EXPERIMENTAL INVESTIGATION AND NEURAL NETWORK PREDICTION OF THE PERFORMANCE OF A MIXED MODE SOLAR DRYER FOR COCONUT
DOI:
https://doi.org/10.24297/jac.v12i25.4395Keywords:
Solar energy, mathematical model, drying capacity, copra, artificial neural networksAbstract
The shelf life of agricultural food products may be enhanced by reducing their moisture contents, by means of a drying process. The present work aims at drying coconut yielding copra. This paper presents the design, analysis of a mixed mode solar dryer for food preservation and energy saving. In the mixed mode solar dryer, the drying cabinet absorbs solar energy directly through the transparent roof and during the same time the heated air from a solar collector is passed through a tray. Various measurements like solar radiation, mass flow rate, and moisture content and relative humidity have been observed. From previous literature four different models (Newton, Page, Henderson & Pabis and Wang & Singh) are chosen for testing the performance of mixed mode solar dryer. Selected models are evaluated by using EMD, ERMS, R2 and ðœ’2 and it is concluded that page model is more suitable for the fabricated cabinet solar dryer at air flow rate 0.009Kg/s based on the experimental analysis. The direct radiant solar energy and a convective hot air stream dry the products, resulting in longer life for the products which are also free from impurities. The experimental results are utilized to evolve a suitable mathematical model, among the different models that are chosen, for copra. This will help in designing suitable dryers for actual users. Also, a multilayer neural network approach has been used to predict the performance of a mixed mode solar dryer for drying coconut. The simulation of neural network is based on the feed forward back propagation algorithm.
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References
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27. Rahman, M. M., and Bala, B. K. 2010. Modeling of jute production using artificial neural networks. Biosystems Engineering, 105(3):350-356.
28. Sharma, V. K., Colalgelo, A., and Spagna, G. 1995. Experimental investigation of different solar dryers suitable for fruits and vegetables drying. Renewable Energy, 6(4):413-424.
29. Henderson, S. M. 1974. Progress in developing the thin layer drying equation. Transactions of the ASAE, 17, 1167-1172.
30. Diamante, L. M., and Munro, P. 1993. Mathematical modeling of the thin layer solar drying of sweet potato slices. Sol. Energy, 51:271-276.
31. Zhang, Q., and Litchfield, J. B. 1991. An optimization of intermittent corn drying in a laboratory Scale thin layer dryer. Drying Technology 9:383-395.
32. Wang, C.Y., and Singh, R.P. 1978. A single layer drying equation for rough rice. ASAE 78-3001.
2. Manashi Das Purkayastha, Amit Nath, Bidyut Chandra Deka, Charu Lata Mahanta. 2013. Thin layer drying of tomato slices. Journal of Food Science and Technology, 50(4):642-653.
3. Amruta, R., Eswar and M Ramakrishnarao. 2013. Solar energy in food processing a critical appraisal. J. Food Sci Techno., 50:209-227.
4. Sra., S. K, Sandhu, K. S, Ahluwalia, P. 2014. Effect of treatments and packaging on the quality of dried carrot slices during storage. Journal of Food Science and Technology 51(4):645-654.
5. Sharma, V. K., Colalgelo, A., Spagna, G. 1995. Experimental investigation of different solar dryers suitable for fruits and vegetables drying. Renewable Energy, 6(4):413-424.
6. Akwasi and Ayensu. 1997. Dehydration of food crops using a solar dryer with convective heat flow solar energy. Solar Energy, 59,121-126.
7. Bala, B..K., Mondol, M. R. .A., Biswas, B. K., Das Choudhury, B. L. and Janjai, S. 2003. Solar drying of pineapple using solar tunnel drier. Renewable Energy, 28(2):183-190.
8. Karim, M. A., and Hawlader, M. N. A. 2005. Mathematical modeling and experimental investigation of tropical fruits drying. International Journal of Heat Mass Transfer, 48(23):4914-4925.
9. Ratti, C., and Mujumdar, A. S. 1997. Solar drying of foods modeling and numerical simulation. Solar Energy, 60(3):151-157.
10. Schirmer, Janjai, S., Esper A., Smitabhindu, R., and Mühlbauer, W. 1996. Experimental investigation of the performance of the solar drier for drying bananas. Renewable Energy, 7(2):119-129.
11. Jain, D., and Jain, R. K. 2004. Performance evaluation of an inclined multi-pass solar air heater with in-built thermal storage on deep-bed drying application. Journal of Food Engineering, 65:497-509.
12. U.S. Energy Information Administration. Annual Energy Outlook 2010 with Projections to 2035 (DOE/EIA – 0383) 54-134.
13. Mani, A., and Rangarajan, S. 1982. Solar radiation over India. New Delhi: Allied Publishers.
14. Tunde-Akintunde, T. Y., and Ogunlakin, G.O. 2013. Mathematical modeling of drying of pretreated and untreated Pumpkin. Journal of Food Science and Technology, 50(4):705-713.
