Tayfun MENLİK, Volkan KIRMACI, Hüseyin USTA

Modeling of freeze drying behaviors of strawberries by using artificial neural network

Dondurarak kurutma işlemi, kurutma süresi, basınç, ürün kalınlığı, kurutma odası sıcaklığı, ürün sıcaklığı ve bağıl nem gibi farklı parametrelere bağlıdır. MC ve MR gibi dondurarak kurutma davranışlarının belirlenmesi oldukça karmaşıktır. Bu çalışmada, bu karmaşıklığı gidermek için yapay sinir ağlarının kullanılması amaçlanmıştır. Dondurarak kurutma işleminde çileklerin MC ve MR gibi kurutma davranışlarının tahmin edilerek belirlenebilmesi için yapay sinir ağı modeli geliştirildi. Ağ modellinde geri besleme yayılımlı öğrenme algoritması, Levenberg– Marquardt (LM), ve Fermi transfer fonksiyonu kullanılmıştır. Ayrıca $R^2$, RMSE ve MAPE kullanılarak geliştirilen modelin doğruluğu belirlenmiştir. MC ve MR için $R^2$, RMSE ve MAPE sırasıyla, 0.999, 0.001924, 0.152284 ve 0.999, 1.87E-05, 0.13393 olarak belirlenmiştir.

Çileklerin dondurarak kurutma davranışlarının yapay sinir ağları kullanılarak modellenmesi

The freeze drying process is based on different parameters, such as drying time, pressure, sample thickness, chamber temperature, sample temperature and relative humidity. Hence, the determination of the drying behaviors, such as MC and MR, of the freeze drying process are too complex. In this paper, to simplify this complex process, the use of artificial neural networks has been proposed. An artificial neural networks model has been developed for the prediction of drying behaviors, such as MC and MR, of strawberries in the freeze drying process. The back-propagation learning algorithm with variant which is Levenberg–Marquardt (LM) and Fermi transfer function have been used in the network. In addition, the statistical validity of the developed model has been determined by using the coefficient of determination ($R^2$), the root means square error (RMSE) and the mean absolute percentage error (MAPE). $R^2$, RMSE and MAPE have been determined for MC and MR as 0.999, 0.001924, 0.152284 and 0.999, 1.87E-05, 0.13393, respectively.

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Claussen, I. C., Ustad, T. S., Strommen, I. and Walde, P. M., Atmospheric Freeze Drying-A Review, Drying Technology 25, 957-967, 2007.
Cubillos, F. and Reyes, A., Drying of carrots in a fluidized bed. II. Desing of a model based on a modular neural network approach, Drying technology 21, N. 7, 1185-1196, 2003.
Doymaz, İ., Convective drying kinetics of strawberry, Chemical Engineering and Processing 47, 914-919, 2008.
Hussain, M. A. and Rahman, M. S. Ng. C. W., Prediction of pores formation (porosity) in foods during drying: generic modes by the use of hybrid neural network, Journal of Food Engineering 51, 239- 248, 2002.
Islam, Md. R., Sablani, S. S. and Mujumdar, A. S., An artificial neural network model for prediction of drying rates, Drying technology 21, N. 9, 1867-1884, 2003.
Jang, J-SR., Sun, C. T. and Mizutani, E., Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence, Prentice-Hall International, 1997.
Kavak Akpinar E. and Bicer, Y., Mathematical modelling and experimental study on thin layer drying of strawberry, International Journal Food Engineering 2 (1), article 5, 2006.
Kırmacı, V., Dondurarak Kurutma Sisteminin Tasarımı, İmalatı ve Performans Deneylerinin Yapılması, Gazi University, Ph.D. Thesis, Ankara, 2008.
Kırmacı, V. Usta, H., Menlik, T., An Experimental Study on Freeze-Drying Behavior of Strawberries, Drying Technology, 26 (12), 1570-1576, 2008.
Liapis, A. I. and Bruttini, R., Freeze-drying of pharmaceutical crystalline and amorphous solutes in vials: Dynamic multi-dimensional models of the primary and secondary drying stages and qualitative features of the moving interface, Drying Technology 13, 43-72, 1995.
Liapis, A. I., Pikal, M. J. and Bruttini, R., Research and development needs and opportunities in freeze drying, Drying Technology 14, 1265-1300, 1996.
Marques, L. G. and Freire, J. T., Analysis of Freeze- Drying of Tropical Fruits, Drying Technology 23, 2169-2184, 2005.
Poonnoy, P., Tansakul, A. and Chınnan, M., Artificial neural network modeling for temperature and moisture content prediction in tomato slices undergoing microwave-vacuum drying, Journal of Food Science 72, Nr. 1, 42-47, 2007.
Sadikoglu, H. and Liapis, A. I., Mathematical modeling of the primary and secondary drying stages of bulk solution freeze drying in trays: Parameter estimation and model discrimination by comparison of theoretical results with experimental data, Drying Technology 15, 791-810, 1997.
Sadikoglu, H., Ozdemir, M. and Seker, M., Freeze Drying of Pharmaceutical Products: Research and Development Needs, Drying Technology 24, 849-861, 2006.
Satish, S. and Setty, Y. P., Modeling of a continuous fluidized bed dryer using artificial neural Networks, International Communications in Heat and Mass Transfer 32, 539-547, 2005.
Sharma, R., Singhal, D., Ghosh, R. and Dwivedi, A., Potential applications of artificial neural networks to thermodynamics: vapor–liquid equilibrium predictions, Computers and Chemical Engineering 23, 385-390, 1999.
Sözen, A., Arcaklıoğlu, E. and Özalp, M., A new approach to thermodynamic analysis of ejector– absorption cycle: artificial neural Networks, Applied Thermal Engineering 23, 937-952, 2003.
Sözen, A. and Arcaklıoğlu, E., Prediction of net energy consumption based on economic indicators (GNP and GDP) in Turkey, Energy Policy 35, 4981-4992, 2007.
Sözen, A., Gülseven, Z. and Arcaklıoğlu, E., Forecasting based on sectoral energy consumption of GHGs in Turkey and mitigation policies, Energy Policy 35, 6491-6505, 2007.
Sözen, A., Menlik, T. and Ünvar, S., Determination of efficiency of flat-plate solar collectors using neural network approach, Expert Systems with Applications, 35(4), 1533-1539, 2008.
Sözen, A., Arcaklıoğlu, E., Menlik, T. and Özalp, M., Determination of thermodynamic properties of an alternative refrigerant (R407c) using artificial neural network, Expert Systems with Applications, 36(3), 4346-4356, 2009.
Yücesu, S., Sözen, A. Topgül, T. and Arcaklıoğlu, E., Comparative study of mathematical and experimental analysis of spark ignition engine performance used ethanol–gasoline blend fuel, Applied Thermal Engineering 27, 358-368, 2007.
Zhang, B. M., Xiao, G. and Salokhe, V. M., Preservation of strawberries by modified atmosphere packages with other treatments, Packag. Technol. Sci. 19, 183-191, 2006.