Modeling and optimum operating conditions for FCCU using artificial neural network  

作　　者：李全善 李大字 曹柳林 

机构地区：[1]Department of Automation, Beijing University of Chemical Technology

出　　处：《Journal of Central South University》2015年第4期1342-1349,共8页中南大学学报（英文版）

基　　金：Projects(60974031,60704011,61174128)supported by the National Natural Science Foundation of China

摘　　要：A self-organizing radial basis function(RBF) neural network(SODM-RBFNN) was presented for predicting the production yields and operating optimization. Gradient descent algorithm was used to optimize the widths of RBF neural network with the initial parameters obtained by k-means learning method. During the iteration procedure of the algorithm, the centers of the neural network were optimized by using the gradient method with these optimized width values. The computational efficiency was maintained by using the multi-threading technique. SODM-RBFNN consists of two RBF neural network models: one is a running model used to predict the product yields of fluid catalytic cracking unit(FCCU) and optimize its operating parameters; the other is a learning model applied to construct or correct a RBF neural network. The running model can be updated by the learning model according to an accuracy criterion. The simulation results of a five-lump kinetic model exhibit its accuracy and generalization capabilities, and practical application in FCCU illustrates its effectiveness.A self-organizing radial basis function（RBF） neural network（SODM-RBFNN） was presented for predicting the production yields and operating optimization. Gradient descent algorithm was used to optimize the widths of RBF neural network with the initial parameters obtained by k-means learning method. During the iteration procedure of the algorithm, the centers of the neural network were optimized by using the gradient method with these optimized width values. The computational efficiency was maintained by using the multi-threading technique. SODM-RBFNN consists of two RBF neural network models： one is a running model used to predict the product yields of fluid catalytic cracking unit（FCCU） and optimize its operating parameters; the other is a learning model applied to construct or correct a RBF neural network. The running model can be updated by the learning model according to an accuracy criterion. The simulation results of a five-lump kinetic model exhibit its accuracy and generalization capabilities, and practical application in FCCU illustrates its effectiveness.

关 键 词：radial basis function（RBF） neural network self-organizing gradient descent double-model fluid catalytic cracking unit（FCCU） 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]