideal volume of real gas, and correlates how much a gas can deviate from perfect behaviour (Heidaryan, Moghadasi, & Rahimi, 2010). To ensure an ideal gas model is an appropriate assumption to use in this case, the compressibility factor, which is unity for a perfect gas, is calculated for the superheated steam at the operating pressure and temperature given in this case.…”

Section: Conservation Equationsmentioning

confidence: 99%

CFD simulations of polymer devolatilization in steam contactors

Gabor

Shindle

Chandy

2017

Engineering Applications of Computational Fluid Mechanics

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Removal of volatiles and other unwanted products in a polymer mix is a critical step in polymer manufacturing. This process serves many purposes, such as improvement in the physical and chemical properties of the polymer, elimination of odours, recovery of monomers and solvents, and fulfilment of environmental regulations. In this study, the mixture of polymer and solvent, or cement, is mixed with superheated steam which causes the unwanted solvent to evaporate. As the cement droplets lose solvent and dry out, they are ejected as cement 'crumb' from the mixing device. This process is modelled using computational fluid dynamics (CFD) to gain insight into the complex phenomena. The model simulates the heat transfer and phase changes associated with cement and steam, via 3D axisymmetric calculations. Using an Eulerian-Lagrangian approach, the superheated steam is modelled as the continuous phase and tracked in an Eulerian frame, while the cement droplets are treated using a Lagrangian tracking method, thus as a whole allowing us to track the particle sizes, temperatures, and solvent content. Furthermore, a parametric study is carried out to analyse the effect of initial polymer temperature on final polymer product. The simulation is carried out with three different initial polymer temperatures and the resulting solvent concentration, and the size of the cement particles between the three cases are compared. By increasing the cement operating temperature, the solvent concentration in the cement crumb is significantly lower, and the final cement crumb sizes shows a slight decrease, which indicates better production performance. Indeed, full 3D CFD calculations, presented to verify the axisymmetric assumption, show differences in the particle statistics, which could have implications for design considerations. Such studies can provide further insights into control parameters that can potentially enhance the efficiency of such a manufacturing process. ARTICLE HISTORY

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“…In this method, the Z-factor is solved as a root of the EOS [1]. Beside the complexity of calculations in some particular EOSs, the interactions between gas molecules and consequently gas PVT behavior are not well simulated in most of EOSs.…”

Section: Equation Of States (Eoss)mentioning

confidence: 99%

“…This type of Z-factor correlations is classified into two categories; indirect models which have been derived iteratively [16,[18][19][20][21][22] and direct methods that have been obtained by fitting techniques [1,[23][24][25][26][27][28]. Direct methods are more complex, require initial value and extensive computation, and cause higher magnitude of error [25].…”

Section: Empirical Correlationsmentioning

confidence: 99%

“…Thus, a fast and accurate calculation of Z-factor is highly desirable for process engineering calculations and for lower complexity of simulations in the thermodynamics context. This importance is highlighted when dealing with various aspects such as material balances, gas reserve evaluation, gas reservoir simulation, gas well testing and gas processing technologies in upstream and downstream sectors [1,2].…”

Section: Introductionmentioning

confidence: 99%

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