Open access peer-reviewed chapter

Equation of State

Written By

Eman Mohamed Mansour

Submitted: February 5th, 2019 Reviewed: September 25th, 2019 Published: March 4th, 2020

DOI: 10.5772/intechopen.89919

From the Edited Volume

Inverse Heat Conduction and Heat Exchangers

Edited by Suvanjan Bhattacharya, Mohammad Moghimi Ardekani, Ranjib Biswas and R. C. Mehta

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An equation of state (EOS) is a thermodynamic expression that relates pressure (P), temperature (T), and volume (V). This equation is used to describe the state of reservoir fluids at given conditions. The cubic equations of state (CEOS) such as Van der Waals, Redlich-Kwong, Soave, and Peng-Robinson are simple models that have been widely used in the oil industry. This chapter expressed literature for EOS that varies from simple expressions to multiple constant and convoluted types of equations. Many attempts have been made to describe the thermodynamic behavior of fluids to predict their physical properties at given conditions. So, several forms of the equation of state have been presented to the oil industry in order to calculate reservoir fluid properties. The heat exchanger is important in wildly fields as in aerospace, petrochemical industry, refrigeration, and other fields. The optimization design of the heat exchanger is a great significance to industry process to reduce production cost, realize energy conservation, and reduce energy consumption.


  • EOS types
  • EOS importance
  • EOS history
  • cubic equation of state
  • heat transfer

1. Introduction

In the last six decades, petroleum engineers realized the importance of using EOS for PVT modeling in addition to the following [1]:

  1. Predicting physical properties of the black oil in the laboratories is very expensive.

  2. Difficulty to obtain a representative sample due to reservoir nature or operation issues.

  3. Insufficient sample volume in order to obtain a complete analysis.

  4. Error in lab analyses.

  5. Samples are nonrepresentative (not monophasic sample, OBM contamination, etc.).

  6. Lab report quality check.

  7. Estimating the hydrocarbon reserves.

1.1 Classification of equation of state

There are different types of EOS which fall into three categories:

1.1.1 First class of EOS

These equations are basically cubic equation of state. The cubic equations of state such as the Van der Waals [2], Redlicha and Kwong [3], Soave-Redlich-Kwong [4], and Peng-Robinson [5] equations give reasonable results for the thermodynamic behavior of real fluids.

1.1.2 Second class of EOS

These EOS are non-cubic in form. They are providing accurate results for both vapor and liquid phases. The Benedict et al. [6] equation is a good example for this class equation.

1.1.3 Third class of EOS

These are nonanalytical EOS that are highly constrained for some specific fluids [7]. Even though they are constrained, they are capable of expressing real fluid thermodynamic properties precisely.

Among all these EOS, the first-class EOS is more useful because it provides an analytical solution than the more complex and complicated non-cubic second type and nonanalytical third type that require time-consuming iterative calculations. In general, the overall performance in fluid properties prediction is somewhat better using the Soave-Redlich-Kwong (SRK) equation than using the Redlich-Kwong (RK) and Van der Waals EOS [8].


2. Development history of the equation of state

Several forms of EOS have been presented to the petroleum industry to estimate hydrocarbon reservoir fluid properties and sought to a better representation of the PVT relationship for fluids [9].

In 1662, Robert Boyle (Boyle’s law) discovered that for a constant temperature, there is an inverse relationship between volume of gas and its pressure (P ∝ V−1). In 1780, Jacques Charles (Charles’s Law) showed that the volume of gas is proportional to the absolute temperature at a constant pressure (V ∝ T). In 1834, Clapeyron combined these two results into the ideal gas law, PV = RT [10], assuming that the molecules are very far and have no attractive or repulsive forces between them and elastic collisions between these molecules. This equation is known as the ideal gas law and/or the general gas law. It is expressed mathematically as [11].

P V = n R T E1

where P: absolute pressure, psia; V: volume, ft3; T: absolute temperature, °R; R: the universal gas constant (10.73159 ft3 psia °R−1 lb-mole−1; n: number of moles of gas, lb-mole.

For gases at low pressures, the ideal gas law is a convenient satisfactory tool. The application of the ideal gas law at higher pressures may lead to errors up to 500%, compared to 2–3% at atmospheric pressure. Real gases behave differently than ideal gases, the reason for this deviation is that the ideal gas law was derived under the assumption that the volume of molecules is very small and neither molecular attraction nor repulsion exists between them, and this is not the real case. In order to write an equation of state for a real gas, a correction factor has to be inserted into the ideal gas equation [12]:

P V = Zn R T E2

where Z: the correction factor which is known as the compressibility factor.

