Artificial Intelligent Systems and Machine Learning

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Implementation of Mixed Refrigerants Suitability by Using Radial Basis Function Neural Network


Affiliations
1 Satyabama University, India
2 Department of Mechanical Engineering, KSR College of Engineering, Tiruchengode, India
3 Udaya School of Engineering, Kanyakumari District-629204, India
     

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This paper presents implementation of Radial basis function (RBF) neural network to find out mixture of Hydrofluorocarbon (HFC) and Hydrocarbon (HC) for obtaining higher Coefficients of Performances (COPs). The thermodynamic properties of refrigerants are obtained using REFPROP 9 software that contains details of refrigerants. Different combinations of the refrigerants along with their COPs are obtained by the REFPROP 9. It consumes time in obtaining the correct combination of refrigerants as lot of menu options have to be chosen in the REFPROP 9. In order to make the process of finding out the correct mixed refrigerants with less manual intervention, RBF is trained and tested with the patterns of mixed refrigerants. The RBF mixed refrigerant analysis software has been developed by using MATLAB 10.

Keywords

Radial Basis Function, Artificial Neural Network, Mixed Refrigerant, Coefficient of Performance.
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  • Implementation of Mixed Refrigerants Suitability by Using Radial Basis Function Neural Network

Abstract Views: 240  |  PDF Views: 3

Authors

N. Austin
Satyabama University, India
P. Senthilkumar
Department of Mechanical Engineering, KSR College of Engineering, Tiruchengode, India
S. Purushothaman
Udaya School of Engineering, Kanyakumari District-629204, India

Abstract


This paper presents implementation of Radial basis function (RBF) neural network to find out mixture of Hydrofluorocarbon (HFC) and Hydrocarbon (HC) for obtaining higher Coefficients of Performances (COPs). The thermodynamic properties of refrigerants are obtained using REFPROP 9 software that contains details of refrigerants. Different combinations of the refrigerants along with their COPs are obtained by the REFPROP 9. It consumes time in obtaining the correct combination of refrigerants as lot of menu options have to be chosen in the REFPROP 9. In order to make the process of finding out the correct mixed refrigerants with less manual intervention, RBF is trained and tested with the patterns of mixed refrigerants. The RBF mixed refrigerant analysis software has been developed by using MATLAB 10.

Keywords


Radial Basis Function, Artificial Neural Network, Mixed Refrigerant, Coefficient of Performance.