Original scientific paper

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, Algeria
Salah Hanini ; Biomaterials and Transport Phenomena Laboratory, University of Yahia Fares, Médéa, Algeria
Maamar Laidi orcid.org/0000-0002-8977-9895 ; Biomaterials and Transport Phenomena Laboratory, University of Yahia Fares, Médéa, Algeria
Ahmed Rezrazi ; Biomaterials and Transport Phenomena Laboratory, University of Yahia Fares, Médéa, Algeria

Abstract

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.

Keywords

artificial neural network; fruits; solar drying; moisture content; drying rate

Hrčak ID:

257538

URI

https://hrcak.srce.hr/257538

Publication date:

18.5.2021.

Article data in other languages: croatian

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