Dynamic Modeling of the Drying Process of Corn Grains using Neural Networks

https://doi.org/10.22146/agritech.44483

Galih Kusuma Aji(1*), Wildan Fajar Bachtiar(2), Henry Yuliando(3), Endy Suwondo(4)

(1) Department of Bioresource Technology and Veterinary, Vocational College, Universitas Gadjah Mada, Jl. Yacaranda, Sekip Unit II, Yogyakarta 55281
(2) Department of Bioresource Technology and Veterinary, Vocational College, Universitas Gadjah Mada, Jl. Yacaranda, Sekip Unit II, Yogyakarta 55281
(3) Department of Agro-industrial Technology, Faculty of Agricultural Technology, Universitas Gadjah Mada, Jl. Flora No. 1 Bulaksumur, Yogyakarta 55281
(4) Department of Agro-industrial Technology, Faculty of Agricultural Technology, Universitas Gadjah Mada, Jl. Flora No. 1 Bulaksumur, Yogyakarta 55281
(*) Corresponding Author

Abstract


This study examines the model development of the drying process of corn grains as a dynamic system. The appropriate use of a dynamic model for the drying process of corn grains could lead to an effective method for optimizing the system. The optimal control strategy can be determined by predicting the future behaviors of the process using a dynamic model. In this work, the dynamic characteristic of the water loss of corn grains during dynamics treatment of temperature in the drying process was measured in a continuous manner using a precise load cell. The nonlinear autoregressive with external input (NARX) neural network is used to identify and develop a model of dynamic characteristics of the drying process of corn grains. Then for model training and validation, the dynamic responses of the rate of water loss of corn grains to drying temperature were used. A three-layered NARX neural network model consists of 1-10-1 number neurons of each layer with two times delay was successfully developed to identify and make a model such a complex system. The developed model showed the accuracy of the rate water loss of corn grains during the drying process with the mean square error (MSE), and coefficient of determination (R-squared) values are 1.88892 x 10-4 and 0.891594 consecutively.


Keywords


Drying temperature; neural networks; drying process; dynamic model; the rate of water loss

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DOI: https://doi.org/10.22146/agritech.44483

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