The equation has different names, such as the compressibility equation and/or the real gas equation [13]. A review of recent advances in the empirical cubic EOS field is presented next [11]. Van der Waals [2] is one of the earliest attempts to represent the behavior of real gases by an equation, where the two assumptions were made for the ideal gas EOS:

  1. The gas molecule volume is very small compared to the volume of the container.

  2. There are no attractive or repulsive forces between the gas molecules or the walls of the container.

Van der Waals attempted to eliminate these assumptions in the development of an empirical EOS for the real gases.

First assumption elimination: the gas molecules occupy a considerable fraction of the volume at higher pressures, and the volume of the molecules (b) is subtracted from the actual molar volume (V) to give the following expression:

p = RT v b E3

Second assumption elimination: he added corrective term (a), denoted by (a/V2), in order to account for the attractive forces between molecules.

Van der Waals introduced the following equation (Eq. (4)):

p + a V M 2 V M b = RT E4

where a: attraction parameter; b: repulsion parameter.

The symbol “a” is considered a measure of the intermolecular attractive forces between the molecules. “b” is known as the co-volume and considered to reflect the volume of molecules [2]. The “a” and “b” values can be obtained from the critical properties of the fluid [14], where the repulsion pressure, prepulsion, is represented by the term RT/(Vm – b), and the attraction pressure, pattraction, is described by a/Vm 2. The Van der Waals equation of state despite its simplicity, while it is provide a correct description and qualitative of the PVT substances behavior in the liquid and gaseous phases. Yet, it is not accurate enough to be suitable for design purposes. The equation of state approach for calculating physical properties and phase equilibrium proved to be a powerful tool, and much energy was devoted to the development of new and accurate equations of state [11]. Other researchers began attempts to improve Van der Waals equation of state for over 100 years. Usually a change of the molecular attraction term (a/VmM2) was proposed. Clausius in 1880 [15] proposed that the molecular attraction term was inversely proportional to temperature [16]:

p + a T V M + c 2 V M b = RT E5

The addition of a fourth constant (c) enabled better agreement with data. However, mathematical manipulations required in thermodynamic calculations were more difficult. So Berthelot in 1899 [17] removed the constant (c), resulting in the following equation:

p + a TV M 2 V M b = RT E6

Dieterici in 1899 [18] handled the temperature dependence of the molecular attraction term in a different manner [6]:

P EXP a V M RT V M b = RT E7

Lorentz in 1881 [19] addressed the molecular volume term [20]:

p + a V M 2 V M bV M V M + b = RT . E8

Wohl in 1927 [21] considered the effect of temperature on the molecular attraction term:

P + a TV M V M b c T 2 V M 3 VM b = RT E9

The constants a, b, and c in the equations above have different values for different substances. Several investigations proposed virial-type of EOS. Kammerlingh-Onnes in 1901 [22] proposed the virial equation of state as follows [23]:

PV M = RT 1 + B V M + C V M 2 + . . E10

where B and C are not constants which are functions of temperature and are called the second and third virial coefficients. Beattie and Bridgeman in 1927 published a five-constant equation that gives a satisfactory representation of volumetric properties except in the critical region [24]:

P = RT V M 2 1 c V M T 3 V M + B o 1 b V M A o 1 a / V M V M E11

Benedict et al. [6] suggested a multiparameter equation of state known as the Benedict-Webb-Rubin (BWR) equation [6]:

P = RT V M + B o RT A o C o / T 2 V M 2 + bRT a V M 3 + V M 6 + c T 2 V M 3 1 + γ V M 2 EXP γ V M 2 E12

This equation may be considered a modification of the Beattie-Bridgeman equation of state where A0, B0, C0, a, b, c, α, and γ are eight adjustable parameters. The BWR equation could treat critical components and was able to work in the critical area. However, the BWR equation suffers from some disadvantages [25]. Perhaps, the most important model for the modification of the Van der Waals equation of state is the Redlich-Kwong (RK) (1949) which is demonstrated by an adjustment of the Van der Waals’s attraction term (a/Vm 2) and includes the system temperature explicitly. They could improve the prediction of the physical and volumetric properties of the vapor phase. In RK EOS, the attraction pressure term was replaced with a generalized temperature-dependent term (Eq. (13)) [3]:

ρ = RT V b α V V + b T E13

For pure substances, the equation parameters a and b are usually expressed as.

b = Ωb R T c / P c E14
a = Ω α R 2 T c 2.5 / P c E15

where Ωa = 0.42747 and Ωb = 0.08664.

