Neural network for evaluating boiler behaviour
Neural network for evaluating boiler behaviour
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DOI：
10.1016/j.applthermaleng.2005.12.006
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发表时间：
2006-10-01
影响因子：
6.4
通讯作者：
Gareta, Raquel
中科院分区：
文献类型：
--
作者：
Romeo, Luis M.;Gareta, Raquel
结垢和结渣是制约生物质能源潜力开发和实现可再生能源利用目标的难点。分析生物质锅炉结垢影响的适当技术是监测传热设备中的吸热演变。传统的基于方程的监测技术在处理这种复杂现象时存在问题。本文的目的是提出神经网络（NN）的设计方法和应用的生物质锅炉监测，并指出在这些情况下的神经网络的优势。采用传统方法与神经网络结构相结合的方法对锅炉进行监控，可以彻底解决这一问题。神经网络监测结果与真实的数据吻合较好。它还得出结论，NN是一个更强大的工具，监测比基于方程的监测。这项工作将是未来发展的基础，以控制和最大限度地减少生物质锅炉污垢的影响。(c)2005爱思唯尔有限公司保留所有权利。
Fouling and slagging are some difficulties for the development of biomass as energy potential and to achieve the targets of renewable energy sources utilization. The proper technique to analyze the influence of fouling in a biomass boiler is to monitorize the evolution of heat absorption in heat transfer equipment. Traditional equation-based monitoring techniques have problems to tackle with this complex phenomenon. The objective of this paper is to present the methodology of Neural Network (NN) design and application for a biomass boiler monitoring and point out the advantages of NN in these situations. A combination of traditional methods aided with a NN structure to monitorize the boiler could completely solve the problem. NN monitorizing results show an excellent agreement with real data. It is also concluded that NN is a stronger tool for monitoring than equation-based monitoring. This work will be the basis of a future development in order to control and minimize the effect of fouling in biomass boilers. (c) 2005 Elsevier Ltd. All rights reserved.