Performance prediction of porous bed solar air heater using MLP and GRNN model- A comparative study

  • Harish Kumar Ghritlahre Chhattisgarh Swami Vivekanand Technical University, Bhilai, Chhattisgarh, 491107, India.
Keywords: Solar air heater, Porous bed, Thermal performance, Artificial neural network

Abstract

In present work, two different types of neural model have been used to predict the thermal performance of unidirectional flow porous bed solar air heater (SAH). These models are multi-layer perceptron (MLP) and generalized regression neural network (GRNN). Total 96 data were used in neural model. The neural model developed with six input parameters: mass flow rate, wind speed, ambient temperature, inlet air temperature, air mean temperature and solar intensity, thermal efficiency is used as output variable. In MLP model, LM with 13 neurons was optimal model and in case of GRNN model, maximum accuracy in prediction has been obtained at spread value- 0.8.  The comparative analysis shows that the GRNN is the best model as compared to MLP due to less error and highest value of R2. These results show that the GRNN model is appropriate model for predicting the thermal performance SAH.

References

[1]. Duffie, J.A., W.A. Beckman, W.A.,1991. Solar Engineering of Thermal Processes, Second ed. New York:Wiley Publication.
[2]. Tiwari, G.N., 2004. Solar Energy: Fundamentals, Design, Modelling and Applications”, New Delhi, India: Narosa Publishing House.
[3]. Chiou, J.P., El-Wakil, M.M., Duffie, J.A., 1965. A slit-and -expanded aluminum-foil matrix solar collector, Sol. Energy 9, 73–80.
[4]. Sharma, S.P., Saini, J.S., Varma, H.K., 1991. Thermal performance of packed bed solar air heaters, Solar Energy 47, 59– 67.
[5]. Prasad, R.K., Saini, J.S., 1993. Comparative performance study of packed bed solar air heaters, Emerging trends in Mechanical Engineering, In proceeding of the Eighth ISME Conference on Mech. Engg., I.I.T. Delhi, India, 190-197.
[6]. Prasad, R.K., Saini, J.S., 1993. Thermal performance characteristics of unidirectional flow porous bed solar energy collectors for heating air, Ph.D. Thesis, University of Roorkee, Roorkee, India.
[7]. Ahmad, Saini, J.S., Varma, H.K., 1995. 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, 1185-1195.
[8]. Varshney, L., Saini, J.S., 1998. Heat transfer and friction factor correlations for rectangular solar air heater duct packed with wire mesh screen matrices, Solar Energy 62 (4), 255-262.
[9]. Mittal, M.K. , Varshney, L., 2006. Optimal thermohydraulic performance of a wire mesh packed solar air heater, Solar Energy 80, 1112-1120.
[10]. Omojaro, A.P., Aldabbagh, L.B.Y. , 2010. Experimental performance of single and double pass solar air heater with fins and steel wire mesh as absorber, Applied Energy 87, 3759-3765.
[11]. Gardner, M.W., Dorling, S.R., 1998. Artificial Neural Networks (The Multilayer Perceptron): a review of applications in the atmospheric sciences. Atmos. Env. 32: 2627-2636.
[12]. Ben-Nakhi, A.E., Mahmoud, M.A., 2004. Cooling load prediction for buildings using general regression neural networks, Energy Conversion and Management 45, 2127–2141.
[13]. Facao, J., Szabolcs, V., Oliveira, A. C., 2004. Evaluation of the Use of Artificial Neural Networks for the Simulation of Hybrid Solar Collectors, International Journal of Green Energy 1(3), 337–352.
[14]. Kalogirou, S.A., 2006. Prediction of flat-plate collector performance parameters using artificial neural networks, Solar Energy 80, 248–259.
[15]. 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.
[16]. Kassem, A.S., Al-Sulaiman, M.A., Aboukarima, A.M., Kassem, S.S., 2011. Predicting drying efficiency during solar drying process of grapes clusters in a box dryer using artificial neural network, Aust. J. Basic Appl. Sci. 5, 230–241.
[17]. Caner, M., Gedikand, 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.
[18]. 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.
[19]. Sahin, M., Kaya, Y., Uyar, M., 2013. Comparison of ANN and MLR models for estimating solar radiation in Turkey using NOAA/AVHRR data, Adv. Space Res. 51, 891–904.
[20]. Çakmak, G., 2014. The water temperature prediction of a double exposure solar cooker, Environ. Prog. Sust. Energy 33, 629–635.
[21]. Citakoglu, H., 2015. Comparison of artificial intelligence techniques via empirical equations for prediction of solar radiation, Comput. Electron. Agric. 118, 28–37.
[22]. Mashalyand, A.F., Alazba, A.A., 2015. Comparative investigation of artificial neural network learning algorithms for modeling solar still production, J. Water Reuse Desalination 5 (4), 480–493.
[23]. Mashaly, A.F., Alazba, A.A., 2016. MLP and MLR models for instantaneous thermal efficiency prediction of solar still under hyper-arid environment, Computers and Electronics in Agriculture 122 , 146–155.
[24]. Ghritlahre, H.K., Prasad , R.K., 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.
[25]. Ghritlahre, H.K., Prasad , R.K., 2017. Energetic and exergetic performance prediction of roughened solar air heater using artificial neural network, Ciência e Técnica Vitivinícola, 32 (11), 2-24.
[26]. Ghritlahre, H.K., Prasad , R.K., 2018. Application of ANN technique to predict the performance of solar collector systems - A review, Renewable and Sustainable Energy Reviews 84, 75–88.
[27]. Ghritlahre, H.K., Prasad, R.K., 2018. Exergetic Performance Prediction of roughened Solar Air Heater Using Artificial Neural Network, Strojniški vestnik - Journal of Mechanical Engineering 64 (3), 195–206.
[28]. Ghritlahre, H.K., Prasad, R.K., 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.
[29]. Ghritlahre, H.K., Prasad, R.K., 2018. Investigation on heat transfer characteristics of roughened solar air heater using ANN technique. International Journal of Heat and Technology 36 (1), 102-110.
[30]. Ghritlahre, H.K., Prasad, R.K., 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.
[31]. Ghritlahre, H.K., Prasad, R.K., 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.
[32]. Ghritlahre, H.K., Prasad, R.K., 2018. Prediction of exergetic efficiency of arc shaped wire roughened solar air heater using ANN model International Journal of Heat and Technology 36 (3), 1107-1115.
[33]. Ghritlahre, H.K., Prasad, R.K., 2018. Prediction of heat transfer of two different types of roughened solar air heater using Artificial Neural Network technique. Thermal Science and Engineering Progress. Volume 8, December 2018, Pages 145-153.
[34]. Ghritlahre, H. K., 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, 213-235. DOI: 10.17737/tre.2018.4.2.0078
[35]. Haykin, S., 1994. Neural networks, a comprehensive foundation, New Jersey: Prentice- Hall.
Published
2019-07-29
Section
Articles