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
相关论文
共 50 条
  • [31] ANN-based estimation model for the preconstruction cost of pavement rehabilitation projects
    Shehab, Tariq
    Blampied, Nigel
    Nasr, Elhami
    Sindhu, Laxmi
    [J]. INTERNATIONAL JOURNAL OF CONSTRUCTION MANAGEMENT, 2024, 24 (08) : 894 - 901
  • [32] ANN-based Internal Model Control strategy applied in the WWTP industry
    Pisa, Ivan
    Morell, Antoni
    Lopez Vicario, Jose
    Vilanova, Ramon
    [J]. 2019 24TH IEEE INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION (ETFA), 2019, : 1477 - 1480
  • [33] ANN-Based Model for the Prediction of the Bond Strength between FRP and Concrete
    Cascardi, Alessio
    Micelli, Francesco
    [J]. FIBERS, 2021, 9 (07)
  • [34] A Novel Comparative Analysis of Solar P&O, ANN-based MPPT Controller under Different Irradiance Condition
    Pavithra, C.
    Dhayalan, R.
    AnandhaKumar, S.
    Dharshan, Y.
    Haridharan, R.
    Vijayadharshini, M.
    [J]. EAI Endorsed Transactions on Energy Web, 2024, 11 : 1 - 6
  • [35] ANN-Based LUBE Model for Interval Prediction of Compressive Strength of Concrete
    Akbari, Mahmood
    Kabir, H. M. Dipu
    Khosravi, Abbas
    Nasirzadeh, Farnad
    [J]. IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF CIVIL ENGINEERING, 2022, 46 (02) : 1225 - 1235
  • [36] ANN-based Model for Aiding Leak Detection in Water Distribution Networks
    Sivapragasam, C.
    Maheswaran, R.
    Venkatesh, Veena
    [J]. ASIAN JOURNAL OF WATER ENVIRONMENT AND POLLUTION, 2008, 5 (03) : 111 - 114
  • [37] Dynamic Bus Travel Time Prediction Using an ANN-based Model
    As, Mansur
    Mine, Tsunenori
    [J]. PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON UBIQUITOUS INFORMATION MANAGEMENT AND COMMUNICATION (IMCOM 2018), 2018,
  • [38] ANN-Based Model for Predicting the Nonlinear Response of Flush Endplate Connections
    Georgiou, Gregory
    Elkady, Ahmed
    [J]. JOURNAL OF STRUCTURAL ENGINEERING, 2024, 150 (05)
  • [39] A study on the ANN-based credit risk prediction model and its application
    Peng, Y
    Tu, XY
    [J]. Artificial Intelligence Applications and Innovations II, 2005, 187 : 459 - 468
  • [40] ANN-Based LUBE Model for Interval Prediction of Compressive Strength of Concrete
    Mahmood Akbari
    H. M. Dipu Kabir
    Abbas Khosravi
    Farnad Nasirzadeh
    [J]. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 2022, 46 : 1225 - 1235
12345