Research Article  Open Access
Zahra Kalantar, Hossein Nikoofard, Faezeh Javadi, "Extension of LIR Equation of State to Alkylamines Using Group Contribution Method", International Scholarly Research Notices, vol. 2013, Article ID 804576, 9 pages, 2013. https://doi.org/10.1155/2013/804576
Extension of LIR Equation of State to Alkylamines Using Group Contribution Method
Abstract
In this work, the modified linear isotherm regularity (LIR) equation of state parameter table is extended in order to represent volumetric behaviour of primary alkylamines. In addition, the isothermal compressibility and thermal expansion coefficient of these compounds have been predicted. To do so, we consider each of primary alkylamine as a hypothetical mixture of methyl, methylene and a primary amine functional group, in which the interaction potential of each pair is assumed to be the average effective pair potential. Then, the LIR equation of state has been extended to such a hypothetical mixture. Furthermore, three basic compounds, namely, propane, butane, and cyclohexane are used to obtain the contribution of methyl and methylene groups in the EOS parameters, and also other appropriate compounds are used to obtain the contribution of the primary amine functional groups, such as 1pentylamine for the contribution of −CH_{2}NH_{2} and 2aminopentane for the contribution of groups. The calculated EOS parameters along with the modified EOS are then used to calculate the density and its derivatives for alkylamines at different pressures and temperatures. The obtained results for different properties are compared with the experimental values.
1. Introduction
The thermodynamic studies are important for efficient design of chemical processes, to develop correlation and prediction methods applicable over wide temperature and pressure ranges. Among others, volumetric properties such as density and its derivatives are of great interest not only for industrial applications but also for fundamental aspects. These properties can be obtained either experimentally or by thermodynamic modeling based on the equation of state (EOS). Since experimental measurements are lengthy and costly, the amount of experimental works can be reduced if efficient thermodynamic models are used to calculate the properties at different conditions of pressure and temperature. However, equations of state which are often used to predict thermodynamic properties require pure fluid parameters as inputs. The values of such parameters are, however, not only fluid specific but also temperature dependent.
To develop an EOS which is predictive, a group contribution method (GCM) can be used. This method has strong theoretical ties to statistical mechanical theory. The main idea of GCM is to reduce all the interactions existing in the system to those pertaining to the pairs of the functional groups or segments from which the molecules are built. Therefore, properties and/or EOS parameters of corresponding chemicals may be calculated through formulae accounting for weighted contributions of the different groups present in the molecules. In the last 3 decades, this method has provided a practical and powerful tool for predicting as well as calculating the parameters of equations of state from the chemical composition and state of matter. In 1974, Nitta et al. published the first group contribution EOS [1] that is based on the cell model and is only able to deal with some pure compounds in liquid phase. In 1975, the first successful and widely applied group contribution method, UNIFAC model was published based on the lattice theory [2]. The further development of the group contribution methods proceeded through the generalization of the lattice theory to describe the gas and vapor properties and the highpressure vaporliquid equilibria as well [3]. SkjoldJørgensen developed a group contribution equation of state (GCEOS) by employing a CarnahanStarlingvan der Waals equation to calculate vaporliquid equilibria of nonideal mixtures with low to medium molecular weight compounds [4]. Then, Espinosa et al. [5] extended the application of this model to lowvolatile highmolecular weight compounds using a unique set of parameters; a satisfactory correlation and prediction of VLE and LLE in mixtures of supercritical fluids with natural oils and derivatives could be achieved [6]. Majeed and Wagner developed the parameters of the modified FloryHuggins theory to account for the molecular size difference [7]. Georgeton and Teja developed a GCEOS using a modified form for the perturbed hard chain equation of state [8]. Pults et al. developed chainofrotator group contribution equation of state [9]. Gros et al. introduced a group contribution associating term to the original GCEOS Helmholtz residual energy expression, extending the application of the model (socalled GC Associating EOS) to alcohols, water, gases, and their mixtures [10]. Then, GCAEOS parameters table extended in order to represent phase equilibria behavior of carboxylic acids, alcohols, water, and gases mixtures [11] and aromatic compounds containing phenol, aromatic acid, and aromatic ether compounds [12]. Also, a few group contribution hole models and their numerous versions have been appeared [13–15].
