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Evaluation of Thin-Layer Drying Models and Artificial Neural Networks for Describing Drying Kinetics of Canola Seed in a Heat Pump Assisted Fluidized Bed Dryer

  • Narjes Malekjani EMAIL logo , Seid Mahdi Jafari , Mohammad Hashem Rahmati , Ebrahim Esmaeel Zadeh and Hbibollah Mirzaee
Published/Copyright: November 6, 2013

Abstract

In this study, drying characteristics of canola seeds were determined using heated ambient air at 40, 50 and 60°C, relative humidity of 20, 40 and 60% and constant velocity of 3 m/s. To select a suitable drying curve, six thin-layer drying models were fitted to experimental data. The models were compared according to three statistical parameters: R2, reduced chi-square (χ2) and root mean square error. Using some experimental data, an Artificial neural network model, trained by Feed Forward Back-Propagation algorithm, was developed to predict moisture ratio values based on the three input variables. Different activation functions and several rules were used to assess percentage error between the desired and predicted values. According to the results, the approximation of diffusion drying model had better agreement with the drying data. The artificial neural network model was able to predict the moisture ratio quite well with R2 of 0.9994. The predicted mean square error was obtained as 0.00012575.

References

1. DonaldBE, BassinP. Rapeseed and crambe: alternative crops with potential industrial uses. Bulletin 656. Agricultural Experiment Station, Kansas State University, Manhattan, Walter R. Woods, 36 p, 1991. ISSN 0097-0484.Search in Google Scholar

2. SadowskaJ, FornalJ, OstaszykA, SzmatowiczB. Drying conditions and processability of dried rapeseed. J Sci Food Agric1996;72:25762.10.1002/(SICI)1097-0010(199610)72:2<257::AID-JSFA650>3.0.CO;2-9Search in Google Scholar

3. AkpinarEK, SarsilmazC, YildizC. Mathematical modeling of a thin layer drying of apricots in a solar energized rotary dryer. Int J Energy Res2004;28:73952.10.1002/er.997Search in Google Scholar

4. ChakravertyA, SinghRP. Post-harvest technology of cereals, pulses and oilseeds. New Delhi, India: Oxford and IBH Publishing, 1988.Search in Google Scholar

5. MidilliA. Determination of pistachio drying behavior and conditions in a solar drying system. Int J Energy Res2001;25:71525.10.1002/er.715Search in Google Scholar

6. MidilliA, KucukH. Mathematical modeling of thin layer drying of pistachio by using solar energy. Energy Convers Manage2003;44:111122.10.1016/S0196-8904(02)00099-7Search in Google Scholar

7. BasuniaMA, AbeT. Thin-Layer solar drying characteristics of rough rice under natural convection. J food Eng2001;47:295301.10.1016/S0260-8774(00)00133-3Search in Google Scholar

8. YaldizO, ErtekinC. Thin layer solar drying of some vegetables. Dry Technol2001;19:58396.10.1081/DRT-100103936Search in Google Scholar

9. DincerI. Sun drying of sultana grapes. Dry Technol1996;14:182738.10.1080/07373939608917176Search in Google Scholar

10. YaldızO, ErtekinC, UzunHI. Mathematical modelling of thin layer solar drying of sultana grapes. Energy2001;26:4579.10.1016/S0360-5442(01)00018-4Search in Google Scholar

11. SinghH, SodhiNS. Dehydration kinetics of onions. J Food Sci Technol2000;37:5202.Search in Google Scholar

12. DoymazI, PalaM. The effects of dipping pretreatment on air-drying rates of seedless grapes. J Food Eng2002;52:41327.10.1016/S0260-8774(01)00133-9Search in Google Scholar

13. SharmaGP, PrasadS, DattaAK. Drying kinetics of garlic cloves under convective drying conditions. J Food Sci Technol2003;40:4551.Search in Google Scholar

14. FriantNR, MarksBP, Bakker-ArkemaFW. Drying rate of corn. T ASAE2004;47:160510.10.13031/2013.17590Search in Google Scholar

15. WuD, ZijiangY, LiangL. Using DEA-neural network approach to evaluate branch efficiency of a large Canadian bank. Expert Syst Appl2006;31:10815.10.1016/j.eswa.2005.09.034Search in Google Scholar

16. FujiwaraT. In: BulsariAB, editor. Neural networks for chemical engineers. Amsterdam: Elsevier-Publishers, 1995:28395.Search in Google Scholar

17. HaykinS. Neural networks, a comprehensive foundation. New York: Macmillan College Publishing Company, 1994.Search in Google Scholar

18. Tai-YueW, Shih-ChienC. Forecasting innovation performance via neural networks – a case of Taiwanese manufacturing industry. Technovation2006;26:63543.10.1016/j.technovation.2004.11.001Search in Google Scholar

19. GrossbergS. Studies of the mind and brain. Drodrecht, Holland: Reidel Press, 1982.10.1007/978-94-009-7758-7Search in Google Scholar

20. KohonenT. Self-organization and associative memory, 2nd ed. Springer Series in Information Sciences, Vol. 8. Berlin: Springer Verlag, 1987.10.1007/978-3-662-00784-6Search in Google Scholar

21. HillT, MarquezL, OconnorM, RemusW. Artificial neural network models for forecasting and decision making. Int J Forecasting1994;10:5.10.1016/0169-2070(94)90045-0Search in Google Scholar

