摘要
用BP神经网络技术建立了某2.80 Mt?a蜡油高压加氢裂化装置反应系统模型,该模型可较好地预测原料量、各段反应器进口温度和冷氢导入量对系统产品分布和各段反应器出口温度的影响,模型精度较高,温度预测误差小于0.1℃,并具有较好的再现性及泛化能力,可以用于指导生产操作。
The highly complexity of petroleum hydrocracking process results in the application of artificial neural network（ANN）in this field.In this paper a BP-ANN was used to model a VGO hydrocracking unit with a capacity of 2.8 Mt/a.The effect of feed rate,inlet temperatures of reactors,and amount of quench H2 used on product distribution and outlet temperatures of reactors were well predicted by the model.The results show that the model has a higher accuracy,especially in the prediction of temperatures（less than 0.1 ℃）and a good ability of reproducibility and generalization ability and that the model is able to guide practical operation.
出处
《石油炼制与化工》
CAS
CSCD
北大核心
2015年第8期90-95,共6页
Petroleum Processing and Petrochemicals
关键词
石油馏分
加氢裂化
神经网络
数据挖掘
预测
建模
petroleum fraction
hydrocracking
neural network
data mining
prediction
modeling
