Investigation of Performance of Air Solar Collector Assisted Greenhouse Food Dryer System

Öz

Drying refers to the process of removing water or volatile substances from solids by thermal methods. Drying agricultural products with solar energy is known as one of the oldest food storage methods. In solar drying, the quality of food is seriously reduced due to environmental factors. For this reason, drying with special purpose artificial dryers shortens the drying time and provides a better quality and cleaner product with a long shelf life. In this study, an air solar collector (ASC) supported greenhouse type dryer was designed to improve the quality and performance of food drying. In the study, drying experiments were carried out in the climatic conditions of Elazığ province. After the drying experiments, heat and mass transfer analysis of the product to be dried were carried out. Moisture content, moisture ratio, convective heat transfer coefficient parameters were calculated in the experiments. During the experiments, greenhouse and HGK inlet and outlet temperatures, solar radiation and product weight values were measured at 15-minute intervals. With the support of the HGK, the product drying time of the greenhouse dryer was reduced by 24%. Thus, a faster drying process was achieved. In addition, convective heat transfer coefficient, which is an important parameter in drying processes, was calculated and predictive models were obtained with machine learning (ML) algorithms for this parameter. The aim of this study is to use air solar collectors to improve the performance of greenhouse type food dryers and to produce useful models for convective heat transfer using ML algorithms. In short, a thermodynamic system in which both artificial intelligence and experimental applications will be performed has been obtained. Artificial neural network (ANN) and decision tree (DTA) algorithms were selected as machine learning algorithms. Model results obtained with ML algorithms are compared with experimental results. The error rate between the experimental results and ANN results is 1%, while the error rate between KA results is 7%.

Anahtar Kelimeler

• Greenhouse dryer
• solar collector
• convective heat transfer coefficient
• machine learning

Havalı Güneş Kollektör Destekli Sera Gıda Kurutucu Sisteminin Performansının İncelenmesi

Öz

Kurutma katı maddelerden ısıl yöntemlerle su veya uçucu maddelerin giderilmesi işlemini tanımlamaktadır. Güneş enerjisi ile tarım ürünlerini kurutma, en eski gıda saklama yöntemlerinden birisi olarak bilinmektedir. Güneşte kurutmada çevresel faktörler nedeniyle gıdanın kalitesi ciddi olarak azalmaktadır. Bu nedenle kurutma işleminin özel amaçlı yapay kurutucular ile yapılması hem kuruma süresini kısaltmakta hem de uzun raf ömrüne sahip daha kaliteli ve temiz ürün elde edilmesini sağlamaktadır. Bu çalışmada gıda kurutma kalitesini ve performansını arttırmak için havalı güneş kolektör (HGK) destekli sera tipi bir kurutucu tasarlanmıştır. Çalışmada Elazığ ili iklim şartlarında kurutma deneylere gerçekleştirilmiştir. Kurutma deneyleri sonrası kurutulacak ürünün ısı ve kütle transferi analizleri yapılmıştır. Deneylerde nem içeriği, nem oranı, konvektif ısı transfer katsayısı parametreleri hesaplanmıştır. Deneyler süresince, sera ve HGK giriş ve çıkış sıcaklıkları, güneş ışınımı ve ürün ağırlık değerleri 15 dakikalık periyotlarla ölçülmüştür. HGK desteği ile sera kurutucunun ürün kurutma süresi % 24 oranında azalmıştır. Böylelikle daha hızlı bir kurutma süreci elde edilmiştir. Ayrıca kurutma işlemlerinde önemli bir parametre olan konvektif ısı transfer katsayısı hesaplanmış ve bu parametre için makine öğrenmesi (MÖ) algoritmaları ile tahminsel modeller elde edilmiştir. Bu çalışmanın amacı, sera tipi gıda kurutucuların performansını arttırmak için havalı güneş kollektörü kullanmak ve konventif ısı transferi için MÖ algoritmaları kullanılarak faydalı modellerin üretilmesidir. Kısaca hem yapay zekâ hem de deneysel uygulamaların yapılacağı termodinamik bir sistem elde edilmiştir. Makine öğrenmesi algoritmaları olarak yapay sinir ağı (YSA) ve karar ağacı (KA) algoritmaları seçilmiştir. MÖ algoritmaları ile elde edilen model sonuçları ile deneysel sonuçlar karşılaştırılmıştır. Deneysel sonuçları ile YSA sonuçları arasındaki hata %1 iken, KA sonuçları arasındaki hata %7 dir.

Anahtar Kelimeler

• Sera kurutucu
• güneş kollektörü
• konvektif ısı transfer katsayısı
• makine öğrenmesi

Kaynakça

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