OPEN ACCESS
In present work, Artificial Neural Network (ANN) model has been structured to predict the exergetic efficiency of roughened solar air heater. Arc shaped wire rib roughened absorber plate with relative roughness pitch (P/e) =10, relative roughness height (e/D) =0.0395 and angle of attack (α) = 60o is used in the experiments. Experiments were conducted at Jamshedpur (India) under the local weather conditions for 7 days between 09:00 and 16:00 h in the month of February. Total 105 data samples have been collected from the experiments. By the use of experimental data and calculated values of exergy efficiency were used to develop ANN model. The MLP model has been developed with eight parameters in input layer and one parameter in output layer. Levenberg-Marquardt (LM) learning algorithm has been used to training an ANN model. For optimal topology, 10-16 neurons were used to train the network and found that 14 numbers of neurons with single hidden layer is optimal on the basis of statistical error analysis. The values of R2 for predicted exergetic efficiency is 0.99583 which shows that predicted values are very close to experimental data. The statistical analysis also show that the value of RMSE and COV were 0.021747 and 1.63766 respectively, which are very low as required for accurate prediction. The statistical results show that the proposed MLP model successfully predicts the exergetic performance of roughened solar air heater.
solar air heater, artificial neural network, exergy efficiency, learning algorithm, multi-layer perceptron
The Authors are very thankful to National Institute of Technology, Jamshedpur, India for providing all the facilities to carry out the research work.
[1] Duffie JA, Beckman WA. (1991). Solar Engineering of Thermal Processes. second ed.,Wiley Publication, New York.
[2] Garg HP, Prakash J. (2000). Solar Energy Fundamentals and Applications. First ed., Tata McGraw-Hill, New Delhi.
[3] Prasad BN, Behura AK, Prasad L. (2014). Fluid flow and heat transfer analysis for heat transfer enhancement in three sided artificially roughened solar air heater. Sol. Energy 105: 27-35. https://doi.org/10.1016/j.solener.2014.03.027
[4] Sharma SK, Kalamkar VR. (2015). Thermo-hydraulic performance analysis of solar air heaters having artificial roughness–A review. Renewable and Sustainable Energy Reviews 41: 413–435. https://doi.org/10.1016/j.rser.2014.08.051
[5] Sahu MK, Prasad RK. (2016). A review of the thermal and thermo hydraulic performance of solar air heater with roughened absorber plate. Journal of Enhanced Heat Transfer 23(1): 47-89. https://doi.org/10.1615/JEnhHeatTransf.2017015624
[6] Sahu MK, Prasad RK. (2017). Thermo hydraulic performance analysis of an arc shape wire roughened solar air heater. Renewable Energy 108: 598-614. https://doi.org/10.1016/j.renene.2017.02.075
[7] Cengel YA, Boles MA. (2006). Thermodynamics: An engineering approach. 5th ed. New York: McGraw-Hill. https://www.mheducation.co.in
[8] Alta D, Bilgili E, Ertekin C, Yaldiz O. (2010). Experimental investigation of three different solar air heaters: Energy and exergy analyses. Applied Energy 87: 2953–2973. https://doi.org/10.1016/j.apenergy.2010.04.016
[9] Lalji MK, Sarvaiya RM, Bhagoriya JL. (2012). Exergy evaluation of packed bed solar air heater. Renewable and Sustainable Energy Reviews 16: 6262-6267. https://doi.org/10.1016/j.rser.2012.04.024
[10] Sahu MK, Prasad RK. (2016). Exergy based performance evaluation of solar air heater with arc-shaped wire roughened absorber plate. Renewable Energy 96: 233-243. https://doi.org/10.1016/j.renene.2016.04.083
[11] Haykin S. (1994). Neural Networks: A Comprehensive Foundation. New Jersey: Prentice- Hall, New Jersey.
