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

N. Austin, P. Senthilkumar, S. Purushothaman

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

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References


Arcaklıoglu E, Erisen A, Yılmaz R., Artificial neural network analysis

of heat pumps using refrigerant mixtures. Energy conversion and

management, 45, 2004,pp.1917–1929.

Arcaklıoglu E., Performance comparison of CFCs with their substitutes

using artificial neural networks. International Journal of Energy

Research, 28:12: 2004, pp.1113–1125.

Camporese R, Bigolaro G, Bobbo S, Cortella G., Experimental

evaluation of refrigerant mixtures as substitutes for CFC12 and R502.

International Journal of Refrigeration, 20,1997, pp. 22–31.

Chen.S and Billings, S.A, , Neural networks for nonlinear dynamic

system modeling and identification, International Journal of Control,

Vol.56, No. 22, 1992, pp. 319-349.

Cowan,C.F.N., Chen, S., Billings, S.A and Grant, P.M., Practical

Identification of NARMAX models using radial basis functions,

International Journal of Control, Vol. 52, 1990, pp. 1327-1350.

Didion DA, Bivens DB., Role of refrigerant mixtures as alternatives to

CFCs. International Journal of Refrigeration,13, 1990, pp.163–75.

Erol Arcaklıoglu, AbdullahCavusoglu, Ali Erisen, , Thermodynamic

analyses of refrigerant mixtures using artificial neural networks,Applied

Energy, 78, 2004, pp.219–230.

Grant P.M.., Chen. S, and Billings.S.A,, , Recursive hybrid algorithm for

nonlinear system identification using radial basis function networks,

International Journal of Control, Vol.55, No.5, 1992, pp. 1051-1070.

Gunther D, Steimle F., Mixing rules for the specific heat capacities of

several HFC-mixtures. International Journal of Refrigeration, 20, 1997,

pp.235–43.

McMullan JT., Refrigeration and the environment issues and strategies

for the future. International Journal of Refrigeration, 25, 2002, pp.89–99.

Olofsson T, Andersson S., Long-term energy predictions based on shortterm

measured data. Energy and Buildings, 33, 2001, pp.85–91.

Pacheco-Vega A, Sen M, Yang KT, McClain RL., Neural network

analysis of fin-tube refrigerating heat-exchanger with limited

experimental data. International Journal of Heat Mass and Transfer;44:

, pp.763–70.

Richardson R, Butterworth J., The performance of propane/isobutane in

a vapor-compression refrigeration system. International Journal of

Refrigeration,18, 1995, pp.58–62.

Sharma R, Singhal D, Ghosh R, Dwivedi A., Potential applications of

artificial neural networks to thermodynamics: vapor-liquid equilibrium

predictions. Computers and Chemical Engineering, 23, 1999, pp.385–90.

Sozen A, Arcaklıoglu E, Ozalp M. A new approach to thermodynamic

analysis of ejector–absorption cycle:artificial neural-networks. Applied

Thermal Engineering;23, 2003, pp.937–52.

Venkatarathnam Gadhiraju, Klaus D. Timmerhaus and Carlo Rizzuto,

,Cryogenic Mixed Refrigerant Processes, Springer, 2008.


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