A Systematic Approach for Solving Large-scale Problems by Neural Network: Open Refrigerated Display Cases and Droplet Evaporation Problems

Document Type

Article

Publication Date

12-19-2008

Publication Title

Food and Bioprocess Technology

Abstract

A systematic approach for solving a large-scale design problem is proposed. The method consists of building a multidimensional test case matrix connecting an output (scalar or vector) to an input vector. Every element of the input vector spans over its possible minimum and maximum values with one or more levels in between. The experimental and validated computational methods are combined to find the output as a function of all permutations of the input variables. The results are used to train a neural network program to perform the proper interpolation for any other expected scenario. The number of required experiments is obtained asymptotically by adding more data to the neural network training set and examining the error. The applicability and feasibility of this approach is shown in two different problems: first, predicting and minimizing the infiltration rate into an open refrigerated display case; and second, predicting the evaporation rate of sessile microdroplets on a nonpermeable surface.

Volume

3

Issue

1

First Page

276

Last Page

287

DOI

https://doi.org/10.1007/s11947-008-0167-6

ISSN

1935-5130

Comments

ESSN: 1935-5149

Rights

© 2008 Springer Nature

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