Recently, the extension of the linear isotherm regularity equation of state to long chain organic compounds such as alkanes, primary, secondary, and tertiary alcohols, ketones, and 1carboxylic acids is reported via group contribution method [16, 20]. The present paper is a fresh attempt to extend LIR equation of state to alkylamines and their mixtures and also predict the parameters of equations of state using the group contribution method.
2. Theory
2.1. Linear Isotherm Regularity Equation of State
Using the LJ (12, 6) potential for the average effective pair potential (AEPP) along with the pairwise additive approximation for the molecular interactions in the dense fluids and considering only the nearest neighbor interactions, linear isothermal regularity equation of state (LIREOS) have been derived from the exact thermodynamic relations as [21] where is the compressibility factor, is the number density, and and are the temperature dependent parameters. AEPP was considered to be the interaction between the nearest neighbor molecules, to which all of the longer range interactions are added, and also the effect of the medium in the charge distributions of two neighboring molecules was included [22]. The temperature dependencies of the LIR parameters were found as follows: where and are related to the attraction and repulsion terms of the average effective pair potential and is related to the nonideal thermal pressure.
The LIR was experimentally found to be hold for densities greater than the Boyle density (, where is the critical density) and temperature less than twice the Boyle temperature, the temperature at which the second virial coefficient is zero. According to the onefluid approximation, the regularity holds for the dense fluid mixtures as well [23].
In our previous works, we have showed that such linearity vanished for different organic compounds [16, 20]. Such a behavior is expected, because the mathematical form of the AEPP function is assumed to be the LJ (12, 6), and the potential function which is more appropriate for the sphericalsymmetrical molecules then nonspherical molecules, such as chain organic compounds, show deviation from the linear behavior of the LIR. Hence, using the group contribution method, the LIR may be modified for such fluids.
2.2. Modified LIR Equation of State for Long Chain Organic Compounds
Using the GC concept, each organic compound was considered as a hypothetical mixture of their carbonic groups, in which the interaction potential of each pair is assumed to be the average effective pair potential. This potential includes both physical and chemical (bond) interactions. Then, according to the Van der Waals onefluid approximation, the LIREOS would be appropriate for such a mixture, but the new EOS parameters depend on the group compositions in the mixture (the length of the chain, in this case) as well as temperature. Hence, if the molar density of the organic compound at temperature is , the total density for the hypothetical fluid is equal to , where is number of carbonic groups of the molecule. Therefore, the LIR is reduced to which we were referred to it as the modified linear isotherm regularity (MLIR) [16, 20]. Like the LIR, the MLIR was found to be valid for dense fluids only for . and are the MLIR parameters per each carbonic group and For all studied organic compounds, we found a better linearity for versus than versus for each isotherm, especially for the longer chains. Also we found that the values obtained for and are linear with respect to , just the same as those for the LIR parameters [16, 20],
2.3. Carbonic Group Contributions in and Parameters
To predict the MLIR parameters (, ) for the mentioned organic compounds using the group contribution method, each of these fluids has been considered as a hypothetical mixture of their carbonic groups, namely, methyl, terminal methylene, middle methylene, , , , , and groups.
If and are the contributions of methyl groups in and , and and are those for the terminal methylene groups, respectively, the contributions of methyl and terminal methylene groups in and parameters will be obtained from two basic compounds, namely, propane and butane, from the following expressions: The unlike parameters are taken as the mean geometric of the like parameters; that is, and . Note that the 2/3 and 1/3 coefficients in the former expressions are the fraction of carbonic groups (1) and (2) in propane, respectively, and 2/4 in the others are for butane. The contributions of a middle methylene group in and parameters are related to those of CH_{2} in cyclohexane. If and are the contributions of the middle methylene groups to the and , then the values of and for cyclohexane are the same as and for linear alkanes [20]. Other appropriate compounds were also used to obtain the contributions of the five functional groups to the MLIREOS parameters in the same approach: 1pentanol for the contribution of (), 2pentanol for (), 2methyl2propanol (BuOH) for (), 2pentanon for (), and 1pentanoic acid for (). We may use the values for , , , and for the basic compounds given in Table 1 along with (5) to obtain values of and for them at any temperature [16].

