Alternative approach in thermal analysis of plate heat exchanger

This paper presents alternative approach in heat transfer analysis of plate heat exchangers. In order to obtain heat transfer rate and effectiveness values of plate heat exchanger, neural network (NN) approach was used. Experimentally, system used in plate heat exchanger for heating and cooling applications was designed and constructed. Experimental data were used for training and testing network. The training and validation were performed with good accuracy. The correlation coefficient obtained when unknown data were applied to the networks was 0.9994 for heat transfer rate and 0.9976 for effectiveness, which is very satisfactory. Using the weights obtained from the trained network, a new formulation is presented for determination of heat transfer rate and effectiveness. This formulation can provide simplicity in thermal analysis of plate heat exchanger. The presented procedure can also help to heat exchanger designer and manufacturer.

[1]  Xiang Ling,et al.  Optimal design approach for the plate-fin heat exchangers using neural networks cooperated with genetic algorithms , 2008 .

[2]  Mihir Sen,et al.  Dynamic prediction and control of heat exchangers using artificial neural networks , 2001 .

[3]  Yasar Islamoglu,et al.  A new approach for the prediction of the heat transfer rate of the wire-on-tube type heat exchanger––use of an artificial neural network model , 2003 .

[4]  Adnan Sözen,et al.  Modelling (using artificial neural-networks) the performance parameters of a solar-driven ejector-absorption cycle , 2004 .

[5]  Marcus Reppich.  Use of high performance plate heat exchangers in chemical and process industries , 1999 .

[6]  Kemal Ermis,et al.  ANN modeling of compact heat exchangers , 2008 .

[7]  S. C. Kaushik,et al.  Heating and cooling potential of an earth-to-air heat exchanger using artificial neural network , 2006 .

[8]  Qiuwang Wang,et al.  Heat transfer analysis for shell-and-tube heat exchangers with experimental data by artificial neural networks approach , 2007 .

[9]  Adnan Sözen,et al.  Performance analysis of ejector absorption heat pump using ozone safe fluid couple through artificial neural networks , 2004 .

[10]  Yasar Islamoglu,et al.  Performance prediction for non-adiabatic capillary tube suction line heat exchanger: an artificial neural network approach , 2005 .

[11]  Zhang Lin,et al.  Global optimization of absorption chiller system by genetic algorithm and neural network , 2002 .

[12]  Rodney L. McClain,et al.  Neural network analysis of fin-tube refrigerating heat exchanger with limited experimental data , 2001 .

[13]  Soteris A. Kalogirou,et al.  Thermodynamic analysis of absorption systems using artificial neural network , 2006 .

[14]  Frank P. Incropera,et al.  Fundamentals of Heat and Mass Transfer , 1981 .

[15]  Soteris A. Kalogirou,et al.  Optimization of solar systems using artificial neural-networks and genetic algorithms , 2004 .

[16]  Soteris A. Kalogirou.  Long-term performance prediction of forced circulation solar domestic water heating systems using artificial neural networks , 2000 .

[17]  Vojislav Kecman,et al.  New approach to dynamic modelling of vapour-compression liquid chillers: artificial neural networks , 2001 .

[18]  Arzu Şencan,et al.  Performance of ammonia–water refrigeration systems using artificial neural networks , 2007 .

[19]  S. Kalogirou,et al.  A new approach using artificial neural networks for determination of the thermodynamic properties of fluid couples , 2005 .

[20]  C Riverol,et al.  Estimation of fouling in a plate heat exchanger through the application of neural networks , 2005 .

[21]  Spiros V. Paras,et al.  Flow and Heat Transfer Prediction in a Corrugated Plate Heat Exchanger using a CFD Code , 2006 .