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EXPERIMENTAL AND NEURAL NETWORK PREDICTION OF THE PERFORMANCE OF A SOLAR TUNNEL DRIER FOR DRYING JACKFRUIT BULBS AND LEATHER
Author(s) -
BALA B.K.,
ASHRAF M.A.,
UDDIN M.A.,
JANJAI S.
Publication year - 2005
Publication title -
journal of food process engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.507
H-Index - 45
eISSN - 1745-4530
pISSN - 0145-8876
DOI - 10.1111/j.1745-4530.2005.00042.x
Subject(s) - environmental science , photovoltaic system , backpropagation , artificial neural network , engineering , computer science , artificial intelligence , electrical engineering
This article presents the field performance of a solar tunnel drier for drying jackfruit bulbs and leather. The drier consists of a transparent plastic‐covered flat‐plate collector and a drying tunnel connected in series to supply hot air directly into the drying tunnel using two direct‐current fans operated by a photovoltaic module. The drier has a loading capacity of 120–150 kg of fruits. Sixteen experimental runs were conducted for drying jackfruit bulbs and leather (eight runs each). The use of a solar tunnel drier led to a considerable reduction in drying time and dried products of better quality in comparison to products dried under the sun. A multilayered neural network approach was used to predict the performance of the solar tunnel drier. Using solar drying data of jackfruit bulbs and leather, the model has been trained using backpropagation algorithm. The prediction of the performance of the drier was found to be excellent after it was adequately trained. It can be used to predict the potential of the drier for different locations, and can also be used in a predictive optimal control algorithm.