Modelling of back propagation neural network to predict the thermal performance of porous bed solar air heater

• Autorzy: Ghritlahre Harish Kumar , Prasad Radha Krishna

Identyfikatory

DOI

10.24425/ather.2019.131430

• Języki publikacji: EN

Abstrakty

EN

The objective of present work is to predict the thermal performance of wire screen porous bed solar air heater using artificial neural network (ANN) technique. This paper also describes the experimental study of porous bed solar air heaters (SAH). Analysis has been performed for two types of porous bed solar air heaters: unidirectional flow and cross flow. The actual experimental data for thermal efficiency of these solar air heaters have been used for developing ANN model and trained with Levenberg-Marquardt (LM) learning algorithm. For an optimal topology the number of neurons in hidden layer is found thirteen (LM-13).The actual experimental values of thermal efficiency of porous bed solar air heaters have been compared with the ANN predicted values. The value of coefficient of determination of proposed network is found as 0.9994 and 0.9964 for unidirectional flow and cross flow types of collector respectively at LM-13. For unidirectional flow SAH, the values of root mean square error, mean absolute error and mean relative percentage error are found to be 0.16359, 0.104235 and 0.24676 respectively, whereas, for cross flow SAH, these values are 0.27693, 0.03428, and 0.36213 respectively. It is concluded that the ANN can be used as an appropriate method for the prediction of thermal performance of porous bed solar air heaters.

Słowa kluczowe

EN

solar air heater; porous bed; thermal performance; artificial neural network; Levenberg-Marquardt algorithm

• Wydawca: Wydawnictwo Instytutu Maszyn Przepływowych PAN ; Komitet Termodynamiki i Spalania PAN
• Czasopismo: Archives of Thermodynamics
• Rocznik: 2019
• Tom: Vol. 40, no 4
• Strony: 103--128
• Opis fizyczny: Bibliogr. 40 poz., rys., tab., wykr., wz.

Twórcy

autor

Ghritlahre Harish Kumar

• Mechanical Engineering Department, National Institute of Technology, Jamshedpur, 831014, Jharkhand, India

autor

Prasad Radha Krishna

• Mechanical Engineering Department, National Institute of Technology, Jamshedpur, 831014, Jharkhand, India

