集总动力学-BP神经网络混合模型用于预测延迟焦化装置液体产品产率

建立了延迟焦化过程模型对不同性质原料和操作条件下的液体产品产率进行预测, 可实现生产参数调优，进一步提高延迟焦化装置的经济效益。以某炼油厂1.4 Mt/a延迟焦化装置为研究对象，从十一集总动力学模型出发，建立了动态平衡假定下反应器数学模型，选取机理模型计算结果和关键位点历史数据为BP神经网络输入，针对延迟焦化液体产品构建了十一集总动力学-BP神经网络串联混合模型。以焦化柴油产率预测为例，分析了混合模型的预测效果，并与单一机理模型和BP神经网络经验模型进行对比。对比结果表明，3种模型中混合模型预测精度最高，受原料物性和操作条件波动影响小，其预测结果的均方根误差、平均绝对误差和平均相对误差分别为0.751百分点，0.524百分点，2.01%。

A LUMPING-BPNN HYBRID MODEL FOR PREDICTION OF LIQUID YIELD OF DELAYED COKING

To improve the economic benefit of delayed coking unit (DCU), it’s necessary to establish a precise yield prediction model for various feedstocks and operation conditions. A lumping-BP neural network hybrid model in a cascade form was established for a DCU with capacity of 1.4 Mt/a to predict the liquid yield of the unit, based on the mathematical model of the reactor under the assumption of dynamic balance from 11 lumping dynamic model and the BP neural network input of the mechanism model calculation results and the historical data of key sites. In the case study, the coking diesel yield was predicted by the hybrid model, and compared with the results of mechanism model, empirical model. The results demonstrate that among these three methods, the prediction accuracy of the hybrid model is the best. The impact of the material properties and operating conditions fluctuation on the hybrid model results is small, the root mean square error, mean absolute error and the average relative error is 0.751 percentage point, 0.524 percentage point, 2.01%, respectively.