A Relationship Between Chemical Structure and the Critical Temperature

Fedors, R. F.

doi:10.1080/00986448208911092

Cited by 88 publications

(54 citation statements)

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“…The Lydersen and Ambrose techniques are the most accurate and group contributions for these are shown in Tables 2 and 3. The Fedors (1982) method for critical temperature requires no boiling-point temperature. As this may be a real advantage in some instances, his group contributions are given in Table 4.…”

Section: Review Of Group Contribution Methods For T Pc Vcmentioning

confidence: 99%

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Estimation of critical properties with group contribution methods

1984

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Currently available group-contribution methods for T,, P,, and V, were evaluated using the Ambrose (1980) critical property data compilation. The Ambrose estimation methods were found to be the most accurate. Linear regression methods were also employed to develop alternate estimation methods which were found to have an accuracy comparable to those of Ambrose.K. M. KLINCEWICZ SCOPECritical properties (T,, P,, V, ) are often required when using generalized property estimation methods. Due to the paucity of experimental values, one must often estimate these properties, and a group-contribution approach is generally used. The method developed by Lydersen (1955) is the most widely employed. However, many new experimental critical properties have become available since the Lydersen report and several alternate estimation schemes have been proposed. In the first portion of this paper, a few methods have been briefly reviewed and evaluated for accuracy using the currently available data.With the rapid advances in linear regression programming, it was felt to be of value to use this methodology to optimize the coefficients and group contributions utilizing a number of objective functions involving T,, P,, or V,. In this manner, one can rapidly test a number of objective functions, selected group-contribution substructures, auxiliary variables, etc. to obtain the least error method of estimation. CONCLUSIONS AND SIGNIFICANCEEvaluating all available estimation methods for T,, P,, and V, , it was shown that the methods of Ambrose (1979Ambrose ( , 1980b were the most accurate. These methods employ Tb/( T, -Tb), (MW/Pc)l12, and V, as objective functions (Q) with the equation forms given in Table 1 and the necessary group contributions in Table 3. The statistics relating to expected errors are shown in Table 5.Linear regression techniques also provided group estimation methods close in accuracy to those of Ambrose but with a simpler set of structural subgroups. In this type of analysis, multicollinearity was identified as a serious problem and the final choice of subgroups minimized the difficulties encountered.The new proposed estimation equations are given as Eqs. 10 through 12 with group contributions in Table 6. Expected errors are shown in Table 7.Models not involving the concept of group contributions were able to explain a large portion of the variance in the critical property data. Reasonably accurate estimation methods could be found where T,, (MW/P,)112, and V, were correlated only with Tb, molecular weight and the number of atoms in the compound. These preliminary results suggest that factor analysis may provide a viable alternate route to group-contributions in the estimation of critical properties.The properties of one material are often related to properties of another by assuming the respective intermolecular potentials of both are similar if the energy and separation variables are scaled appropriately. Materials that behave in this manner are called conformal substances (Rowlinson, 1969). Utilizing relation...

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Section: Review Of Group Contribution Methods For T Pc Vcmentioning

confidence: 99%

“…If one is not fortunate enough to find reliable experimental values (Gambill, 1959) T i / < Fedors (1982) 10Tc/5%…”

Section: [(Pipc)m (T/tc)m (V/vc)mi = 3 [ ( P / P C ) / 3 (T/tc)p mentioning

confidence: 99%

Estimation of critical properties with group contribution methods

1984

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“…Moreover, the application of some models is limited to specific pure compounds. [2,23] Thus, this comparison is not fair, even if the accuracies of a proposed model for predicting some properties are less than others. To obtain a fair comparison, NN (neural networks) models implemented by K. H. Lee, J. Y. Jung and I.…”

Section: Comparison With the Existing Methodsmentioning

confidence: 99%

“…Lydersen [18] 1.4 8.9 3.1 Ambrose [19,20] 0.7 4.6 2.8 Klincesicz and Reid [21] 1.3 7.8 2.9 Joback and Reid [22] 0.8 5.2 2.3 Fedors [23] 5.0 -3.15 Constantinou, L. and Gani, R [2] 0 …”

Section: Properties Of Isomersmentioning

confidence: 99%

An Advanced Group Contribution Method for High‐Dimensional, Sparse Data Sets

Lee

2011

Molecular Informatics

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Today's chemical processes involve many components, and it is necessary to know their basic physical properties for process design and operation. However, it is not always possible to find the property information of all components in the literature. Generally, there are two ways to evaluate properties of chemical compounds when they do not exist in the literature: the experimental measurement and predictive approaches based on empirical models. The latter is called the group contribution method (GCM), and its basic concept is that specific functional groups or fragments of a molecule contribute to the value of its physical property. The advantage of the GCMs is that they reduce the effort and cost compared to experiments. This study proposes a novel GCM method suitable for high-dimensional, sparse data sets. In order to improve its applicability and accuracy, the database is extended and divided into non-ring group compounds and ring group ones. Support vector regression (SVR) is adopted as the regression model, and a derivative-free optimization approach, referred to as particle swarm optimization, is incorporated into the parameter optimization step in learning the SVM model to avoid local optimality. Performance of the proposed model is compared to those of other GCMs.

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“…The method of Fedors [6] for calculating the critical temperature is completely predictive in nature and does not require the normal boiling point; however, it lacks the accuracy of other methods. Eq.…”

Section: Introductionmentioning

confidence: 99%

Simple Neural Network Models for Prediction of Physical Properties of Organic Compounds

Bhagwat¹

2002

Chem. Eng. Technol.

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A Relationship Between Chemical Structure and the Critical Temperature

Cited by 88 publications

References 1 publication

Estimation of critical properties with group contribution methods

Estimation of critical properties with group contribution methods

An Advanced Group Contribution Method for High‐Dimensional, Sparse Data Sets

Simple Neural Network Models for Prediction of Physical Properties of Organic Compounds

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