文献类型：期刊文献

中文题名：以BP神经网络为基础的MIP工艺过程产品分布优化

英文题名：OPTIMIZING PRODUCT DISTRIBUTION OF MIP PROCESS USING BP NEURAL NETWORK

作者：欧阳福生[1];方伟刚[1];唐嘉瑞[1];江洪波[1]

机构：[1]华东理工大学石油加工研究所,上海200237

年份：2016

卷号：47

期号：5

起止页码：95

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2014】；CSCD:【CSCD_E2015_2016】；

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：fluid catalytic cracking; MIP process; BP neural network; genetic algorithm;

摘要：催化裂化是一个高度非线性和强耦合的系统,传统的机理模型很难描述,而BP神经网络具有强大的非线性拟合和自学习能力。以某炼油厂1.0 Mt/a的MIP装置反应-再生系统为研究对象,选取包括原料油性质、再生剂性质、操作条件的19个变量为神经网络模型的输入变量,液化气、汽油、柴油、焦炭收率为输出变量,建立了19-24-4结构的BP神经网络。在此基础上,考察了原料油预热温度、第一反应区出口温度、第二反应区出口温度、反应压力对产品分布的影响,并采用遗传算法得到使汽油收率最优的操作条件。结果表明,所建立的模型具有良好的预测和外推能力,可为工业装置操作条件的优化提供指导。
Fluid catalytic cracking（ FCC） is a highly non-linear and strong coupled operation system and is too hard to be described by traditional mechanism model. The artificial neural network provides a promising way to solve the problem because of its strong nonlinear prediction and self-learning ability. In a practical application of this method for a 1. 0 Mt/a MIP unit,a 19-24-4 type of BP neural network to predict the yields of liquid petroleum gas（ LPG）,gasoline,diesel and coke was established using nineteen input variables including properties of feedstock,regenerated catalyst and operating variables. Based on the BP neural network,the influences of the feedstock preheating temperature,outlet temperatures of two reaction zones and reaction pressure on product distribution were investigated and the operating variables were optimized using genetic algorithm（ GA） with a view to maximize gasoline yield. The predicted results agree well with industrial data and a significant improvement in the gasoline yield is gained under the optimized conditions.