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Author Xiaofeng Zhuang ♦ Jinshou Yu
Sponsorship Lee Found. ♦ IEEE Montreal Sect. ♦ Candian Soc. Mechanical Eng
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2003
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science ♦ Technology ♦ Engineering & allied operations
Subject Keyword Furnaces ♦ Recurrent neural networks ♦ Neural networks ♦ Dynamic Bias ♦ Cracking furnace ♦ Yield Model ♦ Recurrent Neural Network
Abstract This paper employs a kind of novel neural network, recurrent network with dynamic biases, to model the yields of ethylene and propylene for an industrial cracking furnace. The process information of the furnace is introduced to adapt the furnace's feedstock changes and running phase by the dynamic biases. Comparision with the models based on other algorithms is conducted. The model based on this approach is presented to demonstrate satisfactory result.
Description Author affiliation: Research Institute of Automation, East China University of Science & Technology, Shanghai, 200237, China. Email: (Xiaofeng Zhuang)
ISBN 078037777X
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2003-06-12
Publisher Place Canada
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 309.06 kB
Page Count 5
Starting Page 718
Ending Page 722

Source: IEEE Xplore Digital Library