Online Optimization of Industrial FCC Unit Based on PSO Algorithm and RBF Neural Network

DOI

10.2991/wiet-13.2013.31How to use a DOI?

Keywords

PSO; GA; RBF Neural Network; Online Optimization; FCC.

Abstract

The Particle Swarm Optimization (PSO) and the Genetic Algorithm (GA) are two of the most powerful methods to solve the unconstrained and constrained global optimization problems. In this paper, these two methods are briefly introduced firstly, and then the online rolling optimization of industrial FCC unit is carried out based on the RBF Neural Network predictive model. The results of simulation based on the two optimization methods are compared. The comparative results show that the PSO can perform well as the GA in searching the global optimal position. Furthermore, the PSO runs much faster which makes it more effective in online optimization.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yi Deng
AU  - Qingyin Jiang
AU  - Zhikai Cao
PY  - 2013/12
DA  - 2013/12
TI  - Online Optimization of Industrial FCC Unit Based on PSO Algorithm and RBF Neural Network
BT  - Proceedings of the AASRI Winter International Conference on Engineering and Technology (AASRI-WIET 2013)
PB  - Atlantis Press
SP  - 134
EP  - 138
SN  - 1951-6851
UR  - https://doi.org/10.2991/wiet-13.2013.31
DO  - 10.2991/wiet-13.2013.31
ID  - Deng2013/12
ER  -