Nazmi İzli, Ahmet Polat, Ferhat Kurtulmuş

Yapay Sinir Ağları Kullanarak Kayısının Farklı Kurutma Yöntemleriyle Kurutulmasında Kuruma Hızı ve Nem Oranı Parametrelerinin Modellenmesi

Bu çalışmada geleneksel (sıcak hava ile), elektrohidrodinamik (EHD) ve EHD-sıcak havakombinasyonu kurutma yöntemleriyle kurutulan kayısının farklı kurutma parametrelerinin kuruma hızı veürünün nem oranı üzerine etkilerinin YSA ile modellenmesi amaçlanmıştır. Farklı transfer fonksiyonları veöğrenme algoritmaları denenerek her bir kurutma yöntemi için en iyi model performansını veren YSA tespitedilmiştir. EHD-sıcak hava kombinasyonu ile kayısı kurutmada kuruma hızı ve nem oranı tahminine ilişkin YSAmodellerinin test verisi üzerindeki belirtme katsayıları 0,96’dan yüksek değerler olarak saptanmıştır. Araştırmabulguları EHD yöntemi ile tarımsal ürün kurutmanın YSA esaslı yöntemlerle modellenebileceğini göstermiştir.

Modeling of Drying Rate and Moisture Ratio Parameters in Drying Apricot with Different Drying Methods Using Artificial Neural Networks

In this study, it was aimed to model the effects of different drying parameters of apricot on drying rate and product moisture ratio using ANN and different drying methods such as traditional (hot air), electrohydradynamic (EHD) and combined EHD-Hot air. The best ANN model performance for each drying method was determined by trying different transfer functions and learning algorithms. The determination coefficients on the test data of the ANN models related to drying rate and moisture ratio estimation in apricot drying with EHD-hot air combination were determined as values higher than 0.96. The research findings showed that the drying of agricultural crops using EHD could be modeled with ANN-based methods.

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