[12] Kalogirou SA. (2000). Applications of artificial neural-networks for energy systems. Applied Energy 67(1-2): 17–35. https://doi.org/10.1016/S0306-2619(00)00005-2
[13] Kalogirou SA. (2006). Prediction of flat-plate collector performance parameters using artificial neural networks. Solar Energy 80: 248–259. https://doi.org/10.1016/j.solener.2005.03.003
[14] Sozen A, Menlik T, Unvar S. (2008). Determination of efficiency of flat-plate solar collectors using neural network approach. Expert Syst. Appl. 35(4): 1533–1539. https://doi.org/10.1016/j.eswa.2007.08.080
[15] Caner M, Gedik E, Kecebas A. (2011). Investigation on thermal performance calculation of two type solar air collectors using artificial neural network. Expert Syst. Appl. 38(3): 1668–1674. https://doi.org/10.1016/j.eswa.2010.07.090
[16] Benli H. (2013). Determination of thermal performance calculation of two different types solar air collectors with the use of artificial neural networks. Int. J. of Heat and Mass Transfer 60: 1-7. https://doi.org/10.1016/j.ijheatmasstransfer.2012.12.042
[17] Ghritlahre HK, Prasad RK. (2017). Prediction of thermal performance of unidirectional flow porous bed solar air heater with optimal training function using Artificial Neural Network. Energy Procedia 109: 369-376. https://doi.org/10.1016/j.egypro.2017.03.033
[18] Ghritlahre HK, Prasad RK. (2017). Energetic and exergetic performance prediction of roughened solar air heater using artificial neural network. Ciência e Técnica Vitivinícola 32(11): 2-24. http://ciencia-e-tecnica.org/view.php?v=32&i=11
[19] Ghritlahre HK, Prasad RK. (2018). Application of ANN technique to predict the performance of solar collector systems - A review. Renewable and Sustainable Energy Reviews 84: 75–88. https://doi.org/10.1016/j.rser.2018.01.001
[20] Ghritlahre HK, Prasad RK. (2018). Exergetic performance prediction of roughened solar air heater using artificial neural network. Strojniški vestnik - Journal of Mechanical Engineering 64(3): 195-206. http://dx.doi.org/10.5545/sv-jme.2017.4575
[21] Ghritlahre HK, Prasad RK. (2018). Development of optimal ANN model to estimate the thermal performance of roughened solar air heater using two different learning algorithms. Annals of Data Science 5(3): 453–467. https://doi.org/10.1007/s40745-018-0146-3
[22] Ghritlahre HK, Prasad RK. (2018). Exergetic performance prediction of solar air heater using MLP, GRNN and RBF models of artificial neural network technique. Journal of Environmental Management 223: 566-575. https://doi.org/10.1016/j.jenvman.2018.06.033
[23] Ghritlahre HK, Prasad RK. (2018). Investigation on heat transfer characteristics of roughened solar air heater using ANN technique. International Journal of Heat and Technology 36(1): 102-110. https://doi.org/10.18280/ijht.360114
[24] Ghritlahre HK. (2018). Development of feed-forward back-propagation neural model to predict the energy and exergy analysis of solar air heater. Trends in Renewable Energy 4(2): 213-235. https://doi.org/10.17737/tre.2018.4.2.0078
[25] Ghritlahre HK, Prasad RK. (2018). Prediction of heat transfer of two different types of roughened solar air heater using Artificial Neural Network technique. Thermal Science and Engineering Progress. https://doi.org/10.1016/j.tsep.2018.08.014
[26] Ghritlahre HK, Prasad RK. (2018). Investigation of thermal performance of unidirectional flow porous bed solar air heater using MLP, GRNN, and RBF models of ANN technique. Thermal Science and Engineering Progress 6: 226-235. https://doi.org/10.1016/j.tsep.2018.04.006
[27] Wang RZ, Liu PD, Qian YM, Zhan J. (2017). Application and design of split solar water heater with conversion of solar energy into thermal energy through chemical methods. Chemical Engineering Transactions 62: 397-402. https://doi.org/10.3303/CET1762067
[28] Jumari NF, Yusof KM. (2017). Comparison of melt flow index of propylene polymerization in loop reactors using first principles and artificial neural network models. Chemical Engineering Transactions 56: 163-168. https://doi.org/10.3303/CET1756028