Share

IIR document

A neural network for predicting normal boiling point of pure refrigerants using molecular groups and a topological index.

Author(s) : DENG S., SU W., ZHAO L.

Type of article: Article, IJR article

Summary

An artificial neuron network based on genetic algorithm is presented to predict the normal boiling point (Tb) of refrigerants from 16 molecular groups and a topological index. The 16 molecular groups used in this paper can cover most refrigerants or working fluids in refrigeration, heat pump and organic Rankine cycle; the chosen topological index is able to distinguish all the refrigerant isomers. A total of 334 data points from previous experiments are used to create this network. The calculated results, which are based on a developed numerical method, show a good agreement with experimental data; the average absolute deviations for training, validation and test sets are 1.83%, 1.77%, 2.13%, respectively. A performance comparison between the developed numerical model and the other two existing models, namely QSPR approach and UNIFAC group contribution method, shows that the proposed model can predict Tb of refrigerants in a better accord with experimental data.

Available documents

Format PDF

Pages: 63-71

Available

  • Public price

    20 €

  • Member price*

    Free

* Best rate depending on membership category (see the detailed benefits of individual and corporate memberships).

Details

  • Original title: A neural network for predicting normal boiling point of pure refrigerants using molecular groups and a topological index.
  • Record ID : 30016727
  • Languages: English
  • Source: International Journal of Refrigeration - Revue Internationale du Froid - vol. 63
  • Publication date: 2016/03

Links


See other articles in this issue (19)
See the source

Indexing