Initially, a literature review was conducted on available methods for predicting normal boiling points based on molecular structure alone. Even in the failure probability analysis, only 3% of the data were larger than 20K for the proposed method.
Subscripts
Superscripts
Introduction
In summary, current methods cannot provide a simple and accurate estimate of the normal boiling point in all chemical classes. The main objective of this work is to develop a reliable group contribution estimation method for predicting the normal boiling points of non-electrolyte organic compounds.
Literature Review
Introduction
- Marrero and Gani (2001)
The contributions of the group interactions for equation 2-3 (Tb-) and equation 2-4 (Tb) are shown in Table A-4. The method of Cordes and Raley (2002) suggested a new approach for boiling point prediction.
Theoretical Considerations
Inductive and Resonance Effect
We can see that the group neighbor effect is an important factor in boiling point prediction. The actual difference in boiling point is due to the change in dipole moment discussed earlier.
Chapter Four
Mathematical and Software Considerations
Development of Regression Algorithm
- Non-linear Regression
- Description of the Simplex Method
- Reflection
- Expansion
- Contraction
However, it is not very efficient in terms of the number of function evaluations it requires. Xm is further defined as the center of the points with i :I h and defined [Xi Xj] as the distance between Xi and Xj. The model for the proposed least-squares fitting method is shown in Equation 4-8, where M is the number of structural groups, including second-order corrections.
The outer loop 'DSIM' then solves for the non-linear constants, which requires the evaluation of the function (Yi). The 'AUX' block obtains the objective function and also prepares the function (Yi) for the minimum.
Automatic Fragmentation Procedure
- Ink File
LINRE £;
DSIM
The sixth item is whether the item should be included as part of the group definition. The term 'Nein' is generally used to describe the neighborhood of the group, but is not fragmented as part of the group. However, it has many limitations, especially when introducing, extending or modifying different structural groups of the method.
The object only asks for the component's DDB number, property and method names, and then the normal boiling point is evaluated. This construct is used for the purpose of developing and modifying structural groups.
Development of the Method
Introduction
Data Verification
- Hydrocarbons
In the case of the private DDB, these components are fragmented (Section 4.2) and are entered into the worksheet together with the normal boiling points. With the implementation of these improvements, the proposed method was then developed, which involved the construction of the new method (Section 4.4). Thus in some cases the development of the proposed method involving mono-functional compounds has a slightly poorer estimate than the available methods.
For these types of methods, the predictive ability of the method is now uncertain. The analysis of the regression results led to the introduction of steric hindrance and isomer correction.
Neighbours
The reason for this high deviation is in the classification of the group in the Cordes and Rarey method, since all 7 components were represented by one group. With the exception of the Cordes and Rarey method, the available methods had extremely high deviations for these types of compounds. Deviations of different types of functional groups, along with their ID numbers in parentheses, are shown in Table 5-8.
The probable reasons for these deviations are due to the intermolecular interactions between the strongly associated alcohol groups (Figure 5-5). Unlike monofunctional compounds, the effect of group interaction decreases with the size of the molecule.
0 III
This model has been modified to include first the number of atoms (Equations 5–9) and then both contributions (Equations 5–10). The model has thus been modified to exclude the density, with the inclusion of the number of atoms (equations 5-11). For example, an increase in the number of atoms gives an increase in the normal boiling point, consider Equation 3-11.
Applying the second set of contributions, the first approach was based on the number of atoms of each functional group (Equation 5-19). Following this approach, it was decided that the second group would also be based on the exponent of the number of atoms (Equation 5-20) and the summation of group contributions (Equation 5-21).
Chapter Six
Results and Discussion
Hydrocarbon Compounds .1 Mono-functional Hydrocarbons
- Steric and Isomer Correction
- Carbonyl Compounds
The Marrero and Gani method produces an extremely high mean deviation for the estimation of n-alkanes. For the case of monofunctional compounds, there were no high deviations for these compounds (>20K). In general, there were relatively high deviations for these compounds (> . 12 K) for the proposed method.
The only exception is the Marrero and Pardillo method for estimating primary amines. For the estimation of monofunctional compounds, deviations for the proposed method are among the lowest obtained.
Other Elemental Compounds
The model also produced one of the lowest deviations with only three non-linear parameters. The same model was then tested with the molecular weight instead of the number of atoms (Equation 5-3) and produced a slightly higher mean deviation. The model involving the molecular weight as part of the numerator (Equations 5-12) also produced a similar deviation to the previous model.
