Share

IIR document

Computing thermodynamic properties of ammonia–water mixtures using artificial neural networks.

Author(s) : GOYAL A., GARIMELLA S.

Type of article: Article, IJR article

Summary

Artificial neural networks (ANN) provide a computationally efficient pathway for solving complex non-linear problems. Cascaded ANN are used to compute thermodynamic properties of the ammonia–water mixture working-pair commonly used in vapor absorption heat pumps. Thermodynamic property routines can affect the accuracy and pose a significant computational bottleneck for steady-state and transient cycle simulations of ammonia–water. The properties computed using the proposed method agree within 0.5% of equation of state data over a wide range of operating parameters. It is observed that the ANN based property routines, developed as explicit functions, offer ~60% decrease in computational time over currently used property routines. As a case study, the property calculation modules developed using ANN are employed in simulating the dynamic response of a representative ammonia-water condenser for a vapor absorption cycle. The models predict the transient behavior accurately with an ~80% computational speedup compared to conventional property routines.

Available documents

Format PDF

Pages: 315-325

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: Computing thermodynamic properties of ammonia–water mixtures using artificial neural networks.
  • Record ID : 30025639
  • Languages: English
  • Source: International Journal of Refrigeration - Revue Internationale du Froid - vol. 100
  • Publication date: 2019/04
  • DOI: http://dx.doi.org/10.1016/j.ijrefrig.2019.02.011

Links


See other articles in this issue (46)
See the source

Indexing