摘要
本文将采用BP神经网络技术,以压力、温度、流速、含硫量和酸值作为输入参数,以管道内腐蚀速率作为输出参数,建立了输油管道的腐蚀速率预测模型。计算结果表明,该模型具有较好的预测精度,模拟出的腐蚀速率与实测值能较好的吻合,并且能够反映各因素与腐蚀速率之间的关系。
A model for predicting the corrosion rate of oil pipeline was built based on BP neural network theory. In this model, pressure, temperature, flow speed, sulphur content and acid number were set as input parameters while the corrosion rare was the output parameter. The results show that the model has good forecasting accuracy and it could reflect the corrosion factors effectively.
出处
《中国特种设备安全》
2013年第9期4-7,共4页
China Special Equipment Safety
关键词
BP神经网络
输油管道
腐蚀速率
BP artificial neural network Oil pipeline Corrosion rate
