Optimal control of a fedbatch fermentation process based on an improved multi-objective particle swarm optimization algorithm

PDF(1182 KB)

Journal of Beijing University of Chemical Technology ›› 2014, Vol. 41 ›› Issue (1) : 68-73.

ZHANG ChaoRan;WANG JianLin;ZHAO LiQiang;YU Tao

Author information +

History +

Abstract

An optimization method for feed profiles in a fed-batch fermentation process based on an improved multi-objective particle swarm optimization (MOPSO) algorithm has been derived and the method was used to optimize the feed profiles for a yeast fermentation process. The parameters in the improved MOPSO algorithm were modified, and the speed of constraint boundary particles in the feasible region was slowed down so as to enhance the ability to search for constraint boundaries. The experimental results showed that the proposed method can effectively realize optimal control of the yeast fermentation process by using the optimal feed profiles given by the improved MOPSO.

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

ZHANG ChaoRan;WANG JianLin;ZHAO LiQiang;YU Tao. Optimal control of a fedbatch fermentation process based on an improved multi-objective particle swarm optimization algorithm[J]. Journal of Beijing University of Chemical Technology, 2014, 41(1): 68-73

References

[1]张立, 晏琦, 侯迪波. 免疫PSO算法在啤酒配方优化中的应用研究[J]. 仪器仪表学报, 2008, 29(9): 1982-1985. 
Zhang L, Yan Q, Hou D B. Research of immune particle swarm optimization algorithm for recipe of beer optimization[J]. Chinese Journal of Scientific Instrument, 2008, 29(9): 1982-1985. (in Chinese)
[2]傅春生, 王骥程. 间歇式发酵过程自动控制的回顾与展望[J]. 化工自动化及仪表, 1987, 14(4): 1-7. 
Fu C S, Wang J C. Review and prospects of fermentation batch process control[J]. Control and Instruments in Chemical Industry, 1987, 14(4): 1-7. (in Chinese)
[3]范启亮，章瑶，刘春波，等．基于PSOMKSVM发酵过程建模与补料优化控制[J]．自动化与仪表， 2009, 5: 123-126．
Fan Q L, Zhang Y, Liu C B, et al. Modeling of fermentation process and optimization control of material makeup based on PSOMKSVM [J]. Automation & Instrumentation, 2009, 5: 123-126. (in Chinese)
[4]薛尧予,王建林,于涛, 等. 基于改进PSO算法的发酵过程模型参数估计[J]．仪器仪表学报， 2010， 31 (1)： 178-182．
Xue Y Y, Wang J L, Yu T, et al. Parameter estimation of fermentation process model based on an improved PSO algorithm[J]. Chinese Journal of Scientific Instrument, 2010, 31(1): 178-182. (in Chinese)
[5]Roy S, Gudi R D, Venkatesh K V, et al. Optimal control strategies for simultaneous saccharification and fermentation of starch [J]. Process Biochemistry, 2001, 36: 713-722. 
[6]Xiong Z H, Zhang J. Modeling and optimal control of fedbatch processes using control affine feedforward neural networks[C]∥Proceeding of the American Control Conference, Anchorage, USA, 2002: 5025-5030. 
[7]王海霞, 肖应旺, 徐保国. 基于SVMIGA的补料分批发酵过程优化控制[J]. 计算机测量与控制, 2005, 13(7): 674-676. 
Wang H X, Xiao Y W, Xu B G. Optimization control of material makeup ferment process in batches based on SVMIGA[J]. Computer Measurement & Control, 2005, 13(7): 674-676. (in Chinese)
[8]孔超, 李宏光. 新型优化策略在青霉素间歇发酵过程中的应用研究[J]. 化工自动化及仪表, 2011, 38(5): 529-532. 
Kong C, Li H G. Study on new optimal strategy for penicillin batch fermentation[J]. Control and Instruments in Chemical Industry, 2011, 38(5): 529-532. (in Chinese)
[9]Yüzge U, Türker M, Hocalar A. On-line evolutionary optimization of an industrial fedbatch yeast fermentation process[J]. ISA Transactions, 2009, 48: 79-92. 
[10]Hu X H, Eberhart R. Multiobjective optimization using dynamic neighborhood particle swarm optimization[C]∥Proceeding of the Congress on Evolution Computation, Honolulu, USA, 2002: 1677-1681. 
[11]施展, 陈庆伟. 基于QPSO和拥挤距离排序的多目标量子粒子群优化算法[J]. 控制与决策, 2011, 26(4): 540-547. 
Shi Z, Chen Q W. Multiobjective quantum-behaved particle swarm optimization algorithm based on QPSO and crowding distance sorting[J]. Control and Decision, 2011, 26(4): 540-547. (in Chinese)
[12]杨俊杰, 周建中, 方仍存, 等. 基于自适应网格的多目标粒子群优化算法[J]. 系统仿真学报, 2008, 20(21): 5843-5847. 
Yang J J, Zhou J Z, Fang R C, et al. Multi-objective particle swarm optimization based on adaptive grid algorithms[J]. Journal of System Simulation, 2008, 20(21): 5843-5847. (in Chinese)
[13]Deb K, Pratap A, Agarwal S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197. 
[14]Chen L Z, Nguang S K, Chen X D, et al. Modelling and optimization of fedbatch fermentation processes using dynamic neural networks and genetic algorithms[J]. Biochemical Engineering Journal, 2004, 22: 51-61.

PDF(1182 KB)

Accesses

Citation

Detail

Sections

Recommended

Abstract
Cite this article
References

Received	Revised	Published
2013-04-23	1900-01-01	2014-01-20
Issue Date
2014-01-20

Please choose a citation manager

Content to export

Abstract

Cite this article

{{custom_sec.title}}

{{custom_sec.title}}

References

{{custom_fnGroup.title_en}}

Footnotes

模态框（Modal）标题

Please choose a citation manager

Content to export

Abstract

Cite this article

{{custom_sec.title}}

{{custom_sec.title}}

References

{{custom_fnGroup.title_en}}

Footnotes