ANN-based Approach to Model MC/DR of Some Fruits Under Solar Drying

被引：3
|
作者：
Sadadou, A. [1 ]
Hanini, S. [1 ]
Laidi, M. [1 ]
Rezrazi, A. [1 ]
机构：
[1] Univ Yahia Fares, Biomat & Transport Phenomena Lab, Medea, Algeria
来源：
KEMIJA U INDUSTRIJI-JOURNAL OF CHEMISTS AND CHEMICAL ENGINEERS | 2021年 / 70卷 / 5-6期
关键词：
Artificial neural network; fruits; solar drying; moisture content; drying rate; ARTIFICIAL NEURAL-NETWORKS; PERFORMANCE; PREDICTION; KINETICS; DRYER;
D O I：
10.15255/KUI.2020.050
中图分类号：
O6 [化学];
学科分类号：
0703 ;
摘要：
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.
引用
收藏
页码：233 / 242
页数：10
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