Parameter identification of dynamical system for microorganism in batch fermentation based on PSO algorithm

Computer Engineering and Applications ›› 2007, Vol. 43 ›› Issue (27): 193-195.

• 工程与应用 • Previous Articles Next Articles

Parameter identification of dynamical system for microorganism in batch fermentation based on PSO algorithm

GONG Zhao-hua

Department of Mathematics and Information Science,Shandong Institude of Business and Technology,Yantai,Shandong 264005,China

Received:1900-01-01 Revised:1900-01-01 Online:2007-09-21 Published:2007-09-21
Contact: GONG Zhao-hua

基于PSO算法的微生物间歇发酵动力学参数辨识

宫召华

山东工商学院数学及信息科学学院,山东烟台 264005

通讯作者: 宫召华

Abstract

Abstract: In this paper,a simplified two-stage parameter identification model is constructed in the process of bio-dissimilation of glycerol to 1,3-propanediol（1,3-PD） based on the characteristics and experimental data of the batch fermentation.Subsequently,we briefly discuss the identifiability of the parameters.Finally,in order to find the optimal parameters of the identification model,an improved PSO algorithm is constructed.Numerical results of simulations show that the model and algorithm reduce greatly the error between the experimental data and computational values,and they can simulate the process of batch fermentation better.

Key words: dynamical system, batch fermentation, parameter identification, Particle Swarm Optimization algorithm, 3-propanediol

摘要： 主要根据微生物间歇培养过程的特征、动态行为及实验数据,建立了能够更好反映间歇发酵过程的简化的多阶段参数辨识模型,然后证明了该模型中最优参数的存在性;最后结合模型特点构造了一种改进的粒子群优化（PSO）算法求得最优参数,并利用所得的参数进行过程仿真。结果表明该模型和算法大大减少了实验数据和计算数值之间的误差,能够更好地模拟微生物间歇发酵过程。

关键词: 动力系统, 间歇发酵, 参数辨识, 粒子群优化算法, 3-丙二醇

GONG Zhao-hua. Parameter identification of dynamical system for microorganism in batch fermentation based on PSO algorithm [J]. Computer Engineering and Applications, 2007, 43(27): 193-195.

宫召华. 基于PSO算法的微生物间歇发酵动力学参数辨识[J]. 计算机工程与应用, 2007, 43(27): 193-195.

[1]	SHI Chuntian, ZENG Yanyang, HOU Shouming. Summary of Application of Swarm Intelligence Algorithms in Image Segmentation [J]. Computer Engineering and Applications, 2021, 57(8): 36-47.
[2]	CHEN Wanfen, WANG Yujia, LIN Weixing. Heterogeneous Ensemble Surrogate Assisted Multi-objective Particle Swarm Optimization Algorithm [J]. Computer Engineering and Applications, 2021, 57(23): 71-80.
[3]	XU Gongxian, QIU Tian, JIA Fusheng, WEI Shunxing. Parameter Identification Method for a Class of Biological Systems and Its Application [J]. Computer Engineering and Applications, 2020, 56(4): 163-167.
[4]	ZHOU Qin, CHENG Lizheng. Particle Swarm Optimization Algorithm for Solving Singular Perturbed Problems on Layer Adaptive Mesh [J]. Computer Engineering and Applications, 2020, 56(11): 46-50.
[5]	ZHANG Xin1, ZOU Dexuan1, XIAO Peng1, YU Qiu2. Self-Adjusted Simplified Particle Swarm Optimization Algorithm and Its Application [J]. Computer Engineering and Applications, 2019, 55(8): 250-263.
[6]	CHEN Jie, ZHANG Wendong, SU Linlin, SANG Shengbo. Spacecraft Attitude-Orbit Control Algorithm Based on Lyapunov Stability Criterion and CPSO Algorithm [J]. Computer Engineering and Applications, 2019, 55(24): 229-234.
[7]	SHEN Yan, YIN Jinshan, HAN Shuai, HAN Yu. Research and Its Optimization of GM（1, 1） Model Based on Numerical Integration Formula [J]. Computer Engineering and Applications, 2019, 55(24): 41-45.
[8]	YANG Xiaohua, GUO Jianquan. Fuzzy Programming of Closed-Loop Logistics Network for Fresh Products under New Retail Model [J]. Computer Engineering and Applications, 2019, 55(2): 198-205.
[9]	ZHU Jingwei, FANG Husheng, SHAO Faming, JIANG Chengming. Self-Adaptive Particle Swarm Optimization Algorithm for Solving Inverse Kinematics Problem of Redundant Manipulator [J]. Computer Engineering and Applications, 2019, 55(14): 215-220.
[10]	HE Minghui1, XU Yi1，2, WANG Ran1, HU Shanzhong1. Combination dynamic inertia weight particle swarm optimization algorithm to optimize neural network and application [J]. Computer Engineering and Applications, 2018, 54(19): 107-113.
[11]	CHANG Hongwei1, XIA Kewen1，2, BAI Jianchuan1，2, NIU Wenjia1, WU Panpan1. Attribute reduction method using cloud quantum-behaved PSO [J]. Computer Engineering and Applications, 2018, 54(10): 99-104.
[12]	OUYANG Long, YANG Jianfeng, XU Jun, GUO Chengcheng . CRQP based resource co-allocation algorithm for OFDMA cellular systems [J]. Computer Engineering and Applications, 2017, 53(5): 121-127.
[13]	NAN Jingchang, TIAN Na. Fuzzy wavelet neural network for modeling based on improved particle swarm optimization algorithm [J]. Computer Engineering and Applications, 2017, 53(3): 120-123.
[14]	ZHAO Jiuling, ZHANG Wenhai. Improved particle swarm algorithm to lower characteristic signal of solid propellant [J]. Computer Engineering and Applications, 2017, 53(19): 136-141.
[15]	BAI Shuren1，2, CHEN Long2. Particle clustering algorithm with adaptive K values [J]. Computer Engineering and Applications, 2017, 53(16): 116-120.

Parameter identification of dynamical system for microorganism in batch fermentation based on PSO algorithm

基于PSO算法的微生物间歇发酵动力学参数辨识

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics