Affiliation:
1. Biomaterials and Transport Phenomena Laboratory, University of Yahia Fares Medea
2. Faculty of Technology, University of Médéa, LBMPT Laboratory
3. Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Médéa
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.
Publisher
Croatian Society of Chemical Engineers/HDKI
Subject
General Chemical Engineering,General Chemistry
Cited by
3 articles.
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