Maleki, B.; Ghazvini, M.; Ahmadi, M.H.; Maddah, H.; Shamshirband, S. Moisture Estimation in Cabinet Dryers with Thin-Layer Relationships Using a Genetic Algorithm and Neural Network. Mathematics2019, 7, 1042.
Maleki, B.; Ghazvini, M.; Ahmadi, M.H.; Maddah, H.; Shamshirband, S. Moisture Estimation in Cabinet Dryers with Thin-Layer Relationships Using a Genetic Algorithm and Neural Network. Mathematics 2019, 7, 1042.
Maleki, B.; Ghazvini, M.; Ahmadi, M.H.; Maddah, H.; Shamshirband, S. Moisture Estimation in Cabinet Dryers with Thin-Layer Relationships Using a Genetic Algorithm and Neural Network. Mathematics2019, 7, 1042.
Maleki, B.; Ghazvini, M.; Ahmadi, M.H.; Maddah, H.; Shamshirband, S. Moisture Estimation in Cabinet Dryers with Thin-Layer Relationships Using a Genetic Algorithm and Neural Network. Mathematics 2019, 7, 1042.
Abstract
Nowadays industrial dryers are used instead of traditional methods for drying. In designing dryers suitable for controlling the process of drying and reaching a high quality product, it is necessary to predict the instantaneous moisture loss during drying. For this purpose, ten mathematical-experimental models with a neural network model based on the kinetic data of pistachio drying are studied. The data obtained from the cabinet dryer will be evaluated at four temperatures of inlet air and different air velocities. The pistachio seeds will be 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 are divided into three parts: educational (60%), validation (20%) and test (20%). Finally, the best mathematical-experimental model using genetic algorithm and the best neural network structure for predicting instantaneous moisture are selected based on the least squared error and the highest correlation coefficient.
Keywords
cabinet dryer; genetic algorithm; neural network; temperature; air velocity; moisture
Subject
Computer Science and Mathematics, Applied Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
