Mushroom Drying in Air Heated Solar Collector Drying System and Modeling of Drying Performance with Artificial Neural Network

Year 2018, Volume: 11 Issue: 1, 23 - 30, 24.04.2018

Mehmet Daş , Ebru Kavak Akpınar

Abstract

Bu çalışmada, mantarın kuruma özelliğini tespit etmek üzere hava ısıtmalı güneş kollektörlü (HIGK) kurutucu tasarlanmıştır. Deneylerde ince dilimlenmiş mantar örnekleri kullanılmıştır. Kollektör giriş ve çıkış havaları sıcaklıkları, kurutma odası giriş ve çıkış havaları sıcaklıkları, çevre sıcaklığı, ışınım, hava hızı ve kuruma hızı; kuruma özelliğini etkileyen parametreler olarak düşünülmüştür. Elde edilen sonuçlar, kurutma zamanının fonksiyonu olarak sunulmuştur. Deneylerden elde edilen nem içeriği (Nİ), ayrılabilir nem oranı (ANO) ve kurutma hızı (KH) değerleri, Logsig Aktivasyon fonksiyonu ve Backpropagation öğrenme fonksiyonu kullanılarak 3 katmanlı yapay sinir ağları (YSA) ile modellenmiştir. Geliştirilen modelin istatistiksel geçerliliğinin belirlenmesinde ortalama kareli hata (OKH) kullanılmıştır. Sonuç olarak mevcut kurutma şartları için oluşturulan YSA ile mantarın kuruma davranışları başarılı bir şekilde tahmin edilmiştir.

Keywords

Güneş kollektörü, Gıda kurutma, Tahminsel model, Yapay sinir ağları

Mushroom Drying in Air Heated Solar Collector Drying System and Modeling of Drying Performance with Artificial Neural Network

Abstract

In this study, an air heated solar collector (AHSC) dryer was designed to determine the drying characteristics of the mushroom. In the experiments thinly sliced mushroom samples were used. Collector inlet and outlet air temperatures, drying chamber inlet and outlet air temperatures, ambient temperature, radiation, air velocity and drying rate were considered as parameters affecting the drying feature. The results obtained were presented as a function of drying time. Moisture content (MC), moisture ratio (MR) and drying rate (DR) values obtained from the experiments were modeled with 3-layer artificial neural network (ANN) using Logsig Activation function and Backpropagation learning function. Mean square error (MSE) was used to determine of the statistical validity of the developed model. As a result, drying behavior of mushroom was successfully predicted by ANN for existing drying conditions.

Keywords

Solar Collector, Food drying, Predictive model, Artificial neural networks

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