In other words, inclusion of molecular weight as a linear relationship to normal boiling point did not show any improvement. For the case of fitting the second set of contributions instead of the number of atoms, this produced negative contributions for some groups.
Overall analysis
The results presented in this chapter have demonstrated all the objectives set for the development of the group contribution method for predicting normal boiling points. By the procedure given in this work, these limits have already been established, which includes in particular the components with extrema in the dipole moment and the first few components in the series. For a more sophisticated estimate, the dipole moment can be obtained from a molecular mechanics calculation, which will provide a solution to the weak points mentioned in this chapter.
However, this problem can be captured by including a molecular property such as molecular surface area. Thus, reliability is now even more pronounced with the expectation that, within the constraints of group contribution, estimates can be made with confidence.
Conclusion
These groups are designed for multifunctional components with more than one strongly bonded structural group. Overall, the proposed method proved to be the most accurate group contribution method compared to previous methods and with the widest range of applicability. The reliability of the proposed model is quite good, relatively few examples of components with extremely large deviations are observed.
On a test set of 405 components common to all methods except the Marrero and Pardillo methods (in this case 212 components), the proposed method yielded an average absolute deviation of 4.68K (19.04K for the Joback and Reid method, 7.67K for Stein and Brown, 12.09K for Marrero and Gani, 10.74 for Marrero and Pardillo and 6.30K for Cordes and Raley). This implies that the proposed method yields the lowest average deviation with the widest applicability.
Recommendations
Constantinou L., Gani R., "A New Group Contribution Method for Estimating Properties of Pure Hydrocarbons," IVC-SEP 9319, Institut vir Kemiteknik, The Technical Univ. Constantinou L., Prickett S.E., Mavrovouniotis M.L., "Estimation of thermodynamic and physical properties of acyclic hydrocarbons using the ABC approach and conjugation operators," Ind. Retzekas E., Voutsas E., MagoulasK., Tassios D., "Prediction of physical properties of hydrocarbons, petroleum and coal liquid fractions," Ind.
H., "Group Contribution Methods for Predicting the Melting Points and Boiling Points of Aromatic Compounds," Ind. Vetere A., “Methods for predicting the enthalpies of vaporization at the normal boiling temperature of pure compounds revisited,” Liquid Phase Equilibria.
Previous Group Contributions
Interactions with the >CH- ring (via a single Interactions with the >C< ring (via . bond) single bond). Interactions with -NH2 (via single Interactions with non-ring -5- (via . bond) single bond). a - non-aromatic, p - aromatic, c - interaction via carbon, 0 - interaction via oxygen, r - ring, rr - interaction of a group in the second ring).
Group Definitions
CI-Cl- connected to C or Si not Cl-(C,Si)25-butyl chloride, already substituted with F 72 2-chloroethanol, or Cl. OH -OH for aliphatic chains -OH short chain 36 ethanol, with less than five C =C< conjugated double bond in >C=C. C=C< conjugated double bond in >C=C. gt;Si< associated with at least >Si«O) 71 hexamethyl disiloxane .. gt;Si< associated with at least >Si«F,Cl) 78 trichlorosilane,. C-[F,Clh Carbon with three halogens 121 l,l,l-trifluorotoluene (Ch-C-[F,Clh Secondary carbon with two halogens 122 2,2-dichloropropane No hydrogen component has no hydrogen 123 perfluoro compounds One hydrogen component has one hydrogen 124 nonafluorobutane 3/4 ring Three- or four-membered non-aromatic ring 125 cyclobutene 5 ring five-membered non-aromatic ring 126 cyclopentane Ortho pair(s) Ortho position - counted only once and 127 o-xylene. Group Group Average absolute Average absolute Standard Number of contribution (K) error (%) error (K) deviation (K) components. This routine must be run before the group interaction metalanguage, as it depends on the number of atoms in the molecule. The custom views are then created ('Groups_on' and 'Groups_off'). 5.7) The normal boiling point estimate of the available group contribution methods for each component is then imported into the worksheet ('method_estimate'). This is done by a routine ('remov_bad_data' in the 'start' module). 8) The interaction language should then be run from the '£I_mf' worksheet. This can be easily done using the button "Regenerate the sum of column IT and IJ" in the "data" worksheet (Figure E2). 9) Initial values for regression (for non-linear parameters) must be set in line 6 (Figure El). You can now run the regression by pressing the 'run' button on the 'Control' worksheet.
Group Contributions
Normal Boiling Point File Reconstruction
