Neural network approach for food temperature prediction during solar drying

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• 作者：P.P. Tripathy ; Subodh Kumar
• 关键词：Solar drying ; Artificial neural network ; Heat diffusion model ; Laplace transform ; Statistical model ; Food temperature prediction
• 刊名：International Journal of Thermal Sciences
• 出版年：2009
• 出版时间：July 2009
• 年：2009
• 卷：48
• 期：7
• 页码：1452-1459
• 全文大小：368 K

文摘

In the present study, the application of artificial neural network (ANN) for prediction of temperature variation of food product during solar drying is investigated. The important climatic variables namely, solar radiation intensity and ambient air temperature are considered as the input parameters for ANN modeling. Experimental data on potato cylinders and slices obtained with mixed mode solar dryer for 9 typical days of different months of the year were used for training and testing the neural network. A methodology is proposed for development of optimal neural network. Results of analysis reveal that the network with 4 neurons and logsig transfer function and trainrp back propagation algorithm is the most appropriate approach for both potato cylinders and slices based on minimum measures of error. In order to test the worthiness of ANN model for prediction of food temperature variation, the analytical heat diffusion model with appropriate boundary conditions and statistical model are also proposed. Based on error analysis results, the prediction capability of ANN model is found to be the best of all the prediction models investigated, irrespective of food sample geometry.