Neural Network predictive process modeling: Application to food processing
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Date
2009-03
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Addis Ababa University
Abstract
Currently, food processing industry is driven by several requirements. This
requirement includes ensuring safety, meeting quality standard and customer
expectation and reducing production cost to be competent in market. To achieve
this requirement they have to operate at optimum process conditions all the time.
In food processing, due to the nature of the process, it is difficult to find and
operate at the best conditions solely by experience.
The Ethiopia food industry is no coping up with such requirement due cost
of optimization and low level of education of works operating in the production
system. Thus, it is necessary modeling of the process or part of the process to
capture the relation of between important process parameters and use the model to
control and improve the process better. In addition, it is found necessary to make
the model accessible for the operators working in Ethiopian industry. Using
artificial neural network method is found to be very good modeling to tool to solve
food engineering problems.
In this thesis, therefore, artificial neural network method is used to model
and tested for selected food industry engineering problems, specifically, water
activity prediction, predictive food microbiology and control chart pattern
recognition. The model is enclosed in interactive software so that it could also be
used by people that do not have sophisticated mathematical and technical skills.
The result obtained for all problems shows that neural network modeling can be
used to model food process and to predict food process parameters with sufficient
accuracy.
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Ethiopia food industry