Izvorni znanstveni članak
https://doi.org/10.15255/KUI.2020.050
ANN-based Approach to Model MC/DR of Some Fruits under Solar Drying
Ahmed Sadadou
; Biomaterials and Transport Phenomena Laboratory, University of Yahia Fares, Médéa, Alžir
Salah Hanini
; Biomaterials and Transport Phenomena Laboratory, University of Yahia Fares, Médéa, Alžir
Maamar Laidi
orcid.org/0000-0002-8977-9895
; Biomaterials and Transport Phenomena Laboratory, University of Yahia Fares, Médéa, Alžir
Ahmed Rezrazi
; Biomaterials and Transport Phenomena Laboratory, University of Yahia Fares, Médéa, Alžir
Sažetak
The aim of this work was to model the moisture content (MC) and drying rate (DR) using artificial neural network (ANN) methodology. Many architectures have been tested and the best topology was selected based on a trial and error method. The dataset was randomly divided into 60, 20, and 20 % for training, test, and validation stage of the ANN model, respectively. The best topology was 10-{29-13}-2 obtained with high correlation coefficient R (%) of {99.98, 98.41} and low root mean square error RMSE (%) (0.36, 6.29) for MC and DR, respectively. The obtained ANN can be used to interpolate the MC and DR with high accuracy.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Ključne riječi
artificial neural network; fruits; solar drying; moisture content; drying rate
Hrčak ID:
257538
URI
Datum izdavanja:
18.5.2021.
Posjeta: 1.449 *