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A neural network for predicting saturated liquid density using genetic algorithm for pure and mixed refrigerants.

Author(s) : MOHEBBI A., TAHERI M., SOLTANI A.

Type of article: Article, IJR article

Summary

In this study, a new approach for the auto-design of a neural network based on genetic algorithm (GA) has been used to predict saturated liquid density for 19 pure and 6 mixed refrigerants. The experimental data including Pitzer's acentric factor, reduced temperature and reduced saturated liquid density have been used to create a GA-ANN model. The results from the model are compared with the experimental data, Hankinson and Thomson and Riedel methods, and Spencer and Danner modification of Rackett methods. GA-ANN model is the best for the prediction of liquid density with an average of absolute percent deviation of 1.46 and 3.53 for 14 pure and 6 mixed refrigerants, respectively.

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Pages: 1317-1327

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Details

  • Original title: A neural network for predicting saturated liquid density using genetic algorithm for pure and mixed refrigerants.
  • Record ID : 2009-0065
  • Languages: English
  • Source: International Journal of Refrigeration - Revue Internationale du Froid - vol. 31 - n. 8
  • Publication date: 2008/12

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