Development of Feed-Forward Back-Propagation Neural Model to Predict the Energy and Exergy Analysis of Solar Air Heater

Harish Kumar Ghritlahre

Abstract


In the present work, Artificial Neural Network (ANN) model has been developed to predict the energy and exergy efficiency of a roughened solar air heater (SAH).  Total fifty data sets of samples, obtained by conducting experiments on SAHs with three different specification of wire-rib roughness on the absorber plates, have been used in this work. These experimental data and calculated values of thermal efficiency and exergy efficiency have been used to develop an ANN model. Levenberg-Marquardt (LM) and Scaled Conjugate Gradient (SCG) learning algorithm were used to train the proposed ANN model. Six numbers of neurons were found with LM learning algorithm in the hidden layer as the optimal value on the basis of statistical error analysis. In the input layer, the time of experiments, mass flow rate, ambient temperature, mean temperature of air, absorber plate temperature and solar radiation intensity have been taken as input parameters; and energy efficiency and exergy efficiency have been taken as output parameters in the output layer. The 6-6-2 neural model has been obtained as the optimal model for prediction. Performance predictions using ANN were compared with the experimental data and a close agreement was observed. Statistical error analysis was used to evaluate the results.

Citation: 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


Keywords


Solar air heater; Energy analysis; Exergy analysis; Artificial Neural Network; Multi-layer perceptron

Full Text:

FULL TEXT (PDF)

References


Duffie, J.A., Beckman, W.A. (1991). Solar Engineering of Thermal Processes, second ed.,Wiley Publication, New York.

Bhushan, B., Singh, R. (2010). A review on methodology of artificial roughness used in duct of solar air heaters, Energy, 35, 202–212.

Chamoli, S., Thakur, N.S., Saini,J.S. (2012). A review of turbulence promoters used in solar thermal system. Renew. Sustain. Energy Rev., 16, 3154–3175.

Prasad, B.N. (2013). Thermal performance of artificially roughened solar air heaters. Sol. Energy, 91, 59–67.

Kumar, A., Prasad, B.N., Prasad, L. (2014). Thermal performance of artificially roughened solar air heaters, M.Tech Thesis, National Institute of Technology, Jamshedpur, Jharkhand, India.

Mittal, M.K., Varshney, L. (2006). Optimal thermo hydraulic performance of a wire mesh packed solar heater. Solar Energy, 80, 1112-1120.

Singh, P.L., Deshpandey, B.D., Jena, P.C. (2015). Thermal performance of packed bed heat storage system for solar air heaters. Energy for sustainable Development, 29, 112-117.

Ajam, H., Farahat, S., Sarhaddi, F. (2005). Exergetic optimization of solar air heaters and comparison with energy analysis. Int J Thermodyn, 8(4), 183–190.

Esen, H. (2008). Experimental energy and exergy analysis of a double-flow solar air heater having different obstacles on absorber plates. Build and Environ, 43,1046–1054.

Akpinar, E.K., Koçyigit, F. (2010). Experimental investigation of thermal performance of solar air heater having different obstacles on absorber plates. Int Commun Heat Mass Transfer, 37, 416–421.

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

Bayrak, F., Oztop, H.F., Hepbasli, A. (2013). Energy and exergy analyses of porous baffles inserted solar air heaters for building applications. Energy Build, 57, 338-345.

Panwar, N., Kaushik, S., Kothari, S. (2012). A review on energy and exergy analysis of solar dying systems. Renew Sustain Energy Rev, 16(5), 2812-2819.

Saidur, R., BoroumandJazi, G., Mekhlif, S., Jameel, M. (2012). Exergy analysis of solar energy applications. Renew Sustain Energy Rev, 16(1), 350-356.

Kumar, L., Mukesh Sarvaiya, R.M., Bhagoriya, J.L. (2012). Exergy evaluation of packed bed solar air heater. Renewable and Sustainable Energy Reviews, 16, 6262-6267.

Park, S., Pandey, A., Tyagi, V., Tyagi, S. (2014). Energy and exergy analysis of typical renewable energy systems, Renewable and Sustainable Energy Reviews, 30, 105-123.

Kalogirou, S.A. (2000). Applications of artificial neural-networks for energy systems. Applied Energy, 67(1-2), 17–35.

Yang, I.H.,Yeo, M.S., Kim, K.W. (2003). Application of artificial neural network to predict the optimal start time for heating system in building. Energy Conversion and Management, 44, 2791–2809.

Facao, J., Varga, S., 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.

Ertunc, H.M., Hosoz, M.(2006). Artificial neural network analysis of a refrigeration system with an evaporative condenser. Applied Thermal Engineering, 26, 627–635.

Kalogirou, S.A. (2006). Prediction of flat-plate collector performance parameters using artificial neural networks. Solar Energy, 80, 248–259.

Yilmaz, S., Atik, K. (2007). Modeling of a mechanical cooling system with variable cooling capacity by using artificial neural network. Applied Thermal Engineering, 27, 2308–2313.

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.

Kurt, H., Atik, K., Ozkaymak, M., Recebli, Z. (2008). Thermal performance parameters estimation of hot box type solar cooker by using artificial neural network. International Journal of Thermal Sciences, 47, 192–200.

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.

Nazghelichi, T., Aghbashlo, M., Kianmehr, M.H., Omid, M. (2011). Prediction of Energy and Exergy of Carrot Cubes in a Fluidized Bed Dryer by Artificial Neural Networks. Drying Technology: An International Journal, 29(3), 295-307.

Aghbashlo, M., Mobli, H., Rafiee, S., Madadlou, A. (2012). The use of artificial neural network to predict exergetic performance of spray drying process: A preliminary study. Computers and Electronics in Agriculture, 88, 32–43.

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.

Kalogirou, S.A., Mathioulakis, E., Belessiotis, V. (2014). Artificial neural networks for the performance prediction of large solar systems. Renewable Energy, 63 ,90-97.

Hamdan, M. A., Abdelhafez, E. A., Hamdan, A. M., and Khalil, R. A. H. (2016). Heat Transfer Analysis of a Flat-Plate Solar Air Collector by Using an Artificial Neural Network. Journal of Infrastructure Systems, 22(4), A4014004. DOI: 10.1061/(ASCE)IS.1943-555X.0000213.

Jani, D.B., Mishra, M., Sahoo, P.K. (2016). Performance prediction of solid desiccant – vapor compression hybrid air-conditioning system using artificial neural network. Energy, 103, 618-629.

Jani, D.B., Mishra, M., Sahoo, P.K. (2016). Performance prediction of rotary solid desiccant dehumidifier in hybrid air-conditioning system using artificial neural network. Appl Therm Eng, 98, 1091–1103.

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.

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.

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.

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.

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.

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.

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.

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.

Haykin, S. (1994). Neural networks, a comprehensive foundation, New Jersey: Prentice- Hall.

Cengel, Y.A., Boles, M.A. (2006). Thermodynamics: an engineering approach. 5th ed. New York: McGraw-Hill.




DOI: http://dx.doi.org/10.17737/tre.2018.4.2.0078

Refbacks

  • There are currently no refbacks.


Copyright (c) 2018 Harish Kumar Ghritlahre

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 License.
Copyright @2014-2018 Trends in Renewable Energy (ISSN: 2376-2136, online ISSN: 2376-2144)