石油化工2017，Vol.46Issue(1)：9-16,8.DOI:10.3969/j.issn.1000-8144.2017.01.002
集总动力学模型结合神经网络预测催化裂化产物收率
Prediction of the product yield from catalytic cracking process by lumped kinetic model combined with neural network
欧阳福生 1刘永吉1
作者信息
- 1. 华东理工大学石油加工研究所,上海200237
- 折叠
摘要
Abstract
Based on the reaction mechanism of heavy oil catalytic cracking and the characteristics of maximizing iso-paraffins(MIP) process and combined with a large amount of industrial data,the reaction network of an 8-lump kinetic model including saturates,aromatics,asphaltenes+resins,diesel,gasoline liquefied gas,dry gas and coke for the process was developed.And then the 47 kinetic parameters for the model were calculated by the combination of Runge-Kutta method and genetic algorithm.The results showed that good consistence with the reaction mechanism of heavy oil catalytic cracking,the average relative errors between calculated values and actual values of products are all less than 5％.Combining the lumped model with the 14-7-5 type of BP neural network can further improve the prediction accuracy of the product distribution,which provides a new direction for simulation and optimization for heavy oil catalytic cracking.关键词
催化裂化/MIP工艺/集总模型/神经网络Key words
catalytic cracking/MIP process/lumped model/neural network分类
能源科技引用本文复制引用
欧阳福生,刘永吉..集总动力学模型结合神经网络预测催化裂化产物收率[J].石油化工,2017,46(1):9-16,8.
