Hikmet DOĞAN, Mustafa AKTAŞ, Seyfi ŞEVİK, M. Bahadır ÖZDEMİR

Güneş destekli ısı pompalı bir kurutucuda mantarın kuruma davranışlarının yapay sinir ağı kullanılarak modellenmesi

Kurutucu, güneş enerjili ve güneş enerjisi destekli ısı pompalı olmak üzere ayrı ayrı 45 °C ve 55 °C kurutma havası sıcaklığı 0.9 m s-1 ve 1.2 m s-1 hava hızlarında mantar kurutularak test edilmiştir. Deneylerden elde edilen nem içeriği (MC), ayrılabilir nem oranı (MR) ve kurutma hızı (DR) değerleri Levenberg-Marquardt (LM) geri yayılım öğrenme algoritması ve Fermi transfer fonksiyonu kullanılarak yapay sinir ağları (YSA) ile modellenmiştir. Geliştirilen modelin istatistiksel geçerliliğinin belirlenmesinde kullanılan çoklu belirleme katsayısı (R2), ortalama hata kareleri karekökü (RMSE), ve ortalama mutlak hata yüzdesi (MAPE) istatistik değerleri kullanılmıştır. R2, RMSE ve MAPE sırasıyla MC için 0.998, 0.0015608, 0.1940471, MR için 0.998, 0.0000971, 0.2214687 ve DR için 0.993, 0.0000075, 0.8627478 olarak elde edilmiştir. Böylece, farklı kurutma şartları için bu modelleme ile mantarın kuruma davranışları başarılı bir şekilde analiz edilebilir.

Modeling of drying behaviors of mushroom in a solar assisted heat pump dryer by using artificial neural network

Dryer was tested by drying mushroom with solar energy and solar assisted heat pump separately at 45 °C and 55 °C drying air temperature and 0.9 m s-1 and 1.2 m s-1 drying air velocities. Moisture content (MC), moisture ratio (MR) and drying rate (DR) which were obtained from experiments were modeled by using Levenberg-Marquardt (LM) the back- propagation learning algorithm and fermi transfer function with artificial neural networks (ANNs). The coefficient of multiple determination (R2), the root means square error (RMSE) and the mean absolute percentage error (MAPE) were used for the determination of statistical validity of the developed model. R2, RMSE and MAPE were determined for MC 0.998, 0.0015608, 0.1940471, MR 0.998, 0.0000971, 0.2214687 and DR 0.993, 0.0000075, 0.8627478 respectively. In this way, drying behaviors of mushroom can be analyzed successfully for different drying conditions with this modeling.

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