Replacing the molar volume (V) in Eq. (13) with (ZRT/P) and rearranging give.

Z 3 Z 2 + A B B 2 Z AB = 0 E16


B = bp RT E17
A = ap R 2 T 2.5 E18

Three real roots in the two-phase region are yielded. The largest root corresponds to the compressibility factor of the gas phase, Zv, while the smallest positive root corresponded to that of the liquid, ZL [11].

For mixtures, the equation parameters a and b are usually expressed as am and bm for a hydrocarbon liquid mixture with a composition of xi:

a m = i = 1 n Xi a i 2 E19
b m = i = 1 n X i b i E20

am and bm for a hydrocarbon gas mixture with a composition of yi:

α m = i = 1 n y i a i 2 E21
b m = i = 1 n y i b i E22

where n: number of components in the mixture; ai: Redlich-Kwong a parameter for the i component; bi: Redlich-Kwong b parameter for the i component; bm: parameter b for mixture; xi: mole fraction of component i in the liquid phase; yi: mole fraction of component i in the gas phase.

Replacing the molar volume (V) in Eq. (13) with (ZRT/P) and rearranging give.

Z 3 Z 2 + A B B 2 Z AB = 0 E23


B = b m p RT E24
A = a m P R 2 T 2.5 E25

Then the compressibility factor of the gas phase or the liquid can be calculated.

Joffe and Zudkevitch [26] showed that a substantial improvement in the representation of fugacity of gas mixtures could be obtained by treating interaction parameters as empirical parameters [26]. Spear et al. [27] also states that the RK equation of state could be used to calculate the vapor-liquid critical properties of binary mixtures [28]. Chueh and Prausnitz [29] showed that the RK equation can be adapted to predict both vapor and liquid properties. Spear et al. [28] gave seven examples of systems for which the vapor-liquid critical properties of hydrocarbon mixtures could be calculated by using the RK equation of state. Carnahan and Starling [30] used the Redlich-Kwong equation of state to calculate the gas-phase enthalpies for a variety of substances [30]. Their results showed that the Redlich-Kwong equation was a significant improvement over the Van der Waals equation. Other workers applied the Redlich-Kwong equation to the critical properties and the high-pressure phase equilibria of binary mixtures. The results showed that the accuracy of the Redlich-Kwong equation of state calculations for ternary systems was only slightly less than that for the constituent binaries [31].

The success of the Redlich-Kwong equation has been the impetus for many further empirical improvements. One of the milestones in developing of CEOS was reported by Soave [4]. His development in the evaluation of the parameter in the attraction pressure term for the RK equation is shown in (Eq. (22)). Soave replaced the term (a/T0.5) in Eq. (22) with a more general temperature-dependent term, denoted by a α (T), to give

ρ = RT V b a α T V V + b E26

where α(T) is a dimensionless factor. Soave used vapor pressures of pure components to introduce an expression for the temperature correction parameter α(T). At temperatures other than the critical temperature, the correction parameter α(T) was defined by the following equation:

α T = 1 + m 1 T r 2 E27

Soave correlated the parameter “m” with the centric factor (ω) to give.

m = 0.480 + 1.57 , 0.17 2 E28

where Tr: reduced temperature, °R; ω: a centric factor of the substance; T: system temperature, °R.

For pure substances the equation parameters a and b are usually expressed as.

b = Ωb R T c / P c E29
a = Ωa R 2 T c 2 / P c E30

In general, most EOS inputs are only the critical properties, and a centric factor of each components is shown in Table 1.

