Title :
Modeling delayed coking plant via RBF neural networks
Author :
Zhang, Kejin ; Yu, Jinshou
Author_Institution :
Res. Inst. of Autom., East China Univ. of Sci. & Technol, China
Abstract :
According to the mechanism analysis of a delayed coking plant, combined with the on-site process data, the multiple variables model of liquid products of the delayed coking plant is built by RBF neural networks (RBFNN). This RBFNN-based model provides yield ratio of gasoline, diesel oil, coker gas-oil and general yield ratio of liquid products simultaneously. Results show that the model is practically equivalent and its generalization ability is satisfactory. The simulation results are satisfied and show fine practical value for industrial production operation and optimization control
Keywords :
oil refining; optimal control; process control; radial basis function networks; RBF neural networks; coker gas-oil; delayed coking plant; diesel oil; gasoline; generalization ability; industrial production operation; liquid products; multiple variables model; on-site process data; optimization control; yield ratio; Artificial neural networks; Automation; Delay; Electronic mail; Feeds; Furnaces; Neural networks; Petroleum; Recycling; Temperature;
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-5529-6
DOI :
10.1109/IJCNN.1999.836226
