Three-layers BP neural network was established using MATLAB to predict the hydro-cracking conversion,kerosene product endpoint and pressure drop of high pressure heat exchanger shell of middle pressure hydrocracking plant. The results revealed that the sample data quality and number of network hidden layer nodes affect the BP network accuracy evidently,while the network could predict the process parameters,product properties,and heat exchanger state primely. The prediction accuracy for hydrocracking conversion is the lowest,the relative error is ±(5%-10%);the accuracy of the predic-tion of jet fuel endpoint is high with a relative error of ± (0. 1 5%-2. 0%);The absolute error of the pressure drop prediction of the shell side of the heat exchanger is within ±0. 03 MPa,indicating that the network established satisfies the requirements for heat exchanger condition monitoring.%应用 Matlab软件构建了三层BP神经网络,并对中压加氢裂化装置转化率、喷气燃料终馏点和高压换热器壳程压降等方面进行了预测,结果表明:BP神经网络模型准确度受样本数据质量、网络隐藏层节点数目影响较大,对中压加氢裂化工艺参数、产品性质、高换设备状态等均展示出较好预测能力;其中,对加氢裂化转化率预测的准确度最低,相对误差为±(5%～10%);对喷气燃料终馏点预测的准确度较高,相对误差为±(0. 15%～2. 0%);对高压换热器壳程压降值预测的绝对误差为±0. 03 MPa以内,满足换热器状态监测要求.
展开▼