Having the contributions of the groups from which the molecules are built in the EOS parameters, along with dependencies of the LIR parameters to system composition, the MLIR parameters for each of the compounds mentioned were predicted using the following expressions: where Then using the calculated and parameters along with (3), densities of these compounds and their binary mixtures at different pressures and temperatures were calculated.
3. Results and Discussion
3.1. Extension of MLIR Equation of State to Primary Alkylamines
The main purpose in this section is to investigate the accuracy of the MLIREOS for primary alkylamines. According to MLIREOS, plot of must be linear against for each isotherm of these dense fluids. To do so, we may use the experimental data for these compounds to plot against for each isotherm. As shown in Figure 1 the linearity holds quite well for each isotherm of these fluids with the correlation coefficient, , for all chains.
(a) 1Pentylamine
(b) 1Hexylamine
(c) 1Heptylamine
(d) 2Aminobutane
(e) 2Aminopentane
(f) 2Aminoheptane
(g) 2Aminooctane
The line for each isotherm given in Figure 1 for different alkylamines were used to determine (from the intercept) and (from the slope) in order to calculate the parameters and for that isotherm using (4). Then the calculated values for the parameters were plotted versus to obtain the values for , , , and . These values and the correlation coefficients of (5) are given in Table 2 for each alkylamine. Having these values and using (5), the values of and for an alkylamine may be calculated at any temperature. Then having the calculated values for the parameters and using (4) along with (3), density of the alkylamine can be calculated at different pressures and temperatures.

3.2. Prediction of MLIR Parameters for Alkylamines Using GCM
The next step is to predict MLIR parameters for alkylamines using GCM. At first, we consider the 1alkylamines. Compared to an alkane, in a linear 1alkylamine one methyl group is replaced with a group. Hence, in 1alkylamine, we consider as a new functional group and use the values of and of 1pentylamine, to calculate the contribution of group () from the following expressions at temperature of interest: where the mole fractions of the methyl, terminal methylene, middle methylene, and groups in 1pentylamine are , , , and , respectively.
Similarly, in the primary alkylamines in which an group is attached to a secondary carbon atom in alkyl group, we consider as a new functional group and use the values of and of 2aminopentane to calculate the contribution of group () in the MLIR parameters at each temperature from the following expressions: where the mole fractions of the methyl, terminal methylene, and groups in 2aminopentane are , , and , respectively.
We may use the values for , , , and for the basic compounds given in Table 2 along with (5) to obtain values for and at any temperature. We calculated the contributions of three carbonic groups and the alkylamine functional groups at different temperatures (303.15, 323.15, and 343.15 K). Having the contributions of the constituent groups to the MLIR parameters along with dependencies of the LIR parameters to system composition, the values of and for each alkylamine may be calculated using the following expressions: where could be obtained using (8). Then we may use the values calculated for and parameters at the temperature of interest along with (3) for a given alkylamine to obtain its density at different pressures. Some of the calculated results are given in Table 3.

The statistical parameter, namely, the absolute average percent deviation (AAD), the maximum deviation , and the average deviation (bias) are defined as where is the number of experimental data, and and are the experimental density values and those obtained with (3) for studied fluids, respectively. Since the values of AAD can characterize the fact that the calculated values are more or less close to experimental data, it can be claimed that MLIR EOS can predict the density of these organic compounds with good accuracy at any temperatures and pressures.
3.3. Extension to Mixture of Alkylamines with Previously Studied Organic Compounds
The main purpose in this section is to investigate the accuracy of the MLIREOS for mixtures of the alkylamines with previously studied organic compounds (alkanes, alcohols, ketones, and carboxylic acids). To do so, we may use the experimental data for binary mixture of butylamine with 1alkanols [24]. These binary mixtures, besides selfassociation, exhibit very strong cross association due to the strong hydrogen bonding between the hydroxyl and the amine groups. This strong intermolecular association exhibits relatively large negative excess volumes [24]. Again, we have found that the linearity of against for each isotherm of a binary mixture is as good as those for its pure compounds, see Figure 2. Note that, the average value of for a mixture may be defined as where and refer to the mole fraction and number of carbon atom of component in the mixture.
