EJPAU 2017. Bakhshipour A. , Zare D. A GENERALIZED ARTIFICIAL NEURAL NETWORK MODEL FOR DEEP-BED DRYING OF PADDY <br>DOI:10.30825/5.ejpau.23.2017.20.1

Electronic Journal of Polish Agricultural Universities (EJPAU) founded by all Polish Agriculture Universities presents original papers and review articles relevant to all aspects of agricultural sciences. It is target for persons working both in science and industry,regulatory agencies or teaching in agricultural sector. Covered by IFIS Publishing (Food Science and Technology Abstracts), ELSEVIER Science - Food Science and Technology Program, CAS USA (Chemical Abstracts), CABI Publishing UK and ALPSP (Association of Learned and Professional Society Publisher - full membership). Presented in the Master List of Thomson ISI.

2017
Volume 20
Issue 1

Topic:

Agricultural Engineering

ELECTRONIC
JOURNAL OF
POLISH
AGRICULTURAL
UNIVERSITIES

Bakhshipour A. , Zare D. 2017. A GENERALIZED ARTIFICIAL NEURAL NETWORK MODEL FOR DEEP-BED DRYING OF PADDY
DOI:10.30825/5.ejpau.23.2017.20.1, EJPAU 20(1), #05.
Available Online: http://www.ejpau.media.pl/volume20/issue1/abs-05.html

A GENERALIZED ARTIFICIAL NEURAL NETWORK MODEL FOR DEEP-BED DRYING OF PADDY
DOI:10.30825/5.EJPAU.23.2017.20.1

Adel Bakhshipour, Dariush Zare
Biosystems Engineering Department, Faculty of Agriculture, Shiraz University, Iran

ABSTRACT

In order to estimate the moisture content variations of paddy in a deep bed dryer, an Artificial Neural Network (ANN) simulation model was developed from a validated partial differential equation (PDE) model. Different ANN structures were developed and evaluated to obtain the best simulation model. Subsequently, the best network was selected based on the highest value of coefficient of determination (R²=0.9979), and the lowest value for the mean squared error (MSE=0.0732).The mean relative deviation between PDE data and the verified network outputs was obtained to be close to zero (MRD=0.51%). The performance of the proposed ANN was also evaluated by a set of experimental data. Good agreement was found between experimental and network predicted values (MRD=8.29%). Results indicated that the developed ANN is capable of predicting drying process with reasonable accuracy.

Key words: Deep bed, Artificial Neural Networks, Modeling, Paddy drying.

Adel Bakhshipour
Biosystems Engineering Department, Faculty of Agriculture, Shiraz University, Iran
Former Ph.D. Student

Dariush Zare
Biosystems Engineering Department, Faculty of Agriculture, Shiraz University, Iran
P.O. Box 7144165186, Shiraz, Iran
Phone: +98 711 6138192; Fax: +98 711 2286104
email: dzare@shirazu.ac.ir

Responses to this article, comments are invited and should be submitted within three months of the publication of the article. If accepted for publication, they will be published in the chapter headed 'Discussions' and hyperlinked to the article.