Modelling of a locally fabricated flat-plate solar milk pasteuriser using artificial neural network
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Date
2013Author
Wayua, Francis O
Okoth, Michael W
Wangoh, John
Type
ArticleLanguage
enMetadata
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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 MATLAB®. 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.
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www.academicjournals.org/aJaR/PDF/pdf2013/18Mar/Wayua%20et%20al.pdfhttp://erepository.uonbi.ac.ke:8080/xmlui/handle/123456789/32069