with the collaboration of Iranian Society of Mechanical Engineers (ISME)

Document Type : Research Article

Authors

1 Department of Biosystems Engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran

2 Department of Biosystems Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran

3 Department of Biosystems Engineering, Faculty of Agriculture and Natural Resources, Arak University, Arak, Iran

Abstract

Introduction
Garlic (Allium sativum L.) is an important Allium crop in the world. Due to its therapeutic properties, it was cultivated in many countries. Furthermore, garlic is usually used as a flavoring agent; it may be used in the shape of powder or granule as a valuable condiment for foods. In addition to its use in food products, it was also widely used as an anticancer agent. Shallot (Allium hiertifolium Boiss. L) is a perennial and bulbous plant. It is from Alliaceae family and is an important medicinal plant. The shallot is native of Iran, and grows in the high pastures. Shallot is consumed in dry areas in most parts of the country. Also shallots have been well known in Iranian folk medicine and its bulbs have been widely used for treating rheumatic and inflammatory disorders. In addition, this plant is used in the preparation of significant amounts of potassium, phosphorus, calcium, magnesium, sodium, pickles and as an additive to yogurt and pickles. ANN as a modern approach has successfully been used to solve an extensive variety of problems in the science and engineering, exclusively for some space where the conventional modeling procedure fail. A well-trained ANN can be used as a predictive model for a special use, which is a data processing system inspired by biological neural system. When mathematical equations are difficult to extrapolate, and fuzzy logic is better when decisions must be made with the estimated values below the incomplete information. The fuzzy logic theory effectively addresses the uncertainty problems that solve the ambiguity.
Materials and Methods
 The aim of this study was to predict moisture ratio of garlic and shallot during the drying process with fluidized bed dryer using mathematical model, artificial neural networks and fuzzy logic methods. Tests were carried out on three levels of inlet air temperature (40, 55 and 70 °C) and three inlet air velocities (0.5, 1.5 and 2.5 m s-1). To estimate the drying kinetic of garlic and shallot, five mathematical models were used to fit the experimental data of thin layer drying. Three factors (air temperature, air velocity and drying time) to forecast moisture ratio in fluidized bed dryer as independent variables for artificial neural networks and fuzzy logic was considered. Cascade forward back propagation (CFBP) and feed forward back propagation (FFBP) with Levenberg-Marquardt (LM), Bayesian learning (BR) algorithms for ANN and the Mamdani Fuzzy Inference System using triangular membership function were used for training patterns.
Results and Discussion
Consequently, the Page and Midilli et al. model was selected as the best mathematical model to describe the drying kinetics of the garlic and shallot slices, respectively. The results of artificial neural networks model for predicting MR showed that the R2 of 0.9994 and 0.9996; and and RMSE of 0.0036 and 0.0014 were obtained for garlic and shallot, respectively. Also, The fuzzy inference system presented the R2 of 0.9997 and 0.9998; and and RMSE of 0.0027 and 0.0011 for garlic and shallot, respectively. Comparing the results obtained from mathematical models, artificial neural networks and fuzzy logic, showed that the RMSE in the fuzzy logic was lower than artificial neural network and mathematical models.
Conclusion
Three factors (air temperature, air velocity and drying time) were considered for forecasting moisture ratio in fluidized bed dryer as independent variables using mathematical model, artificial neural networks and fuzzy logic. Cascade forward back propagation (CFBP) and feed forward back propagation (FFBP) with Levenberg-Marquardt (LM), Bayesian learning (BR) algorithms and the Mamdani Fuzzy Inference System using triangular membership function were used for training the patterns. Comparing the results obtained from mathematical models, artificial neural networks and fuzzy logic, showed that the root mean square error in fuzzy logic was lower than others.