Compound Formula Molecular weight Critical pressure (psla) Critical temperature (°F) Critical volume (ft3/lb) Liquid specific gravity (water = 1) Gas specific (air = 1) Acentric factor
Methane CH4 16.042 667.0 −116.66 0.0985 (0.3) 0.55400 0.0115
Ethane C2H6 30.069 706.6 89.92 0.0775 0.35643 1.03830 0.0994
Propane C3H8 44.096 615.5 205.92 0.0728 0.50738 1.52270 0.1529
Isobutane C4H10 58.122 527.9 274.41 0.0715 0.56295 2.00710 0.1865
n-Butane C4H10 58.122 550.9 305.55 0.0703 0.58408 2.00710 0.2003
Isopentane C5H12 72.149 490.4 369 0.0685 0.62460 2.49140 0.2284
n-Pentane C5H12 72.149 488.8 385.8 0.0676 0.63113 2.49140 0.2515
n-Hexane C6H14 86.175 436.9 453.3 0.0688 0.66404 2.97580 0.2993
n-Heptane C7H16 100.202 396.8 512.9 0.0682 0.68819 3.46020 0.3483
n-Octane C8H18 114.229 360.7 564.2 0.0673 0.70698 3.94450 0.3977
n-Nonane C9H20 128.255 330.7 610.8 0.693 0.72186 4.42890 0.4421
n-Decane C10H22 142.282 304.6 652.2 0.0703 0.73406 4.91330 0.4875
Carbon monoxide CO 28.01 506.7 −220.63 0.0527 0.79265 0.96720 0.0510
Carbon dioxide CO2 44.01 1070.0 87.76 0.0343 0.82203 1.51970 0.2239
Hydrogen sulfide H2S 34.082 1306.5 212.81 0.0462 0.80269 1.17690 0.1010
Air 28.9586 551.9 −220.97 0.0458 0.87603 1.00000
Hydrogen H2 2.0159 190.7 −399.9 0.5319 0.07087 0.06961 −0.2140
Oxygen O2 31.9988 731.4 −181.43 0.0367 1.14230 1.10500 0.0222
Nitrogen N2 28.0135 492.5 −232.53 0.0511 0.80687 0.96740 0.0372
Water H2O 18.0153 3200.1 705.1 0.04975 1.00000 0.62210 0.3443

Table 1.

Physical properties of each components.

where Ωa and Ωb are the SRK dimensionless pure component parameters:

Ωa = 0.42747.

Ωb = 0.08664.

Replacing the molar volume (V) in the equation with (ZRT/p) and rearranging give the compressibility factor Z:

Z 3 Z 2 + A B B 2 Z AB = 0 E31


B = b m p RT E32
A = a m P RT 2 E33

For mixtures, the equation parameters a and b are usually expressed as am and bm for a hydrocarbon liquid mixture with a composition of xi:

a m = i j [ x i x j a i a j α i α j 1 k ij E34
b m = i Xi bi E35

The following is the calculation for am and bm for a gas mixture with a composition of yi:

a m = i j [ y i y j a i a j α i α j 1 k ij E36
b m = i yi bi E37

A binary interaction parameter (BI), classically noted as kij, is usually involved in the “a” parameter expression to provide more flexibility to the EOS and designed to characterize any binary system formed by components i and j in the hydrocarbon mixture [32]. Vidal and Daubert [33], Graboski and Daubert [34], and Slot-Petersen [35] suggested that no BIs were required for hydrocarbon systems. However, with no hydrocarbons present, binary interaction parameters can improve the phase in volumetric behavior predictions of the mixture by the SRK EOS for compressibility factor calculations of the gas or the liquid phases [34, 36, 37]. The equilibrium ratio, Ki, that is, Ki = yi /xi, can be redefined in terms of the fugacity of component:

K i = f i L / XiP f i V / y i P = Φ i L Φ i v E38

where fi v = fugacity of component “i” in the gas phase; fi L = fugacity of component “i” in the liquid phase; Φ i v = fugacity coefficient of component “i” in the vapor phase; Φ i L = fugacity coefficient of component “i” in the liquid phase.

Soave proposed the following expression for the fugacity coefficient of component i in the liquid phase:

ln f i L X i P = ln Φ i L = b i Z l 1 b m ln Z L B A P 2 ψ i a m b i b m ln 1 + B Z L E39


ψ j = j x j a i a j α i α j 1 k ij E40
a m = i j [ x i x j a i a j α i α j 1 k ij E41

Fugacity coefficient of component i in the gas phase:

ln ϕ j = b i Z i 1 b m ln Z i B A B 2 ψ i a m b i b m ln 1 + B Z i E42


ψ j = j y j a i a j α i α j 1 k ij E43
a m = i j [ y i y j a i a j α i α j 1 k ij E44

3. Heat exchanger

Heat exchanger is an energy (heat) exchange equipment, where it transfers the heat from a working medium to another working medium. Knowing heat exchanger is important in wildly fields as in aerospace, petrochemical industry, refrigeration, and other fields. The optimization design of the heat exchanger is a great significance to industry process to reduce production cost, realize energy conservation, and reduce energy consumption [38]. The development technique for different types of the heat exchanger has been reviewed by many researchers. The development method can be by two ways: passive method and active method. The passive method is to generate swirling flow and disturb the thermal boundary layer by installing vortex generator or tabulators such as baffle, rib, winglet, wing, etc. The active method is to add the external power to increase efficiency and heat transfer rate such as vibration. So the use of the active method must consider both benefit of the system and additional power cost [39].

