Document Type : Short Paper

Authors

Faculty of Chemical Engineering, Babol Noshirvani University of Technology, Babol, Mazandaran, Iran

Abstract

Introduction: Rice is one of the most important cereals and is the second-highest worldwide production after wheat and also is a good source of nutrients for humans. It plays an important role in the feeding of the many parts of the world including Iran. The harvested paddy rice has the high initial moisture content of nearly 25-28% (wet basis) that caused corruption. Therefore, in order to prevent corruption and safe storage, it must be dried to 10-13% moisture content. Drying is one of the oldest methods of preserving food and agricultural products that used to increase the food’s storage time. There are several methods for drying paddy rice that none of them are ideal and have several advantages and disadvantages that one of them that recently the use of it has been increased is microwave drying. Microwave drying uses electromagnetic radiations with the frequency range of 300 MHz to 300 GHz and the wavelengths of 1-0.01m. In microwave drying due to better energy concentration, moisture is removed more quickly so the drying time decreases. Due to the complex relationship between input and output variables in the drying process, the selection of the model that can estimate the drying behavior of the products is difficult. Hence, the use of intelligent modeling methods such as neural networks is the best choice.
 
Materials and methods: In this research, in order to investigate the effect of microwave power on kinetics of rice drying, head rice yield and effective diffusivity coefficient of moisture, a continuous type of domestic microwave dryer ( DEM-281 QOT-PW) were used. This dryer has a microwave radiation chamber where the samples are put on it on the tray that was placed on a digital balance. The experiments were performed at three microwave power levels designated as 270, 360 and 450 W. Also, Shirudi paddy rice was used as the raw material and the drying rice process from the initial moisture content of 21% to the final moisture content of 11% is examined. In this study, the neural network toolbox of MATLAB 2017R was used to model the kinetics of rice drying in the microwave dryers. RBF and MLP have 3 layers including input, hidden and output layers. The input layer has two neurons that show the number of input variables that were time and microwave power and the output layer has one neuron that shows the number of output variables that was MR in this study. 70% and 30% of the data was used for training and testing the network, respectively. To estimate the ANN performance, mean square error (MSE) and the coefficient of determination (R2) was used.
 
Results and discussion: The maximum and minimum drying time was 42 and 20 minutes in 270 and 450 watts, respectively. Also, the maximum and minimum effective diffusivity coefficient of moisture were 4.17 * 10^-9 and 1.82* 10^-9 in 450 and 270 watts, respectively. RBF network with Guassian transfer function and high neurons number and MLP network with Levenberg-Marquardt ( LM) learning algorithm and tan-sigmoid (tansig) transfer function with low neurons number were able to model the kinetics of drying as well as. In general, the drying time and head rice yield decreased but the effective diffusivity coefficient of moisture increased by increasing the microwave power. Also drying at different microwave power did not affect rice color and quality.  The results of the modeling of rice drying by using two different neural networks including MLP and RBF demonstrated that the MLP network with Levenberg-Mrrqurdt (LM) learning algorithm and tan-sigmoid (tansig) transfer function has the better performance than the RBF network with Gussian transfer function and the error and the correlation coefficient in MLP are less and higher than the RBF, respectively.  