22. Savkovic-StevenovicJ. Neural networks for process analysis and optimization: modelling and applications. Comput Chem Eng1994;18:p 1149.10.1016/0098-1354(94)E004H-ZSearch in Google Scholar

23. SheneC, AndrewsB, AsenjoAJ. Optimization of Bacillus subtilis for the fed-batch fermentations for the maximization of the synthesis of a recombinant b –1, 3-glucanase, CAB, Japan, Vol. 7, 1998:219.Search in Google Scholar

24. SheneC, AndrewsB, AsenjoAJ. Fed-batch optimizations of bacillus subtilis fed-batch ToC46 (pPFF1) for the synthesis of a recombinant b –1, 3-glucanase: experimental study and modelling. Enzyme Microb Tech1999;24:247.10.1016/S0141-0229(98)00118-5Search in Google Scholar

25. Hernandez-PerezJA, Garca-AlvaradoMA, TrystramG, HeydB. Neural networks for the heat and mass transfer prediction during drying of cassava and mango. IFSET2004;5:5764.10.1016/j.ifset.2003.10.004Search in Google Scholar

26. IslamMd, SablaniSS, MujumdarAS. An artificial neural network model for prediction of drying rates. Dry Technol2003;21:186784.10.1081/DRT-120025512Search in Google Scholar

27. CubillosF, ReyesA. Drying of carrots in a fluidized bed. II. Design of a model based on a modular neural network approach. Dry Technol2003;21:118596.10.1081/DRT-120023175Search in Google Scholar

28. AOAC. Official method of analysis, association of official analytical chemists (no. 934.06). Arlington, VA: AOAC, 1990.Search in Google Scholar

29. CassellsJA, CaddickLP, GreenJR, ReussR. Isotherms for Australian canola varieties. In: WrightEJ, WebbMC, HighleyE, editors. Proceedings of the Australian postharvest technical conference, 25–27 June 2003, Canberra, 2003.Search in Google Scholar

30. ANSI/ASAE S448.1. Thin-Layer drying of agricultural crops. ASAE standards 51st edition, 2004:598600.Search in Google Scholar

31. SinicioR, MuirWE, JayasDS, CenkowskiS. Thin-layer drying and wetting of wheat. Postharvest Biol Technol1994;5:26175.10.1016/0925-5214(94)00023-LSearch in Google Scholar

32. CrispJ, WoodsJL. The drying properties of rapeseed. J Agric Eng Res1994;57:8997.10.1006/jaer.1994.1008Search in Google Scholar

33. PathakPK, AgrawalYC, SinghBP. Thin-layer drying model for rapeseed. T ASAE1991;34:25058.10.13031/2013.31899Search in Google Scholar

34. LoCurtoGJ, ZakirovV, BucklinRA, HanesDM, TeixeiraAA, WaltonOR, et al. Soybean friction properties. Annual International Meeting, ASAE, Paper No. 974108, 1997.Search in Google Scholar

35. PageGE. Factors influencing the maximum of air drying shelled corn in thin layer. Indiana: Purdue University, 1949.Search in Google Scholar

36. OzdemirM, DevresYO. The thin layer drying characteristics of hazelnuts during roasting. J Food Eng1999;42:22533.10.1016/S0260-8774(99)00126-0Search in Google Scholar

37. VermaLR, BucklinRA, EndanJB, WrattenFT. Effects of drying air parameters on rice drying models. T ASAE1985;28:296301.10.13031/2013.32245Search in Google Scholar

38. KarathanosVT. Determination of water content of dried fruits by drying kinetics. J Food Eng1999;39:33744.10.1016/S0260-8774(98)00132-0Search in Google Scholar

39. DemirV, GunhanT, YagciogluAK, DegirmenciogluA. Mathematical modelling and the determination of some quality parameters of air-dried bay leaves. Biosyst Eng2004;88:32535.10.1016/j.biosystemseng.2004.04.005Search in Google Scholar

40. TogrulIT, PehlivanD. Modelling of drying kinetics of single apricot. J Food Eng2003;58:2332.10.1016/S0260-8774(02)00329-1Search in Google Scholar

41. KudrasT, EfremovGI. A quasi-stationary approach to drying kinetics in fluidized particulate materials. Dry Technol2003;21:107790.10.1081/DRT-120021875Search in Google Scholar

42. SyahrulS, HamdullahpurF, DicerI. Exergy analysis of fluidized bed drying of moist particles. EXERGY2002;2:8798.10.1016/S1164-0235(01)00044-9Search in Google Scholar

43. TopuzA, GurM, GulMZ. An experimental and numerical study of fluidized bed drying of hazelnut. Appl Therm Eng2004;24:153447.10.1016/j.applthermaleng.2003.11.020Search in Google Scholar

44. ChenCC, MoreyRV. Comparison of four EMC/ERH equations. T ASAE1989;32:98390.10.13031/2013.31103Search in Google Scholar

45. XanthopoulosG, OikonomouN, LambrinosG. Applicability of a single-layer drying model to predict the drying rate of whole figs. J Food Eng2007;81:5539.10.1016/j.jfoodeng.2006.11.033Search in Google Scholar

Published Online: 2013-11-06

©2013 by Walter de Gruyter Berlin / Boston

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