15. Khalil, E. J., Al-Juamily, Abdul JabbarKhalifa, and Tadahmun, N. 2007. A testing of the performance of a fruit and vegetable solar drying system in Iraq. Desalination, 209(1):163-170.
16. Mohanraj, M., Jayaraj, S., and Muraleedharan, C. 2009. Performance prediction of a direct expansion solar assisted heat pump using artificial neural networks. Applied energy, 86(9):1442-1449.
Ming Yang, Xudong Yang, Xing Li, Zhifeng Wang, and Pengsu Wang. 2014. Design and optimization of a solar air heater with offset strip fin absorber plate. Applied energy, 113:1349-1362.
17. IIhan Ceylan, and Mustafa Aktas. 2008. Modeling of a hazelnut dryer assisted heat pump by using artificial neural networks. Applied energy, 85:841-854.
Genta KANAI, Katushiko TAMAKI, and Yuji NAGASAKI. 2013. Review development of classification drying of high moisture wheat grain according to the moisture content. Japan agricultural research quarterly, 47:141-151.
18. Genta KANAI, Hitoshi KATO, Naonobu UMEDA, and Kensuke OKADA. 2010. Drying condition and qualities of rapeseed and sunflower. Japan agricultural research quarterly, 44:173-178.
19. Takahiro NODA, Yasuyuki, Masahiro KANESAKI and Mio YOKOE. 2011. Utilization of a direct combustion type Husk burner for grain drying. Japan agricultural research quarterly, 45:433-440.
20. Tayyeb Nazghelichi, Mortaza Aghbashlo, Mohammad Hossein Kianmehr. 2011. Optimization of an artificial neural network topology using coupled response surface methodology and genetic algorithm for fluidized bed drying. Computer and Electronics in Agriculture, 75(1):84-91.
21. Tayfan Menlik, Mustafa Bahadir Ozdemir, and Volkan Kiramaci. 2010. Determination of freeze-drying behaviors of apples by artificial neural network. Expert System with Applications 37(12):7669-7677.
22. Arijit Das, Animes Kumar Golder, and Chandan Das. 2014. Enhanced extraction of rebaudioside-A: experimental, response surface optimization and prediction using artificial neural network. Industrial Crops and Products Article in Press: INDCRO-7632.
23. Ali Mohammad Nikhakht, Ali Motevali, Saeid Minaei. 2014. Energy and exergy investigation of microwave assisted thin- layer drying of pomegranate arils using artificial neural networks and response surface methodology. J. Saudi Society of Agricultural Science, 13:81-91.
24. Bernd van der Meulen. 2015. Is current EU food safety law geared up for fighting food fraud? Journal fu¨ r Verbraucherschutz und Lebensmittelsicherheit. Journal Consumer Protection and Food Safety, 10:19-23.
25. Ramesh, C., Sivakumar, R., Premalatha, M., Vivekanandan, M., and Manickam, C. 2014. Experimental studies on the performance of a large-scale Solar Greenhouse dryer for banana in India – A case study, journal of advances in chemistry, 12:2321-807X.
26. Tripathy, P.P, and Subodh Kumar. 2009. Neural network approach for food temperature prediction during solar drying. International Journal of Thermal Science 48(7):1452-1459.
Perez-Alonso, J., Perez-Garcia, M., Pasamontes-Romera, M., and Callejon-Ferre, A.J. 2012. Performance analysis and neural modeling of a greenhouse integrated photovoltaic system. Renewable and Sustainable Energy Review, 16(7):4675-4685.
27. Rahman, M. M., and Bala, B. K. 2010. Modeling of jute production using artificial neural networks. Biosystems Engineering, 105(3):350-356.
28. Sharma, V. K., Colalgelo, A., and Spagna, G. 1995. Experimental investigation of different solar dryers suitable for fruits and vegetables drying. Renewable Energy, 6(4):413-424.
29. Henderson, S. M. 1974. Progress in developing the thin layer drying equation. Transactions of the ASAE, 17, 1167-1172.
30. Diamante, L. M., and Munro, P. 1993. Mathematical modeling of the thin layer solar drying of sweet potato slices. Sol. Energy, 51:271-276.
31. Zhang, Q., and Litchfield, J. B. 1991. An optimization of intermittent corn drying in a laboratory Scale thin layer dryer. Drying Technology 9:383-395.
32. Wang, C.Y., and Singh, R.P. 1978. A single layer drying equation for rough rice. ASAE 78-3001.
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Published
2016-12-26
How to Cite
Murugavel, K. K., Subbian, V., & Thirupathieswaran, R. (2016). EXPERIMENTAL INVESTIGATION AND NEURAL NETWORK PREDICTION OF THE PERFORMANCE OF A MIXED MODE SOLAR DRYER FOR COCONUT. JOURNAL OF ADVANCES IN CHEMISTRY, 12(25), 5635–5644. https://doi.org/10.24297/jac.v12i25.4395
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