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 :
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