Nonlinear model of a rice drying process using neural networks

  • José Aldemar MUÑOZ Universidad del Tolima
  • Carlos Arturo SÁNCHEZ Universidad del Tolima
  • Helmer MUÑOZ Universidad del Tolima/Universidad del Sinú https://orcid.org/0000-0002-2445-6585
Keywords: drying, rice, modeling, neural networks, nonlinear.

Abstract

Background: The production quality of rice is highly depended on the drying process as drying weakens the rice kernel. A look at the production process of rice in the industry was taken. The drying of rice influences the storage capacity of the grain, the energy consumption, the final mass of the grain and the percentage of whole grains at the end of the process. Objective: The main objective was to analyse the drying of rice by making an artificial neural network to model and simulate it. Methods: The modeling of a rice drying process using neural networks was presented. These models are suitable to be used in combination with model-based control strategies in order to improve the drying process. The implementation, preprocessing and data retrieval for the design of an artificial neural system was analyzed. Controlling the drying factors is of major importance. Feedforward and dynamic neural networks were compared based on their performance. Results: It was concluded that when some part of the dataset is given as training, even with one dataset, a back-propagation network simulates very well the other parts of the drying curve. It can be said that the approximations done by the networks to obtain a nonlinear model of the rice drying process are quiet good. Conclusions: Firstly, because of the too little data available for training, the networks were not as good as expected. More data is needed to realy have a powerfull network capable of approximated very well the drying curve. Secondly, a backpropagation network can be a good solution for modelling and for use in a controller if more data is available, in contrast a linear network gave bad results. thirdly, a network with little number of layers is the best option. A perfect mapping from the input to the output is impossible due the differences in each test and the imperfect sensors.

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Author Biographies

José Aldemar MUÑOZ, Universidad del Tolima

Agroindustrial engineering

Carlos Arturo SÁNCHEZ, Universidad del Tolima

Agroindustrial engineering

Helmer MUÑOZ, Universidad del Tolima/Universidad del Sinú

Agroindustrial Engineering / Public Accounting Program

References

Garreiro JM, Kahan S, Zecchi B, Gerla P, Clavijo L, Rodríguez A, editors. A SIMPLE MODEL FOR RICE GRAINS IN A

DEEP-BED DRYER. 4th MERCOSUR Congress on Process Systems Engineering: 2nd MERCOSUR Congress on Chemical Engineering : Proceedings of ENPROMER 2005; 2005; Rio de Janeiro. 1 p.

Brooker DB, Bakker-Arkema FW, Hall CW. Drying cereal grains. 4 ed. USA: American Association of Cereal Chemists; 1992.

Eckert ERG, Drake Jr RM. Analysis of heat and mass transfer. 1987.

Thompson T, Peart R, Foster G. Mathematical simulation of corn drying—a new model. Transactions of the ASAE.

;11(4):582-0586.

Stone M, Kranzler G. A microprocessor controlled thin-layer dryer [Down-draft-cross-flow design]. Paper-American Society of Agricultural Engineers (Microfiche collection)(USA) No fiche No 81-5028. 1981.

Wafler P, Warnock W. A microprocessor controlled crossflow grain dryer test unit [Monitors dry bulb and dew point temperatures, hardware, software]. Microfiche collection. 1982.

Mittal G, Otten L. Microprocessor controlled low-temperature corn drying system. Agricultural Systems. 1983;10(1):1-19.

Omid M, Yadollahinia A, Rafiee S, editors. A thin-layer drying model for paddy dryer. Proc of the International conference on Innovations in Food and Bioprocess Technologies, AIT, Pathumthani, Thailand, 12th; 2006. 202-11 p.

Liu X, Chen X, Wu W, Peng G. A neural network for predicting moisture content of grain drying process using genetic algorithm. Food Control. 2007;18(8):928-33.

Amiri Chayjan R, Moazez Y. Estimation of paddy equilibrium moisture sorption using ANNs. Journal of Applied Sciences. 2008;8:346-51.

Freeman JA, Skapura DM. Algorithms, applications, and programming techniques: Addison-Wesley Publishing Company, USA; 1991. 414 p.

Demuth H, Beale M. Neural Network Toolbox For Use with Matlab--User’S Guide Version 3.0. 1993.

Thyagarajan T, Panda R, Shanmugan J, Rao V, Ponnavaikko M. Development of ANN model for non-linear drying process. Drying technology. 1997;15(10):2527-2540.

Tohidi M, Sadeghi M, Mousavi SR, Mireei SA. Artificial neural network modeling of process and product indices in deep bed drying of rough rice. Turkish Journal of Agriculture and Forestry. 2012;36(6):738-748.

Beigi M, Torki-Harchegani M, Tohidi M. Experimental and ANN modeling investigations of energy traits for rough rice drying. Energy. 2017;141:2196-205.

Chokphoemphun S, Chokphoemphun S. Moisture content prediction of paddy drying in a fluidized-bed drier with a vortex flow generator using an artificial neural network. Applied Thermal Engineering. 2018;145:630-636.

Alam MA, Saha CK, Alam MM, Ashraf MA, Bala BK, Harvey J. Neural network modeling of drying of rice in BAU-STR dryer. Heat and Mass Transfer. 2018:1-9.

Thant PP, Robi P, Mahanta P. ANN Modelling for Prediction of Moisture Content and Drying Characteristics of Paddy in Fluidized Bed. International Journal of Engineering and Applied Sciences (IJEAS).3(3):118-23

Published
2018-12-18
How to Cite
MUÑOZ J. A., SÁNCHEZ C. A., & MUÑOZ H. (2018). Nonlinear model of a rice drying process using neural networks. Vitae, 25(3), 120-127. https://doi.org/10.17533/udea.vitae.v25n3a02
Section
Foods: Science, Engineering and Technology