DocumentCode
1709831
Title
Optimization of feeding rate for alcohol fermentation by quantum-behaved Particle Swarm Optimization
Author
Lu, Ke-zhong ; Li, Hai-bo ; Wang, Ru-chuan
Author_Institution
Dept. of Comput. Sci., Chizhou Coll., Chizhou, China
fYear
2010
Firstpage
4677
Lastpage
4680
Abstract
Quantum-behaved Particle Swarm Optimization (QPSO) algorithm is a global convergence guaranteed search method, which introduced quantum theory into traditional Particle Swarm Optimization (PSO). QPSO algorithm outperforms traditional PSO algorithm in search ability as well as having less parameter. In this paper, we employ QPSO algorithm to optimize the feeding rate of alcohol fermentation with the data of alcohol fermentation process. The simulation results show that QPSO algorithm can find better feeding rate quickly and yield more 4% output than traditional PSO algorithm.
Keywords
fermentation; particle swarm optimisation; QPSO algorithm; alcohol fermentation; convergence guaranteed search method; feeding rate optimization; quantum theory; quantum-behaved particle swarm optimization; Biological system modeling; Bioreactors; Computational modeling; Optimization; Particle swarm optimization; Production; alcohol fermentation; feeding optimization; quantum-behaved particle swarm optimization;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Control and Automation (WCICA), 2010 8th World Congress on
Conference_Location
Jinan
Print_ISBN
978-1-4244-6712-9
Type
conf
DOI
10.1109/WCICA.2010.5555294
Filename
5555294
Link To Document