文献类型：期刊文献

中文题名：BP神经网络结合遗传算法优化MIP工艺的产品分布

英文题名：OPTIMIZING PRODUCT DISTRIBUTION OF MIP PROCESS BY BP NEURAL NETWORK COMBINED WITH GENETIC ALGORITHM

作者：欧阳福生[1];游俊峰[1];方伟刚[1]

机构：[1]华东理工大学石油加工研究所,上海200237

年份：2018

卷号：49

期号：8

起止页码：98

中文期刊名：石油炼制与化工

外文期刊名：Petroleum Processing and Petrochemicals

收录：CSTPCD;;Scopus;北大核心:【北大核心2017】；CSCD:【CSCD_E2017_2018】；

语种：中文

中文关键词：催化裂化;MIP工艺;BP神经网络;遗传算法

外文关键词：FCC;MIP process;BP neural network;genetic algorithm

摘要：采用MIP工业装置原料油性质、再生催化剂性质和操作条件等18个变量,使用Pearson相关系数法约简了芳烃含量和第一反应区出口温度两个变量,以约简后的16个变量为输入变量,4个主要产物收率为输出变量,建立了结构为16-20-4的BP神经网络模型。验证表明,所建立的神经网络模型可靠性良好。将所建立的BP神经网络模型与遗传算法相结合,优化了仅汽油收率最大和汽油收率最大并且焦炭收率最小时的操作条件,结果表明,操作条件的优化值与MIP工艺实际情况相符。采用所建立的BP神经网络产品收率模型与遗传算法相结合,可以实现多目标优化,与单纯优化汽油收率相比,虽然汽油收率有所降低,但是焦炭产率有较大幅度下降,对工业生产有指导作用。
Based on the commercial data from a MIP(a kind of FCC process)unit,two variables,aromatics content and outlet temperature,are deleted from the 18 variables including properties of feedstocks,properties of regenerated catalysts and operating conditions by using Pearson correlation coefficient method.Using 16 variables as input variable and 4 main product yields as output variables,a 16-20-4 type of BP neural network model was established.The BP neural network model and genetic algorithm model were combined to optimize the operating conditions for the maximum gasoline yield and the maximum gasoline yield plus the lowest coke yield.The verification results indicated that the calculated values are good consistent with FCC actual values.The multi-objective optimizations can also be obtained by the combined model.Compared with the optimization results for only getting the maximum gasoline yield,though the gasoline yield drops slightly,the coke yield decreases substantially.