摘要
针对含蜡原油输送管道的停输再启动问题,以室内模拟环道试验数据为基础,利用误差反向传播算法(BP算法)对相同运行工况含蜡原油在不同停输温度条件下的启动屈服应力进行计算,并由此预测了含蜡原油在不同启动温度条件下的启动屈服应力值。计算结果与试验结果对比表明,BP神经网络对启动屈服值的预测值和试验值最大误差为1.9%,最小误差为0.12%。
In allusion to the problem of shutting down and restarting of waxy crude oil pipeline and based on the data obtained from simulating experimental loop, a calculation on startup yield stress for waxy crude oil under similar running condition at different shutdown temperatures is made with back propagationalgorithm (BP algorithm), and startup yield value is predicted for waxy crude oil at different startup temperatures based on calculation results. The computational results are compared with the experimental results. The results show that as for the startup yield stress value predicted by the artificial neural network and the experimental value,the maximum value is 1. 9%,and the minimum error is 0. 12%.
出处
《油气储运》
CAS
北大核心
2009年第11期17-20,共4页
Oil & Gas Storage and Transportation
关键词
含蜡原油
屈服应力
停输再启动
BP算法
人工神经网络
预测
waxy crude oil, yield stress, shutdown and restarting, back propagation algorithm (BP) ,artificial neural network,prediction