Keywords

1. Adak, N., N. Heybeli, and C. Ertekin. 2017. Infrared drying of strawberry. Food Chemistry 219: 109-116.
2. Aghbashlo, M., R. Sotudeh-Gharebagh, R. Zarghami, A. S. Mujumdar, and N. Mostoufi. 2014. Measurement techniques to monitor and control fluidization quality in fluidized bed dryers: A review. Drying Technology 32: 1005-1051.
3. Akpinar, E. K., and S. Toraman. 2016. Determination of drying kinetics and convective heat transfer coefficients of ginger slices. Heat and Mass Transfer 52 (10): 2271-2281.
4. Amiri Chayjan, R., and M. Kaveh. 2014. Physical parameters and kinetic modeling of fix and fluid bed drying of terebinth seeds. Journal of Food Processing and Preservation 38: 1307-20.
5. Amiri Chayjan, R., M. Kaveh, and S. Khayati. 2017. Modeling some thermal and physical characteristics of terebinth fruit under semi industrial continuous drying. Journal of Food Measurement and Characterization 11: 12-23.
6. Bebartta, J. P., N. R. Sahoo, S. K. Dash, M. K. Panda, and U. S. Pal. 2014. Kinetics modeling and moisture diffusivity of onion slices in fluidized bed drying. Journal of Food Processing and Preservation 38 (1): 193-199.
7. Doymaz, I., H. Demir, and A. Yildirim. 2015. Drying of quince slices: effect of pretreatments on drying and Rehydration Characteristics. Chemical Engineering Communication 202 (10): 1271-1279.
8. Foroughi-dahr, M., M. Golmohammadi, R. Pourjamshidiyan, M. Rajabi-hamaneh, and S. J. Hashemi. 2015. On the Characteristics of thin layer drying models for intermittent drying of rough rice. Chemical Engineering Communication 202 (8): 1024-1035.
9. Gharibi, H., A. Hossein Mahvi, R. Nabizadeh, H. Arabilibeik, M. Yunesian, and M. H. Sowlat. 2012. A novel approach in water quality assessment based on fuzzy logic Journal of Environmental Management 112: 87-95.
10. Hasanipanah, M., D. J. Armaghani, H. Khamesi, S. B. Amnieh, and S. Ghoraba. 2016. Several non‑linear models in estimating air‑overpressure resulting from mine blasting. Engineering with Computer 32 (3): 441-455.
11. Jafari, S. M., M. Ganje, D. Dehnad, and V. Ghanbari. 2016. Mathematical, fuzzy logic and artificial neural network modeling techniques to predict drying kinetics of onion. Journal of Food Processing and Preservation 40 (2): 329-339.
12. Kaleta, A., K. Gornicki, R. Winiczenko, and A. Chojnacka. 2013. Evaluation of drying models of apple (var. Ligol) dried in a fluidized bed dryer. Energy Conversion and Management 67: 179-185.
13. Kantrong, H., A. Tansakul, and G. S. Mittal, 2014. Drying characteristics and quality of shiitake mushroom undergoing microwave-vacuum drying and microwave-vacuum combined with infrared drying. Journal of Food Science and Technology 51 (12): 3594-3608.
14. Kaveh, M., and R. Amiri Chayjan. 2017. Modeling thin-layer drying of turnip slices under semi-industrial continuous band dryer. Journal of Food Processing and Preservation 41 (2): e12778.
15. Kaveh, M., R. Amiri Chayjan, and A. M. Nikbakht. 2017. Mass transfer characteristics of eggplant slices during length of continuous band dryer. Heat and Mass Transfer 53: 2045-2059.
16. Khanali, M., A. Banisharif, and Sh. Rafiee. 2016. Modeling of moisture diffusivity, activation energy and energy consumption in fluidized bed drying of rough rice. Heat and Mass Transfer 52 (11): 2541-2549.
17. Khoshtaghaza M. H., H. Darvishi, and S. Minaei. 2015. Effects of microwave- fluidized bed drying on quality, energy consumption and drying kinetics of soybean kernels. Journal of Food Science and Technology 52 (8): 4749-4760.
18. Kouchakzadeh, A. 2014. Drying kinetic and shrinkage circumstance of Persian shallot bulb. Agriculture Engineering International. CIGR Journal 16 (2): 176-180.
19. Mahani, M. N. Z., and M. H. Aghkhani. 2016. The effect of slicing type on drying kinetics and quality of dried carrot. Journal of Agricultural Machinery 6 (1): 224-235. (In Farsi).
20. Mohamed, M. T. 2011. Performance of fuzzy logic and artificial neural network in prediction of ground and air vibrations. International Journal of Rock Mechanics and Mining Sciences 48: 845-851.
21. Momenzadeh, L., A. Zomorodian, and D. Mowla. 2011. Experimental and theoretical investigation of shelled corn drying in a microwave-assisted fluidized bed dryer using artificial neural network. Food and Bioproducts Processing 89: 15-21.
22. Murthy, T. P. K., and B. Manohar. 2014. Hot air drying characteristics of mango ginger: Prediction of drying kinetics by mathematical modeling and artificial neural network. Journal of Food Science and Technology 51 (12): 3712-3721.
23. Muzzammil, M., and M. Ayyub, 2010. ANFIS-based approach for scour depth prediction at piers
in non-uniform sediments. Journal of Hydroinformatics 12 (3): 303-317.
24. Nazghelichi, T., M. H. Kianmehr, and M. Aghbashlo. 2011. Prediction of carrot cubes drying kinetics during fluidized bed drying by artificial neural network. Journal of Food Science and Technology 48 (5): 542-550.
25. Oberoi, D. P. S., and D. S. Sogi. 2015. Drying kinetics, moisture diffusivity and lycopene retention of watermelon pomace in different dryers. Journal of Food Science and Technology 52 (11): 7377-7384.
26. Puspasari, I., M. Z. M. Talib, W. R. W. Daud, and S. M. Tasirin. 2012. Drying kinetics of oil palm frond particles in an agitated fluidized bed dryer. Drying Technology 30: 619-630.
27. Samadi, S. H., B. Ghobadian, G. Najafi, A. Motevali, and S. Faal. 2013. Drying of apple slices in combined heat and power (CHP) dryer: Comparison of mathematical models and neural networks. Chemical Product and Process Modeling 8 (1): 41-52.
28. Sarimeseli, A., M. A. Coskun, and M. Yucceer. 2014. Modeling microwave drying kinetics of thyme (Thymus Vulgaris L.) leaves using ANN methodology and dried product quality. Journal of Food Processing and Preservation 38 (1): 558-564.
29. Sharma, G. P., S. Prasad, and V. K. Chahar. 2009. Moisture transport in garlic cloves undergoing microwave-convective drying. Food and Bioproducts Processing 87: 11-16.
30. Silva, B. G. D., A. M. F. Fileti, and O. P. Taranto. 2015. Drying of Brazilian pepper-tree fruits (Schinus terebinthifolius Raddi): development of classical models and artificial neural network approach. Chemical Engineering Communication 202: 1089-1097.
31. Tavakolipour, H., M. Mokhtarian, and A. Kalbasi-Ashtari. 2014. Intelligent monitoring of zucchini drying process based on fuzzy expert engine and ANN. Journal of Food Process Engineering 37 (5): 474-481.
32. Zadeh, L. 1965. Fuzzy sets, Inform. Control 8: 338-353.
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