Proceedings of the AASRI Winter International Conference on Engineering and Technology (AASRI-WIET 2013)

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

Authors
Yi Deng, Qingyin Jiang, Zhikai Cao
Corresponding Author
Yi Deng
Available Online December 2013.
DOI
https://doi.org/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.
Open Access
This is an open access article distributed under the CC BY-NC license.

Download article (PDF)

Proceedings
AASRI Winter International Conference on Engineering and Technology (AASRI-WIET 2013)
Part of series
Advances in Intelligent Systems Research
Publication Date
December 2013
ISBN
978-90786-77-95-6
ISSN
1951-6851
DOI
https://doi.org/10.2991/wiet-13.2013.31How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

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  - AASRI Winter International Conference on Engineering and Technology (AASRI-WIET 2013)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/wiet-13.2013.31
DO  - https://doi.org/10.2991/wiet-13.2013.31
ID  - Deng2013/12
ER  -