摘要
基于遗传算法建立乙烯生产装置神经网络模型;先依据乙烯生产装置工况间的非线性程度设计人工神经网络模型结构,再用遗传算法对该网络模型参数进行组合优化确定,所建模型既可用来进行乙烯产率预测,亦可用来对其生产工况优化。示例表明:建好后的网络模型可对乙烯生产装置进行快速工艺参数优化,获得较优的操作数据。
How to establish the neural network modes of ethylene plant based on genetic algorithms is introduced;first of all, the artificial neural network model structure has been designed according to the nonlinear degree between ethylene plant instances and optimized any longer the network model parameters by use of Genetic Algorithms. This model is used to forecast the ethylene yield ratio and also to optimize the productive operation. An example showed that the process parameters of the ethylene plant can be speedily optimized by the genetic network model, and higher ethylene yield can be obtained.
出处
《石油化工自动化》
CAS
2006年第1期48-51,共4页
Automation in Petro-chemical Industry
关键词
乙烯生产装置
遗传算法
神经网络
建模
非线性
组合优化
ethylene plant
genetic algorithms
neural network
model
nonlinear
combination optimization