3.1 Optimization techniques of heat exchanger

The optimization techniques of heat exchanger can be shown at three different stages as the following [40]:

  1. Identification of the lowest initial cost of a heat exchanger design that meets the process specifications.

  2. Identification of a heat exchanger design that will work most acceptably over the plant lifetime.

  3. Identification of the minimum total cost of the process by choosing heat exchangers system and auxiliary components that will make the best plant process specifications.

3.2 Criteria of the ideal heat exchanger

The dissimilarities of optimization techniques levels can be understood if we list the required criteria of the ideal heat exchanger as follows [41]:

  1. The heat exchangers should be flexible enough to meet specifications process such as normal fouling transients and seasonal and diurnal changes in service stream temperatures.

  2. Special requirements as weight, length, or inventory standards are important for heat exchangers especially in retrofit applications.

  3. The heat exchanger must endure operation under standard and foreseeable operating conditions, maintain the mechanical stresses of manufacturing transport, and minimize the effects of fouling and corrosion.

  4. The heat exchanger must be maintainable, cleaning, repair or replacement and its components as gaskets and tubes with minimum downtime.

  5. The exchanger must achieve process specifications, i.e., achieve any changes in the thermal conditions by allowable pressure drops.

  6. There are other requirements, such as experience, capability of operating, maintenance personnel, and manufacturing time.

  7. The exchanger should cost as little as possible.


4. Fluid flow and heat transfer mechanism

The heat transfer and fluid flow mechanism in porous media is important in engineering and industrial fields such as petroleum and chemical engineering [42]. This mechanism occurs for many studies such as in microchannels and nanofluids.

Example 1: In case of study characteristics of fluid flow and heat transfer in the (100) silicon microchannel heat sink, the heat convection capabilities in the phase changes as well as in a single-phase flow and the mechanism of bubble nucleation. In the heat transfer characteristics, the results illustrate that changing in the phase process in the microchannels reduces environment working temperature and absorbs the heat. Six different microchannel geometries are selected for the heat transfer experiment as shown in Table 2.

Chip name Width (μm)
Depth (μm)
Hydraulic diameter (μm) Number of channels
Chip 1 400 260 221 10
Chip 2 300 130 150 13
Chip 3 250 184 134 15
Chip 4 200 148 109 19
Chip 5 150 113 83 25
Chip 6 100 78 57 38

Table 2.

Specification of the sink.

Figure 1 shows that the decreasing wall temperature phenomenon during the phase change is the same as Peng and Wang [43].

Figure 1.

The heat flux and channel wall temperature.

On the aspect of fluid flow characteristics, the effects of the viscosity and friction coefficient of the fluid in the microchannels are much significant than the macros. Where the specifications of the sink are registered in Table 2, Chip 1–4 are prepared for fluid flow experiment. The friction factor is decreasing with the power of Reynolds number as shown in Figure 2 [44].

  1. Nanoparticle types and concentration in the base fluids

  2. Changing in thermophysical properties such as viscosity, surface tension, thermal conductivity, density, and heat capacity

  3. The operation condition especially the mass and heat fluxes

  4. The modification of wettability and capillary wicking force surface roughness

Figure 2.

Exponential relations between the friction factor and the Reynolds number. Example 1: in the case of study characteristics of fluid flow and heat transfer, nanofluid is widely utilized in numerous industrial applications such as boiler tubes, evaporators, and cooling of reactors in a nuclear power plant. The main parameters that directly influence on the heat transfer performance are listed as follows [45].

During the last 2 years, there were some review papers which outlined the subject of boiling heat transfer using nanofluids as a new category in thermal fluids. Available results reported that the effect of nanoparticles on the flow boiling HTC is conflicting, but the CHF could enhance on 50%. During the boiling process, parameters such as flow pattern and pressure drop were affected by the deposition of nanoparticles [46]. Authors concluded that using nanofluids might intensify the boiling HT and CHF, depending on many parameters related to additives, nanoparticles, geometry concentration, and fluid properties [47].