Keywords

امیری چایچان، ر.، خوش تقاضا، م.، منتظر، غ.، مینایی، س. و علیزاده، م. 1388. تخمین ضریب تبدیل شلتوک با استفاده از شبکه‌های عصبی مصنوعی در خشک‌کردن بستر سیال. مجله علوم و فنون کشاورزی و منابع طبیعی . 13 (48): 298-285.
تهوری، ع. 1395. پیش‌بینی خواص مختلف آب‌های طبیعی با استفاده شبکه عصبی مصنوعی. پایان‌نامه کارشناسی ارشد مهندسی شیمی، دانشگاه صنعتی نوشیروانی بابل.
جعفری، ح.، کلانتری، د و آزادبخت، م. 1394. بررسی نرخ تغییر رطوبت و درصد شکستگی دانه‌های شلتوک با استفاده از خشک‌کن مایکروویو. فناوری‌‌های نوین غذایی. 2 (4) : 63-74 .
خوش تقاضا، م.، حسین زاده سامانی، ب.، فیاضی، ا. و امیر نجات، ح. 1395. پیش‌بینی محتوای رطوبتی خشک شدن لایه نازک قارچ خوراکی به-کمک شبکه‌های عصبی مصنوعی پس انتشار. علوم و صنایع غذایی. 13(50): 182-171.
کلانتری، د.، جعفری، ح. 1395. مقایسه پارامترهای خشک شدن و خصوصیات کیفی شلتوک طارم هاشمی با استفاده از مایکروویو جریان مداوم و مایکروویو خانگی. فناوری‌های نوین غذایی، 3 (12): 88-77.
کلیکانلو, و.، رحمتی، محمد هاشم.، علیزاده, محمدرضا. و پورباقر, رقیه. 1396. اثر دبی و دمای هوای ورودی بر ویژگی های تبدیل و زمان خشک شدن سه رقم شلتوک در خشک کن بسترسیال با چرخه بسته. تحقیقات غلات، 6 (3): 395-385.
مختاریان، م.، کوشکی، ف.1391. تخمین پارامترهای خشک کردن گوجه فرنگی با کمک شبکه های عصبی مصنوعی. پژوهش و نوآوری در علوم و صنایع غذایی، (1)1: 74-61.
یوسفی، ع.، قاسمیان، ن. و سالاری، ا. 1396. مدل‌سازی سینتیک خشک‌کردن برش‌های لیموترش به‌روش تابش مادون قرمز با استفاده از شبکه عصبی هیبریدی. فناوری‌‌های نوین غذایی، 5 (1):105-91.
Akin, D. & Akba, B. 2010. A neural network (NN) model to predict intersection crashes based upon driver, vehicle and roadway surface characteristics. Sci. Res. Essays, 5(19): 2837-2847.
Alibas, I. 2014. Mathematical modeling of microwave dried celery leaves and determination of the effective moisture diffusivities and activation energy. Food Science and Technology, 34(2): 394-401.
Aquerreta, J., Iguaz, A., Arroqui, C., & Virseda, P. 2007. Effect of high temperature intermittent drying and tempering on rough rice quality. J. of Food Engineering, 80: 611-618.
ASAE Standards. 1999. D245.5. Moisture relationship of plant based agricultural products (46th Ed.). St. Joseph, Mich.: ASAE.
Azadbakht, M., Aghili, H., Ziaratban, A. & Torshizi, M.V. 2017. Application of artificial neural network method to exergy and energy analyses of fluidized bed dryer for potato cubes. Energy, 120: 947-958.
Cao, C. & Wang, X.B. 2002. Automatic control of grain driers. Modernizing Agric, 2: 40-44.
Darvishi, H., Khoshtaghaza, M.H., Najafi, G. & Zarein, M. 2013. Characteristics of sunflower seed drying and microwave energy consumption. International Agrophysics, 27(2): 127-132.
Firouzi, F. & Alizadeh, M.R. 2013. An investigation of the effects of harvesting time and milling moisture content of paddy on the quality of milled rice. International Journal of Biosciences, 3 (10): 133-138.
Jafari, H., Kalantari, D. & Azadbakht, M. 2018. Energy consumption and qualitative evaluation of a continuous band microwave dryer for rice paddy drying, Energy, 142:.647-654.
Scala, K.D., Meschino, G., Vega-Galvez, A., Lemus-Mondaca, R., Roura, S. & Mascheroni, R. 2013. An artificial neural network model for prediction of quality characteristics of apples during convective dehydration. Food Science and Technology, 33(3): 411-416.
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.
Hemis, M., Choudhary, R. & Watson, D.G., 2012. A coupled mathematical model for simultaneous microwave and convective drying of wheat seeds. Biosystems engineering, 112(3): 202-209.
Kalra, R., Deo, M.C., Kumar, R. and Agarwal, V.K. 2005. RBF network for spatial mapping of wave heights. Marine Structures, 18(3): 289-300.
Karaaslan, S.N. & Tuncer, I.K. 2008. Development of a drying model for combined microwave–fan-assisted convection drying of spinach. Biosystems Engineering, 100(1): 44-52.
Kouchakzadeh, A. & Shafeei, S., 2010. Modeling of microwave-convective drying of pistachios. Energy Conversion and Management, 51(10): 2012-2015.
Maskan, M. 2000. Microwave/air and microwave finish drying of banana. Journal of food engineering, 44(2): 71-78.
Minaei, S., Rohi, G.R., & Alizadeh, M.R. 2003. Effect of rice crop parameters and dryer on paddy milling waste and hardness. In Second national symposium on losses of agricultural products. Tehran.
Momenzadeh, L., Zomorodian, A., & Mowla, D. 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(1), 15-21.
Motevali, A., Minaei, S., Banakar, A., Ghobadian, B. & Khoshtaghaza, M.H. 2014. Comparison of energy parameters in various dryers. Energy Conversion and Management, 87:711-725.
Niamnuy, C., Kerdpiboon, S. & Devahastin, S. 2012. Artificial neural network modeling of physicochemical changes of shrimp during boiling. LWT-Food Science and Technology, 45(1):110-116.
Rabha, D.K., Muthukumar, P. and Somayaji, C. 2017. Experimental investigation of thin layer drying kinetics of ghost chilli pepper (Capsicum Chinense Jacq.) dried in a forced convection solar tunnel dryer. Renewable energy, 105: 583-589.
Rad, S.J., Kaveh, M., Sharabiani, V.R. & Taghinezhad, E. 2018. Fuzzy logic, artificial neural network and mathematical model for prediction of white mulberry drying kinetics. Heat and Mass Transfer, 1-14.
Salehi, F., Abbasi Shahkoh, Z. & Godarzi, M. 2015. Apricot osmotic drying modeling using genetic algorithm-artificial neural network. Journal of Innovation in food Science and Technology, 7: 65-76.
Therdthai, N. & Zhou, W. 2009. Characterization of microwave vacuum drying and hot air drying of mint leaves (Mentha cordifolia Opiz ex Fresen). Journal of Food Engineering, 91(3): 482-489.
Yadollahnia, A. R. 2006. A thin layer drying model for paddy dryer, Msc thesis, University of Tehran, Karaj, Iran. (In Farsi)
Zhao, P., Zhong, L., Zhu, R., Zhao, Y., Luo, Z. & Yang, X. 2016. Drying characteristics and kinetics of Shengli lignite using different drying methods. Energy Conversion and Management, 120: 330-337.
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