Prediction of Humidification Power in Textile-Spinning Mills Using Functional and Neural Networks
Publication: Journal of Energy Engineering
Volume 129, Issue 1
Abstract
The power required for the supply, pumping, and exhaust system of the humidification plant of a spinning department of a textile mill depends on many factors. The most important factors are power of motor driving machinery, lighting and heating load, number of people inside, temperature gradient, and relative humidity. The usual procedure is to train the back-propagation neural network (BPN) with the available data and, once it is trained, BPN will be used for inferring. Some other investigators have used different neural network architectures. In this article, we give a general methodology to build and work with functional network (FN), an alternative to neural network paradigm. In this architecture neural functions, instead of weights, are learned. In addition to data, domain knowledge can also be incorporated. It is shown by means of an example that this functional network architecture is successfully applied to predict power required for the humidification plants.
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Copyright © 2003 American Society of Civil Engineers.
History
Received: Apr 22, 2002
Accepted: Aug 23, 2002
Published online: Mar 14, 2003
Published in print: Apr 2003
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