论文部分内容阅读
通过对直接空冷凝汽器传热系数计算过程的分析,以直接空冷机组负荷、排汽压力、凝结水温度、排汽温度、空气入口温度、空气出口温度为输入参数,以直接空冷凝汽器的传热系数和迎面风速为输出参数,将理论模型与实际运行数据相结合,采用BP神经网络方法建立了一种新的直接空冷凝汽器换热性能的预测模型.结果表明:该BP神经网络模型预测得到的参数精度较高,可用于直接空冷凝汽器换热性能的在线监测.
Based on the analysis of the calculation of the heat transfer coefficient of the direct air-cooled condenser, taking the direct air-cooled condenser load, the exhaust pressure, the condensing water temperature, the exhaust temperature, the air inlet temperature and the air outlet temperature as the input parameters, Heat transfer coefficient and oncoming wind speed as output parameters, a new predictive model of heat transfer performance of direct air-cooled condenser is established by combining BP neural network with theoretical model and actual operating data.The results show that the BP neural The parameters predicted by the network model have high precision and can be used for on-line monitoring of the heat transfer performance of the direct air-cooled condenser.