Their work shows how the nanofluids can achieve high heat flux with small temperature differences throughout the boiling process, which modify the critical heat flux [48]. All article reviewers said that nanofluids is a complicated phenomenon and it is not fully understood from mechanism of boiling heat transfer and two-phase flow. Collected studies show enhancement in CHF, and its improvement could achieve more than 50% [49]. HTC behavior could increase or decrease during flow boiling and pool, and it depends on several parameters related to surface texture on thermophysical properties [50]. Wang et al. shows the mechanism of depositing nanoparticles on the heating surface and creating the porous layer as shown in Figure 3. The results show that the CHF of flow boiling is enhanced up to 18% as compared to conventional fluid. This enhancement increases with increasing some parameters, for example, the pressure system and the channel diameter [45].

Figure 3.

Illustration of the mechanism of flow boiling CHF using nanofluid introduced.

Example 2: in the case of study fluid flow and heat transfer characteristics using nanofluid in a single-phase turbulent flow by using helically corrugated tubes, pitch-to-diameter ratio (P/DH = 0.18, 0.22 and 0.27) and rib-height-to-diameter ratio (e/DH = 0.02, 0.04 and 0.06) of helically corrugated tubes on the heat transfer enhancement, isothermal friction, and thermal performance factor in a concentric tube heat exchanger are examined. Results illustrate that the thermal performance of the corrugated tube and heat transfer are increased as compared to those of the smooth tube. The rate increase in heat transfer rate is between 123 and 232%, depending on the rib height/pitch ratios and Reynolds number. The friction factor (average) of the corrugated tube is between 1.46 and 1.93 over the smooth tube [51].



EOSequation of state
CEOScubic equations of state
MSRKEmodified Soave-Redlich-Kwong equation of state
psystem pressure, psia
Pc critical pressure, psia
Pr reduced pressure, psia
pi initial pressure, psia
Tsystem temperature, oF
Tc critical temperature, oR
Tr reduced temperature, oR
Ti initial temperature, oF
aequation of state attraction parameter
bequation of state repulsion parameter
Bsecond virial coefficient
Cthird virial coefficient
a, b, c, A0, B0, C0 constant in Benedict-Webb-Rubin equation
a, b, c, A0, B0 constant in Beattie and Bridgeman equation of state
fv i fugacity of component i in the gas phase
fL j fugacity of component j in the liquid phase
Øv i fugacity coefficient of component i in the vapor phase
ØL i fugacity coefficient of component i in the liquid phase
xmole fraction of gas phase
ymole fraction of liquid phase
kequilibrium ratio for the component
Kij interaction coefficient parameter
A, Bparameter in Soave-Redlich-Kwong equation of state
aT temperature-dependent coefficient in Soave-Redlich-Kwong equation of state
ac constant coefficient in Soave-Redlich-Kwong equation of state
aTi temperature-dependent coefficient of component i
mparameter in Soave-Redlich-Kwong equation of state
Zcompressibility factor
Zi the mole fraction of component in the mixture
ZL compressibility factor of the liquid phase
Zv compressibility factor of the gas phase
ωa centric factor of the substance
nl number of moles in liquid phase
nv number of moles in gas phase
nt total number of moles of mixture
Fl one mole liquid of total fraction
Fv one mole vapor of total fraction
Xi mole fraction in the liquid phase
yi mole fraction in the vapor phase
γconstant in Benedict-Webb-Rubin equation of state


