Online Optimization of Industrial FCC Unit Based on PSO Algorithm and RBF Neural Network
Yi Deng, Qingyin Jiang, Zhikai Cao
Available Online December 2013.
- https://doi.org/10.2991/wiet-13.2013.31How to use a DOI?
- PSO; GA; RBF Neural Network; Online Optimization; FCC.
- 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.
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 SP - 134 EP - 138 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 -