Neural networks boiling point

The method of Lydersen [28] is a GCM of this type to estimate the critical temperature, Tc. Other approaches to non-linear GCMs include the model of Lai et al. [29] for the boiling point, Tby and the ABC approach [30] to estimate a variety of thermodynamic properties. Further, artificial neural networks have been used to construct nonlinear models for the estimation of the normal boiling point of haloalkanes [31] and the boiling point, critical point, and acentric factor of diverse fluids [32]. 

Balaban, A. T., et al., Correlation Between Structure and Normal Boiling Points of Haloalkanes C1-C4 Using Neural Networks. J. Chem. Inf. Comput. Sci., 1994 34, 1118-1121. 

A neural network consists of many processing elements joined together. A typical network consists of a sequence of layers with full or random connections between successive layers. A minimum of two layers is required the input buffer where data is presented and the output layer where the results are held. However, most networks also include intermediate layers called hidden layers. An example of such an ANN network is one used for the indirect determination of the Reid vapor pressure (RVP) and the distillate boiling point (BP) on the basis of 9 operating variables and the past history of their relationships to the variables of interest (Figure 2.56). 

Cherqaoui, D., Villemin, D., Mesbah, A., Cense, J.-M. and Kvasnicka, V. (1994). Use of a Neural Network to Determine the Normal Boiling Points of Acyclic Ethers, Peroxides, Acetals and their Sulfur Analogues. J.Chem.Soc.Faraday Trans, 90,2015-2019. 

Egolf, L.M. and Jurs, P.C. (1993a). Prediction of Boiling Points of Organic Heterocyclic Compounds Using Regression and Neural Network Techniques. J.Chem.Inf.Comput.ScL, 33, 616-625. 

Goll, E.S. and Jurs, PC. (1999a). Prediction of the Normal Boiling Points of Organic Compounds from Molecular Structures with a Computational Neural Network Model. J.Chem.Inf.Com-put.Sci., 39,914-983. 

Hall, L.H. and Story, C.T. (1996). Boiling Point and Critical Temperature of a Heterogeneous Data Set QSAR with Atom Type Electrotopological State Indices Using Artificial Neural Networks. J.Chem.lnf.Comput.ScL, 36,1004-1014. 

Lohninger, H. (1993). Evaluation of Neural Networks Based on Radial Basis Functions and Their Application to the Prediction of Boiling Points from Structural Parameters. J. Chem. Inf. Comput.Sci.,33,736-744. 

Tetteh, J., Suzuki, T, Metcalfe, E. and Howells, S. (1999). Quantitative Structure-Property Relationships for the Estimation of Boiling Point and Flash Point Using a Radial Basis Function Neural Network. J.Chem.InfiComput.ScL, 39,491-507. 

Arupjyoti, S. and Iragavarapu, S. (1998) New electrotopological descriptor for prediction of boiling points of alkanes and aliphatic alcohols through artificial neural network and multiple linear regression analysis. Computers Chem., 22, 515-522. 

Goll, E.S. and Jurs, P.C. (1999a) Prediction of the normal boiling points of organic compounds from molecular structures with a computational neural network model./. Chem. Inf. Comput. Sci., 39,974— 983. 

Ivanciuc, O. (1998c) Artificial neural networks applications. Part 9. MolNet prediction of alkane boiling points. Rev. Roum. Chim., 43, 885-894. 

Li, Q., Chen, X. and Hu, Z. (2004) Quantitative structure-property relationship studies for estimating boiling points of alcohols using calculated molecular descriptors with radial basis function neural networks. Chemom. Intell. Lab. Syst., 72, 93-100. 

Lohninger, H. (1993) Fvaluation of neural networks based on radial basis functions and their application to the prediction of boiling points from structural parameters. I. Chem. Inf. Comput. Sci., 33, 736-744. 

Cherqaoui, D. and Villemin, D. (1994) Use of a neural network to determine the boiling point of alkanes. J. Chem. Soc. Faraday Trans. 90,97-102. 

Figure 3 Plot of observed vs. calculated normal boiling points for the 267-compound training set of hydrocarbons using a 6-5-1 computational neural network model found using genetic algorithm feature selection...

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