Moisture Estimation in Cabinet Dryers with Thin-Layer Relationships Using a Genetic Algorithm and Neural Network

Mathematics

Shahaboddin Shamshirband¹  Heydar Maddah²  Mahyar Ghazvini³  Behzad Maleki⁴  MohammadHossein Ahmadi⁵ 

[1] Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam;Department of Chemistry, Payame Noor University (PNU), P.O. Box, Tehran 19395-3697, Iran;Department of Renewable Energy and Environmental Engineering, University of Tehran, Tehran 1417466191, Iran;Energy Institute of Higher Education, Saveh 39177-67746, Iran;Faculty of Mechanical Engineering, Shahrood University of Technology, Shahrood 3616713455, Iran;

关键词: cabinet dryer;    genetic algorithm;    neural network;    temperature;    air velocity;    moisture;   

DOI  :  10.3390/math7111042

来源: DOAJ

【 摘 要 】

Nowadays, industrial dryers are used instead of traditional methods for drying. When designing dryers suitable for controlling the process of drying and reaching a high-quality product, it is necessary to predict the gradual moisture loss during drying. Few studies have been conducted to compare thin-layer models and artificial neural network models on the kinetics of pistachio drying in a cabinet dryer. For this purpose, ten mathematical-experimental models with a neural network model based on the kinetic data of pistachio drying were studied. The data obtained was from a cabinet dryer evaluated at four temperatures of inlet air and different air velocities. The pistachio seeds were placed in a thin layer on an aluminum sheet on a drying tray and weighed by a scale attached to the computer at different times. In the neural network, data was divided into three parts: Educational (60%), validation (20%) and testing (20%). Finally, the best mathematical-experimental model using a genetic algorithm and the best neural network structure for predicting instantaneous moisture were selected based on the least squared error and the highest correlation coefficient.

【 授权许可】

Unknown