Artificial neural networks based computational and experimental evaluation of thermal and drying performance of partially covered PVT solar dryer
Yükleniyor...
Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institution of Chemical Engineers
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
This study proposes a mixed-mode dryer with a semi-transparent photovoltaic thermal (PVT) collector for the assessment of drying and thermal performance using computational and experimental findings. The thermal behavior and fluid flow characteristics have been analyzed to optimize the air flow rate in the PVT solar dryer by considering three different inlet velocities of 0.048 m/s (Case 1), 0.096 m/s (Case 2), and 0.144 m/s (Case 3). The temperature distribution is obtained more uniformly for the PVT collector and dryer cabin in Case 2. The results of the investigation show that Case 3 has a positive impact on the PVT solar dryer performance. In numerical and experimental methods, the enhanced thermal efficiency is attained as 30.78% and 29.78% for Case 2, and 33.20% and 31.14% for Case 3, respectively, in comparison to Case 1. Case 3 has improved Reynolds and Nussselt numbers by 3.06 and 2.45 times, respectively compared to Case 1. Experimental results varied by 2.24 to 4.90% from simulated outcomes obtained from CFD. The machine learning approach of ANN has been implemented with different hidden layers network models to choose the best drying conditions by predicting the drying performance parameters.
Açıklama
Anahtar Kelimeler
ANN, Computational fluid dynamics, Partially covered solar dryer, Photovoltaic thermal system
Kaynak
Process Safety and Environmental Protection
WoS Q Değeri
N/A
Scopus Q Değeri
Q1
Cilt
183
Sayı
Künye
Gupta, A., Das, B., Arslan, E., Das, M., Koşan, M. ve Can, Ö. F. (2024). Artificial neural networks based computational and experimental evaluation of thermal and drying performance of partially covered PVT solar dryer. Process Safety and Environmental Protection, 183, 1170-1185.
