Development of Optimal ANN Model to Estimate the Thermal Performance of Roughened Solar Air Heater Using Two different Learning Algorithms

In the present study, artificial neural network (ANN) model has been developed with two different training algorithms to predict the thermal efficiency of wire rib roughened solar air heater. Total 50 sets of data have been taken from experiments with three different types of absorber plate. The experimental data and calculated values of collector efficiency were used to develop ANN model. Scaled conjugate gradient (SCG) and Levenberg–Marquardt (LM) learning algorithms were used. It has been found that TRAINLM with 6 neurons and TRAINSCG with 7 neurons is optimal model on the basis of statistical error analysis. The performance of both the models have been compared with actual data and found that TRAINLM performs better than TRAINSCG. The value of coefficient of determination $$(\hbox {R}^{2})$$(R2) for LM-6 is 0.99882 which gives the satisfactory performance. Learning algorithm with LM based proposed MLP ANN model seems more reliable for predicting performance of solar air heater.

[1]  B. N. Prasad.  Thermal performance of artificially roughened solar air heaters , 2013 .

[2]  Unal Akdag,et al.  Prediction of heat transfer on a flat plate subjected to a transversely pulsating jet using artificial neural networks , 2016 .

[3]  Radha Krishna Prasad,et al.  Prediction of Thermal Performance of Unidirectional Flow Porous Bed Solar Air Heater with Optimal Training Function Using Artificial Neural Network , 2017 .

[4]  Ranjit Singh,et al.  A review on methodology of artificial roughness used in duct of solar air heaters , 2010 .

[5]  Ali Etem Gürel,et al.  The artificial neural network model to estimate the photovoltaic modul efficiency for all regions of the Turkey , 2014 .

[6]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[7]  Soteris A. Kalogirou,et al.  Prediction of flat-plate collector performance parameters using artificial neural networks , 2006 .

[8]  Manish Mishra,et al.  Performance prediction of rotary solid desiccant dehumidifier in hybrid air-conditioning system using artificial neural network , 2016 .

[9]  Engin Gedik,et al.  Investigation on thermal performance calculation of two type solar air collectors using artificial neural network , 2011, Expert Syst. Appl..

[10]  Myoung-Souk Yeo,et al.  Application of artificial neural network to predict the optimal start time for heating system in building , 2003 .

[11]  H. Kurt,et al.  Thermal performance parameters estimation of hot box type solar cooker by using artificial neural network , 2008 .

[12]  Sanjay K. Sharma,et al.  Thermo-hydraulic performance analysis of solar air heaters having artificial roughness–A review , 2015 .

[13]  Soteris A. Kalogirou,et al.  Applications of artificial neural-networks for energy systems , 2000 .

[14]  H. Benli.  Determination of thermal performance calculation of two different types solar air collectors with the use of artificial neural networks , 2013 .

[15]  B. N. Prasad,et al.  Fluid flow and heat transfer analysis for heat transfer enhancement in three sided artificially roughened solar air heater , 2014 .

[16]  Murat Hosoz,et al.  Artificial neural network analysis of a refrigeration system with an evaporative condenser , 2006 .

[17]  Adnan Sözen,et al.  Determination of efficiency of flat-plate solar collectors using neural network approach , 2008, Expert Syst. Appl..

[18]  W. Beckman,et al.  Solar Engineering of Thermal Processes , 1985 .

[19]  Hikmet Esen,et al.  Modelling and experimental performance analysis of solar-assisted ground source heat pump system , 2017, J. Exp. Theor. Artif. Intell..

[20]  Anil Kumar Patil,et al.  Heat transfer mechanism and energy efficiency of artificially roughened solar air heaters—A review , 2015 .