  1. 1. Asadisaghandi J, Tahmasebi P. Comparative evaluation of back-propagation neural network learning algorithms and empirical correlations for prediction of oil PVT properties in Iran oilfields. Journal of Petroleum Science and Engineering. 2011;78(2):464-475
  2. 2. Van der Waals JD. Over de Continuiteit van den Gas-en Vloeistoftoestand. Leiden: A.W. Sijthoff; 1873
  3. 3. Redlich O, Kwong JN. On the thermodynamics of solutions. V. An equation of state. Fugacities of gaseous solutions. Chemical Reviews. 1949;44(1):233-244
  4. 4. Soave G. Equilibrium constants from a modified RedlichKwong equation of state. Journal of Chemical Engineering Science. 1972;27:1197-1203
  5. 5. Peng DY, Robinson DB. A new two-constant equation of state. Journal of Engineering Chemical Fundamental. 1976;15:59-64
  6. 6. Benedict M, Webb GB, Rubin LC. An empirical equation for thermodynamic properties of light hydrocarbons and their mixtures I. Methane, ethane, propane and n-butane. The Journal of Chemical Physics. 1940;8(4):334-345
  7. 7. Goodwin RD. Provisional Thermodynamic Functions of Propane, from 85 to 700 K at Pressures to 700 Bar. National STANDARD Reference Data System. Institute for Basic Standards. 1977
  8. 8. Patel NC, Teja AS. A new cubic equation of state for fluids and fluid mixtures. Chemical Engineering Science. 1982;37(3):463-473
  9. 9. Whitson CH, Brulé MR. Phase Behavior, Henry L. Doherty Memorial Fund of AIME, Society of Petroleum Engineers; 2000
  10. 10. Atllhan M. A new cubic equation of state [M.Sc. thesis]. Texas: A&M University; 2004
  11. 11. Ahmed T. Equations of State and PVT Analysis. Gulf Professional Publishing; 2013
  12. 12. Mansour E, Desouky S, Batanoni M, Mahmoud M, Farag A, El-Dars F. Modification proposed for SRK equation of state. Oil and Gas Journal. 2012;110(6):78-91
  13. 13. Yao Y, Xie T, Gao Y. Physical Chemistry Handbook. Shanghai Science and Technology Publishing Company; 1985. p. 706
  14. 14. Wei YS, Sadus RJ. Equations of state for the calculation of fluid phase equilibria. AIChE Journal. 2000;46:169-196
  15. 15. Clausius R. Ueber des verhalten der kohlensaure in begug auf druck, volumen and temperatur. Annual Physical Chemistry. 1880;9:337
  16. 16. William D. The Properties of Petroleum Fluids Handbook. Vol. 148. Tulsa: PennWell Publishing Company; 1994
  17. 17. Berthelot DJ. Sur une méthode purement physique pour la détermination des poids moléculaires des gaz et des poids atomiques de leurs éléments. Journal of Physics. 1899;8:263
  18. 18. Dieterici C. Ueber den kritischen Zustand. Annual Physical Chemistry. 1899;30:685
  19. 19. Lorentz. Wied. Annual Physical Chemistry. 1881;12(127):660
  20. 20. Boyd MF. Studies on the bionomics of north American AnopheLines: Physical and chemical factors in their relation to the distribution of larvae in northeastern North Carolina. American Journal of Hygiene. 1929;9(2):6
  21. 21. Wohl KZ. Physical Chemistry. 1927;133:305
  22. 22. Kammerlingh-Onnes HK. Commununications from the Physics Laboratory. Vol. 71. Leiden, Holland; 1901
  23. 23. Adamson AW, Gast AP. Physical Chemistry of Surfaces. New York: Interscience; 1967
  24. 24. Beattie JA, Bridgeman OC. A new equation of state for fluids. I. Application to gaseous ethyl ether and carbon dioxide1. Journal of the American Chemical Society. 1927;49(7):1665-1667
  25. 25. Mansour E, Farag A, El-Dars F, Desouky S, Batanoni M, Mahmoud M. Predicting PVT properties of Egyptian crude oils by a modified Soave-Redlich-Kowng equation of state. Egyptian Journal of Petroleum. 2013;22(1):137-148
  26. 26. Joffe J, Zudkevitch D. Fugacity coefficients in gas mixtures containing light hydrocarbons and carbon dioxide. Industrial & Engineering Chemistry Fundamentals. 1966;5(4):455-462
  27. 27. Spear RR, Robinson RL, Chao KC. Critical states of mixtures and equations of state. Industrial Engineers Chemical Fundamental. 1969;8:2-8
  28. 28. Spear RR, Robinson RL Jr, Chao K-C. Critical states of ternary mixtures and equations of state. Industrial & Engineering Chemistry Fundamentals. 1971;10(4):588-592
  29. 29. Chueh PL, Prausnitz JM. Vapour-liquid equilibria at high pressures: Calculation of partial molar volumes in non-polar liquid mixtures. AIChE Journal. 1967;13:1099
  30. 30. Carnahan NF, Starling KE. Intermolecular repulsions and the equation of state for fluids. AICHE Journal. 1972;18(6):1184-1189