We may use the group contribution method to predict the MLIR parameters for mixtures. The contributions of different groups at the temperature of interest may be calculated from the same procedure explained in the previous sections (using the values for pure compounds). Having the contributions of constituent groups in the MLIR parameters along with the dependencies of the LIR parameters to system composition, the MLIR parameters for a mixture may be calculated from the following expressions: where and are the contribution of group in and and is its mole fraction, in the hypothetical mixture. Note that may be calculated from the following expression: where and are the mole fraction and number of carbon atoms of component in the mixture and is the number of group in component .
Using the calculated values of and parameters along with (3), the density of a mixture at any pressure, temperature, and mole fraction may be calculated. We have used this approach to calculate the density of binary mixture of butylamine with ethanol, 1propanol, and 1butanol at different pressures and mole fractions. Some of the results are given in Table 4. An ispection of Table 4 indicates that the strength of the intermolecular hydrogen bonding (between the hydroxyl and the amine groups) is an important factor influencing the predicted density deviation of these mixtures. The deviation is remarkable for the mixture with ethanol and decreases, as the chain length of the alkanol molecule increases. This result corresponds to heats of mixing study in these systems. Measured heats of mixing values at 298.15 K are −2915, −2870, and −2705 J·mol^{−1} for the mixtures of butylamine with ethanol, 1propanol, and 1butanol, respectively [24].

3.4. Calculation of Other Properties
Having an accurate EOS, the MLIR for different chemicals, we may expect to make use of it to calculate other properties such as isothermal compressibility compressibility () and thermal expansion coefficient () via the GCM. We may use the calculated values of and parameters along with an appropriate derivative of pressure to obtain these properties at any thermodynamic state. For instance, the isothermal compressibility may be calculated using the following expression:
We have calculated and parameters for pentylamine, hexylamine, heptylamine, 2aminobutane and 2aminooctane, at different temperatures: 303.15, 323.15, and 343.15 K as explained before along with (16) to calculate for these compounds at different pressures, see Table 5. The average percentage error for was found to be less than 2.11.

4. Conclusions
In this work the MLIR equation of state is extended to primary alkylamines by group contribution method. To do so, the linearity of against was investigated for aliphatic esters. Experimental data for different aliphatic esters were used to check the linearity of against for different isotherms (Figure 1). As shown in this figure, the linearity holds quite well with the correlation coefficient, , for these fluids over a wide range of temperatures and pressures. The temperature dependencies of the intercept and slope parameters of MLIREOS were also determined for these fluids.
In order to predict the MLIR parameters for primary alkylamines via the group contribution method, we had to use appropriate compounds to obtain the contribution of primary alkylamine functional groups in the MLIR parameters. Three basic compounds, namely, propane, butane, and cyclohexane were used to obtain the contribution of methyl and methylene groups and 1pentylamine and 2aminopentane for contribution of , groups in the MLIR parameters. Having the contribution of constituent groups to the EOS parameters along with dependencies of the LIR parameters to system composition, the MLIR parameters for each compound were calculated. Using the calculated EOS parameters along with the MLIR, the densities of these series of compounds were calculated at different pressures and temperatures, with the average percentage error less less than 1.54 (Table 3). Furthermore, we have used the group contribution method to predict the MLIR parameters for mixtures (14). Using the calculated values of and parameters along with (3), the density of mixtures at any pressure, temperature, and mole fraction may be calculated (Table 4). Thus, using the parameters of (5) for the basic compounds, we may calculate the density of pure or mixed fluids even for temperatures for which experimental data of basic compounds are not available.
We may use the calculated values of and parameters along with an appropriate pressure derivative, in order to obtain other properties at any thermodynamic state. The isothermal compressibility at different pressures was calculated for different alkylamines and compared with the literature values (Table 5).
Acknowledgment
Financial support for this work by the research affair of Shahrood University of Technology, Shahrood, Iran, is gratefully acknowledged.
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