Comparison of Artificial Neural Networks and Fuzzy Logic Methods in the Hazelnut Shell Drying Process

Abstract

Drying and storing food products have been widely used techniques for extending shelf life for many years. In recent times, there has been a focus on the drying of food residues, similar to the preservation of foods by drying. The process of drying a product involves reducing the moisture content within the product. However, the devices established to reduce moisture content are often costly and rely heavily on experience-based systems for determining the drying ratio. Therefore, in recent years, there has been significant interest in the mathematical modeling of drying processes and the creation of a model for system behavior using artificial intelligence methods. This study aims to model the drying process of hazelnut shells using artificial intelligence techniques, specifically artificial neural networks and fuzzy logic methods. The proximity of the models created to the experimental results of the drying ratio is examined.