Modelling of a locally fabricated flat-plate solar milk pasteuriser using artificial neural network

The objective of this work was to develop an artificial neural network model to predict milk temperature of a locally fabricated solar milk pasteuriser, based on measures of error deviation from experimental data. A three-layer feed-forward neural network model based on back propagation algorithm wa...

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Bibliographic Details
Published in:African journal of agricultural research 2013-03, Vol.8 (9), p.741-749
Main Authors: Wayua, Francis O, Okoth, Michael W, Wangoh, John
Format: Article
Language:English
Subjects:
Farm machinery
Forecasts
Mathematical models
Milk
Neural networks
Science and Technology
Solar energy
Storage
Temperature
Online Access:Get full text
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Summary:The objective of this work was to develop an artificial neural network model to predict milk temperature of a locally fabricated solar milk pasteuriser, based on measures of error deviation from experimental data. A three-layer feed-forward neural network model based on back propagation algorithm was developed using the Neural Network Toolbox for MATLABr. The inputs of the model were ambient air temperature, solar radiation, wind speed, temperature of hot water, and water flow rate through the collector, whereas the output was temperature of milk being pasteurised. The optimal neural network model had a 4-4-1 structure with sigmoid transfer function. The neural network predictions agreed well with experimental values with mean squared error, mean relative error and correlation coefficient of determination (R2) of 5.22°C, 3.71% and 0.89, respectively. These results indicate that artificial neural network can successfully be used for the prediction of the performance of a locally fabricated solar milk pasteuriser. [PUBLICATION ABSTRACT]
ISSN:1991-637X