  31. 31. Mansour E, Al-Sabagh A, Desouky S, Zawawy F, Ramzi M. Experimental approach of minimum miscibility pressure for CO2 miscible flooding: Application to Egyptian oil fields. International Journal of New Technology and Research. 2016;2(5):8
  32. 32. Mansour E, Al-Sabagh A, Desouky S, Zawawy F, Ramzi M. A laboratory investigation of carbon dioxide-enhanced oil recovery by focusing on CO2-oil physical properties. Egyptian Journal of Petroleum. 2019;28(1):21-26
  33. 33. Vidal J, Daubert T. Equations of state-reworking the old forms. Journal of Chemical Engineering Society. 1978;33:787-791
  34. 34. Graboski MS, Daubert TE. A modified soave equation of state for phase equilibrium calculations. 1. Hydrocarbon systems. Industrial & Engineering Chemistry Process Design and Development. 1978;17(4):443-448
  35. 35. Slot-Petersen C. A systematic and consistent approach to determine binary interaction coefficients for the Peng-Robinson equation of state. In: 62nd, Annual Technical Conference of the SPE, Dallas, TX; 27-30 September 1987
  36. 36. Slot-Petersen C. A systematic and consistent approach to determine binary interaction coefficients for the Peng-Robinson equation of state (includes associated papers 20308 and 20393). SPE Reservoir Engineering. 1989;4(04):488-494
  37. 37. Vidal J. Equations of state-reworking the old forms. Fluid Phase Equilibria. 1983;13:15-33
  38. 38. Zeugmann T, Poupart P, Kennedy J, Jin X, Han J, Saitta L, et al. Particle Swarm Optimization. Boston, MA: Springer US; 2011. pp. 760-766
  39. 39. Boonloi A, Jedsadaratanachai W. Numerical study on flow and heat transfer mechanisms in the heat exchanger channel with V-orifice at various blockage ratios, gap spacing ratios, and flow directions. Modelling and Simulation in Engineering. 2019;2019:5
  40. 40. Rao RV, Patel V. Multi-objective optimization of heat exchangers using a modified teaching-learning-based optimization algorithm. Applied Mathematical Modelling. 2013;37(3):1147-1162
  41. 41. Yao J. A review of industrial heat exchange optimization. In: IOP Conference Series: Earth and Environmental Science. IOP Publishing; 2018
  42. 42. Chatti S, Ghabi C, Mhimid A. Fluid flow and heat transfer in porous media and post heated obstacle: Lattice Boltzmann simulation. International Journal of Heat and Technology. 2016;34(3):377-385
  43. 43. Peng X, Peterson G, Wang B. Frictional flow characteristics of water flowing through rectangular microchannels. Experimental Heat Transfer An International Journal. 1994;7(4):249-264
  44. 44. Chen Y-T, Kang S-W, Tuh W-C, Hsiao T-H. Experimental investigation of fluid flow and heat transfer in microchannels. 淡江理工學刊. 2004;7(1):11-16
  45. 45. Kamel MS, Lezsovits F, Hussein AK. Experimental studies of flow boiling heat transfer by using nanofluids. Journal of Thermal Analysis and Calorimetry. 2019;138:1-25
  46. 46. Zhang C, Zheng L, Zhang X, Chen G. MHD flow and radiation heat transfer of nanofluids in porous media with variable surface heat flux and chemical reaction. Applied Mathematical Modelling. 2015;39(1):165-181
  47. 47. Fang X, Chen Y, Zhang H, Chen W, Dong A, Wang R. Heat transfer and critical heat flux of nanofluid boiling: A comprehensive review. Renewable and Sustainable Energy Reviews. 2016;62:924-940
  48. 48. Kamel MS, Lezsovits F, Hussein AM, Mahian O, Wongwises S. Latest developments in boiling critical heat flux using nanofluids: A concise review. International Communications in Heat and Mass Transfer. 2018;98:59-66
  49. 49. Cheng L, Xia G, Li Q, Thome JR. Fundamental issues, technology development, and challenges of boiling heat transfer, critical heat flux, and two-phase flow phenomena with nanofluids. Heat Transfer Engineering. 2018;3:1-36
  50. 50. Moreira TA, Moreira DC, Ribatski G. Nanofluids for heat transfer applications: A review. Journal of the Brazilian Society of Mechanical Sciences and Engineering. 2018;40(6):303
  51. 51. Pethkool S, Eiamsa-Ard S, Kwankaomeng S, Promvonge P. Turbulent heat transfer enhancement in a heat exchanger using helically corrugated tube. International Communications in Heat and Mass Transfer. 2011;38(3):340-347

Written By

Eman Mohamed Mansour

Submitted: February 5th, 2019 Reviewed: September 25th, 2019 Published: March 4th, 2020