化工进展Issue(2)：389-396,8.DOI:10.16085/j.issn.1000-6613.2016.02.008
应用经遗传算法优化的BP神经网络预测催化裂化装置焦炭产率
Predicting coke yield of FCC unit using genetic algorithm optimized BP neural network
苏鑫 1裴华健 1吴迎亚 1高金森 1蓝兴英1
作者信息
- 1. 中国石油大学 北京 重质油国家重点实验室，北京 102249
- 折叠
摘要
Abstract
Coke is the main by-product of fluid catalytic cracking (FCC) process. It is of great significance to predict coke yield accurately to enhance stability and economic performance of FCC plant. Artificial neural network (ANN) has a strong self-learning and adaptive ability,and has obvious advantages in nonlinear forecasting. In this paper,a new model combining BP neural network and genetic algorithm (GA) was developed to predict coke yield by choosing 28 key parameters involving feedstock properties,catalyst properties and operating conditions of industrial data of FCC unit,The prediction results obtained from BP neural network and the genetic algorithm optimized BP neural network (GA-BP) were compared. The GA-BP model had a better result in both accuracy and stability. Furthermore,the influence of key parameters,such as reaction temperature,feedstock carbon residue on coke yield was investigated,which further proved the accuracy of BP neural network model optimized by genetic algorithm.关键词
催化裂化/焦炭产率/神经网络/遗传算法Key words
fluid catalytic cracking (FCC)/coke yield/neural networks/genetic algorithm分类
能源科技引用本文复制引用
苏鑫,裴华健,吴迎亚,高金森,蓝兴英..应用经遗传算法优化的BP神经网络预测催化裂化装置焦炭产率[J].化工进展,2016,(2):389-396,8.
