锅炉受热面积灰在线监测的研究

本文主要进行了电站锅炉受热面积灰在线监测的研究工作,在选择监测参数的过程中,放弃了传统的热有效系数和灰污系数,而选择了相对较易监测的灰污特征参数,并运用人工神经网络之BP网络预测各种工况下受热面清洁时的吸热量,最终推算出灰污特征参数;基于以上理论,充分利用电厂DAS数据资源,在不增加额外测点的条件下,开发和实现了对流受热面积灰的计算机在线监测以及优化吹灰指导。

RESEARCH ON ON-LINE FOULING MONITORING OF BOILER HEATING SURFACE

In this paper, the on-line monitoring system of fouling of heating surface was investigated. The dirty parameter of dust, which is relatively easy to monitor, was chosen as the monitoring parameter, instead of the traditional thermal effective coefficient and dirty coefficient of dust. With BP network of the artificial neural network, the clean heat-absorption under any operating conditions can be predicted. After the real and clean heat-absorption is acquired, the dirty parameter of dust can be calculated through definition. Based on the above-mentioned theory, with the advantage of the data of DAS, the computer on-line monitoring system of heating surface fouling of coal-fired boiler was developed, offering strong guarantee for security and economical operation of the unit.