Bibliografia

• [1] Duffie J.A., Beckman W.A.: Solar Engineering of Thermal Processes (2nd Edn.). Wiley, New York 1991.
• [2] Tiwari G.N.: Solar Energy: Fundamentals, Design, Modelling and Applications. Narosa, New Delhi 2004.
• [3] Karsli S.: Performance analysis of new-design solar air collectors for drying applications. Renew. Energy 32(2007) 1645–1660.
• [4] Prasad B.N., Behura A.K., Prasad L.: Fluid flow and heat transfer analysis for heat transfer enhancement in three sided artificially roughened solar air heater. Sol. Energy 105(2014), 27–35.
• [5] Behura A.K., Prasad B.N., Prasad L.: Heat transfer, friction factor and thermal performance of three sides artificially roughened solar air heaters. Sol. Energy 130(2016), 46–59.
• [6] Behura A.K., Rout S.K., Pandya H., Kumar A.: Thermal analysis of three sides artificially roughened solar air heaters. Energy Procedia 109(2017) 279–285.
• [7] Sharma S.P., Saini J.S., Varma H.K.: Thermal performance of packed bed solar air heaters. Sol. Energy 47(1991) 59–67.
• [8] Prasad R.K. Saini, J.S.: Comparative performance study of packed bed solar air heaters. In: Proc. 8th ISME Conf. on Mechanical Engineering, New Delhi, 1993, 190–197
• [9] Prasad R.K., Saini J.S.: Thermal performance characteristics of unidirectional flow porous bed solar energy collectors for heating air. PhD thesis, University of Roorkee, Roorkee 1993.
• [10] Ahmad A., Saini J.S., Varma H.K.: Effect of geometrical and thermophysical characteristics of bed materials on the enhancement of thermal performance of packed bed solar air heaters. Energy Conv. Mgmt. 36(1995), 1185–1195.
• [11] Varshney L., Saini J.S.: Heat transfer and friction factor correlations for rectangular solar air heater duct packed with wire mesh screen matrices. Sol. Energy 62(1998), 4, 255–262.
• [12] Thakur N.S., Saini J.S., Solanki S.C: Heat transfer and friction factor correlations for packed bed solar air heater for a low porosity system. Sol. Energy 74(2003), 319–329.
• [13] Mittal M.K., Varshney L.: Optimal thermohydraulic performance of a wire mesh packed solar air heater. Sol. Energy 80(2006), 1112–1120.
• [14] A.P. Omojaro, L.B.Y. Aldabbagh: Experimental performance of single and double pass solar air heater with fins and steel wire mesh as absorber. Appl. Energ. 87(2010), 3759–3765.
• [15] Kalogirou S.A.: Applications of artificial neural-networks for energy systems. Appl. Energ. 67(2000), 1-2, 17–35.
• [16] Kalogirou S.A., Bojic M.: Artificial neural networks for the prediction of the energy consumption of a passive solar building. Energy 25(2000), 479–491.
• [17] Yang I.H., Yeo M.S., Kim K.W.: Application of artificial neural network to predict the optimal start time for heating system in building. Energ. Convers. Manage. 44(2003), 2791–2809.
• [18] Facao J., Varga S., Oliveira A.C.: Evaluation of the Use of Artificial Neural Networks for the Simulation of Hybrid Solar Collectors. Int. J. Green Energy 1(2004), 3, 337–352.
• [19] Ertunc H.M., Hosoz M.: Artificial neural network analysis of a refrigeration system with an evaporative condenser. Appl. Therm. Eng. 26(2006), 627–635.
• [20] Kalogirou S.A.: Prediction of flat-plate collector performance parameters using artificial neural networks. Sol. Energy 80(2006), 248–259.
• [21] Yilmaz S., Atik K.: Modeling of a mechanical cooling system with variable cooling capacity by using artificial neural network. Appl. Therm. Eng. 27(2007), 2308–2313.
• [22] Sozen A., Menlik T., Unvar S.: Determination of efficiency of flat-plate solar collectors using neural network approach. Expert Syst. Appl. 35(2008), 4, 1533–1539.
• [23] Kurt H., Atik K., Ozkaymak M., Recebli Z.: Thermal performance parameters estimation of hot box type solar cooker by using artificial neural network. Int. J. Therm. Sci. 47(2008), 192–200.
• [24] Yuhong Z., Wenxin H.: Application of artificial neural network to predict the friction factor of open channel flow. Commun. Nonlinear Sci. 14(2009), 5, 2373– 2378.
• [25] Caner M., Gedik E., Kecebas A.: Investigation on thermal performance calculation of two type solar air collectors using artificial neural network. Expert Syst. Appl. 38(2011), 3, 1668–1674.
• [26] Benli H.: Determination of thermal performance calculation of two different types solar air collectors with the use of artificial neural networks. Int. J. Heat Mass Tran. 60(2013), 1–7.
• [27] Dikmen E., Ayaz M., Ezen H.S., Kucuksille E.U., Sahin A.S.: Estimation and optimization of thermal performance of evacuated tube solar collector system. Heat Mass Transfer 50(2014), 5, 711–719.
• [28] Kalogirou S.A., Mathioulakis E., Belessiotis V.: Artificial neural networks for the performance prediction of large solar systems. Renew. Energy 63(2014), 90– 97.
• [29] Ghritlahre H.K., Prasad R.K.: Prediction of thermal performance of unidirectional flow porous bed solar air heater with optimal training function using artificial neural network. Energy Procedia 109(2017), 369–376.
• [30] Haykin S.: Neural Networks. A Comprehensive Foundation. Prentice-Hall, New Jersey 1994.
• [31] May R.J., Maier H.R., Dandy G.C., Fernando T.M.K.G.: Non-linear variable selection for artificial neural networks using partial mutual information. Environ. Modell. Softw. 23(2008), 1312–1326.
• [32] May R., Dandy G., Maier H.: Review of input variable selection methods for artificial neural networks. In: Artificial neural networks-methodological advances and biomedical applications (S. Kenji, ed.), InTech, Rijeka 2011.
• [33] Ghritlahre H.K., Prasad R.K.: Energetic and exergetic performance prediction of roughened solar air heater using artificial neural network. Ciencia e Tecnica Vitivinicola 32(2017), 11, 2–24.
• [34] Ghritlahre H.K., Prasad R.K.: Application of ANN technique to predict the performance of solar collector systems – A review. Renew. Sust. Energ. Rev. 84(2018) 75–88.
• [35] Ghritlahre H.K., Prasad R.K.: Development of optimal ANN model to estimate the thermal performance of roughened solar air heater using two different learning algorithms. Annals of Data Sci. (2018), 1–15.
• [36] Ghritlahre H.K., Prasad R.K.: Exergetic performance prediction of a roughened solar air heater using artificial neural network. Strojniski vestnik – J. Mech. Eng. 64(2018), 3, 195–206.
• [37] Ghritlahre H.K., Prasad R.K.: Investigation on heat transfer characteristics of roughened solar air heater using ANN technique. Int. J. Heat Technology 36(2018), 1, 102–110.
• [38] Ghritlahre H.K., Prasad R.K.: Investigation of thermal performance of unidirectional flow porous bed solar air heater using MLP, GRNN, and RBF models of ANN technique. Therm. Sci. Eng. Prog. 6(2018), 226–235.
• [39] Ghritlahre H.K., Prasad R.K.: Exergetic performance prediction of solar air heater using MLP, GRNN and RBF models of artificial neural network technique. J. Environ. Manage. 223(2018), 566–575.
• [40] Matlab, Version 8.4, Neural Network Tool Box, Inc. R2014b.

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).