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Title
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Modeling Drying Properties of Pistachio Nuts, Squash and Cantaloupe Seeds under Fixed and Fluidized Bed Using Data-Driven Models and Artificial Neural Networks
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Type
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JournalPaper
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Keywords
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drying, artificial neural network, effective moisture diffusivity, specific energy consumption, fluidized bed
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Abstract
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This paper presents the application of feed forward and cascade forward neural networks to model the nonlinear behavior of pistachio nut, squash and cantaloupe seeds during drying process. The performance of the feed forward and cascade forward ANNs was compared with those of nonlinear and linear regression models using statistical indices, namely mean square error (𝑀𝑆𝐸), mean absolute error (𝑀𝐴𝐸), standard deviation of mean absolute error (SDMAE) and the correlation coefficient (𝑅2). The best neural network feed forward backpropagation topology for the prediction of effective moisture diffusivity and energy consumption were 3-3-4-2 with the training algorithm of Levenberg-Marquardt (LM). This structure is capable to predict effective moisture diffusivity and specific energy consumption with 𝑅2= 0.9677 and 0.9716, respectively and mean-square error (𝑀𝑆𝐸) of 0.00014. Also the highest 𝑅2 values to predict the drying rate and moisture ratio were 0.9872 and 0.9944 respectively.
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Researchers
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Behrooz Khezri (Third Researcher), Reza Amiri Chayjan (Second Researcher), Mohammad Kaveh (First Researcher)
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