Experimental evaluation and modeling of paddy rice drying in convective dryer by artificial neural network

Document Type : Research Article

Authors

1 Master student, Faculty of Chemical Engineering, Babol Noshirvani University of Technology.

2 Professor, Faculty of Chemical Engineering, Babol Noshirvani University of Technology.

3 Phd student, Faculty of Chemical Engineering, Babol Noshirvani University of Technology.

Abstract

Harvested rice has a high moisture content and it should be reduced to avoid corruption. In this study, the hot air drying was used to evaluate the kinetics of drying. This research was carried out under three variables including hot air velocity ( 0.65 , 0.8 m/s), hot air temperature ( 50 , 65 0C), and final moisture content (11, 13%). Modeling of rice drying was done by a multilayer perceptron artificial neural network. In order to evaluate the performance of training algorithms and transfer functions in predicting the drying behavior of paddy rice, three algorithms including Levenberg Marquardt, Resilient Bach Propagation and Scale Conjugate Gradient and two transfer functions including logsig and tansig were used. The results showed that the maximum drying time was approximately 8 hours at temperature of 500C, hot air velocity of 0.65 m/s and final moisture content of 11% and the minimum drying time was 1.21 hours at temperature of 650C, hot air velocity of 8 m/s and final moisture content of 13%. In general, the drying time decreased with increasing the temperature, hot air velocity and final moisture content. Also, the results of modeling showed that the levenberg Marquardt training algorithm had the best performance compared to the other algorithms. In general the topology of 3-11-1 with levenberg Marquardt training algorithm and logsig transfer function had the lowest mean square error and the highest correlation coefficient.

Graphical Abstract

Experimental evaluation and modeling of paddy rice drying in convective dryer by artificial neural network

Highlights

  • Obtaining the kinetics of rice drying under different experimental conditions.
  • Predicting the kinetics of rice drying using a multilayer perceptron artificial neural network
  • Optimization the neural network configuration
  • Different transfer functions including logsig and tansig have been evaluated to choose the best one.
  • Evaluating different training algorithm including Levenberg Marquardt, Resilient Bach Propagation and Scale Conjugate Gradient

Keywords

References

[1] Rafiee, S. (1998). An investigation on paddy drying techniques. MS thesis, Department of Agricultural Machinery, University of Tehran, Karaj, Iran, (In Farsi)
[2] Discala, J. K., Meschino, G., Vega-Galvez, A., Lemus- Mondaca,  R., Roura, S.,  Mascheroni, R. (2003). An artificial neural network model for prediction of quality characteristics of apples during convective dehydration. Food Sci. Technol., 33(3), 411-416.
[3] Cao, C., Wang, X. B. (2002). Automatic control of grain driers. Modernizing. Agri., 2, 40-44.
[4] Dayhoff, J. E. (1990) . Neural Network Principles. Prentice – Hall International, U.S.A.
[5] 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.
[6] Satish, S., Setty, Y. P. (2005). Modeling of a continuous fluidized bed dryer using artificial neural networks. Int. Commun. Heat. Mass., 32(3-4), 539-547.
[7] Cubillos, F., Reyes, A. (2003). Drying of carrots in a fluidized bed. II. Design of a model based on a modular neural network approach. Dry. Technol., 21(7), 1185-1196.
[8] 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. Bioprod. Process., 89(1), 15-21.
[9] Cakmak, G., Yildiz, C. (2011). The prediction of seedy grape drying rate using a neural network method. Comput. Electron. Agr., 75, 132-138.
[10] اصغری، م.؛ ابراهیمی، ر.؛ حسین زاده، ب.؛ قنبریان، د. (1396) مدل­سازی پارامترهای کیفی توت­سفید در فرایند خشک شدن با استفاده از شبکه عصبی مصنوعی. مهندسی بیوسیستم ایران، جلد 48، شماره 1، ص 9-18.
[11] یوسفی، ع.؛ قاسمیان، ن.؛ سالاری، ا. (1396) مدل­سازی سینتیک خشک­کردن برش­های لیموترش به­روش تابش مادون قرمز با استفاده از شبکه عصبی هیبریدی. فناوری­­های نوین غذایی، جلد 5، شماره 1، ص 91-105.
[12] 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. Mass. Transfer., 1-14.
[13] Yadollahnia, A. R.(2006). A thin layer drying model for paddy dryer, Msc thesis, University of Tehran, Karaj, Iran. (In Farsi)
[14] Dinani, S.T., Hamdami, N., Shahedi, M., Havet, M. (2014). Mathematical modeling of hot air/electrohydrodynamic (EHD) drying kinetics of mushroom slices. Energ. Convers. Manage., 86, 70-80.
[15] Niamnuy, C., Kerdpiboon, S., Devahastin, S. (2012). Artificial neural network modeling of physicochemical changes of shrimp during boiling. Food. Sci. Technol., 45(1), 110-116.
[16] Azadbakht, M., Aghili, H., Ziaratban, A., Torshizi, M.V. (2016). Application of artificial neural network method to exergy and energy analyses of fluidized bed dryer for potato cubes. Energy., 120, 947-958.
Innovative Food Technologies
Volume 6, Issue 4
August 2019
Pages 497-505

Files

History

  • Receive Date: 07 September 2018
  • Revise Date: 01 November 2018
  • Accept Date: 20 January 2019

Share

How to cite

Statistics