期刊名称：International Journal of Energy and Environment
印刷版ISSN：2076-2895
电子版ISSN：2076-2909
出版年度：2011
卷号：2
期号：5
页码：829-844
出版社：International Energy and Environment Foundation (IEEF)
摘要：Heat exchanger is a device in which heat is transferred from one medium to another across a solid surface. The performance of heat exchanger deteriorates with time due to fouling on the heat transfer surface. It is necessary to assess periodically the heat exchanger performance, in order to maintain at high efficiency level. Industries follow adopted practices to monitor but it is limited to some degree. Online monitoring has an advantage to understand and improve the heat exchanger performance. In this paper, online performance monitoring system for shell and tube heat exchanger is developed using artificial neural networks (ANNs). Experiments are conducted based on full factorial design of experiments to develop a model using the parameters such as temperatures and flow rates. ANN model for overall heat transfer coefficient of a design/ clean heat exchanger system is developed using a feed forward back propagation neural network and trained. The developed model is validated and tested by comparing the results with the experimental results. This model is used to assess the performance of heat exchanger with the real/fouled system. The performance degradation is expressed using fouling factor (FF), which is derived from the overall heat transfer coefficient of design system and real system. It supports the system to improve the performance by asset utilization, energy efficient and cost reduction interms of production loss.
