Kinetic Parameter Identification of Microbial Batch Fermentation Based on PSO Algorithm

2022 ◽  
pp. 1645-1649
Author(s):  
Qiuduo Zhao ◽  
Jinxia Fan ◽  
Zheqing Tang ◽  
Wenzhe Li
Keyword(s):  
Kinetic Parameter ◽  
Parameter Identification ◽  
Batch Fermentation ◽  
Pso Algorithm

Parameter Identification Model with the Control Term in Batch Anaerobic Fermentation

2012 ◽  
Vol 217-219 ◽  
pp. 1535-1540 ◽  
Author(s):  
Hong Li Xiao ◽  
Shu Xi Zhang ◽  
Zhi Long Xiu ◽  
En Min Feng
Keyword(s):  
Experimental Data ◽  
Parameter Identification ◽  
Batch Fermentation ◽  
Anaerobic Fermentation ◽  
Optimal Parameter ◽  
Dynamical Behavior ◽  
Pso Algorithm ◽  
Anaerobic Culture ◽  
Inertia Weight ◽  
Identification Model

In this paper, according to the characteristics, dynamical behavior and the experimental data of the batch anaerobic culture, a parameter identification model was improved to describe the dynamical system for microorganism in batch fermentation. And some relative characters were introduced. Finally, a PSO algorithm with the inertia weight was used to get the best optimal parameter of the identification model. The results show that the model reduces the errors between the experimental data and computational values, and they can simulate the process of batch fermentation better.

Marine Asynchronous Propulsion Motor Parameter Identification Using Dynamic Particle Swarm Optimization

2013 ◽  
Vol 860-863 ◽  
pp. 2211-2217
Author(s):  
Si Yuan Liu ◽  
Yan Cheng Liu ◽  
Chuan Wang ◽  
Jun Jie Ren
Keyword(s):  
Particle Swarm Optimization ◽  
Parameter Identification ◽  
Electric Propulsion ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Identification System ◽  
Solution Quality ◽  
Swarm Optimization ◽  
Motor Parameter ◽  
Weight Learning

This paper proposes a new application of dynamic particle swarm optimization (PSO) algorithm for parameter identification of vector controlled asynchronous propulsion motor (APM) in electric propulsion ship. The dynamic PSO modifies the inertia weight, learning coefficients and two independent random sequences which affect the convergence capability and solution quality, in order to improve the performance of the standard PSO algorithm. The standard PSO and dynamic PSO algorithms use measurements of the mt-axis currents, voltages of APM as the inputs to parameter identification system. The experimental results obtained compare the identified parameters with the actual parameters. There is also a comparison of the solution quality between standard PSO and dynamic PSO algorithms. The results demonstrate that the dynamic PSO algorithm is better than standard PSO algorithm for APM parameter identification. Dynamic PSO algorithm can improve the performance of ship propulsion motor under abrupt load variation.

scholarly journals Parameter Identification of Composite Materials Based on Spectral Model by Using Model Updating Method

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Rui Sun ◽  
Ricardo Perera ◽  
Enrique Sevillano ◽  
Jintao Gu
Keyword(s):  
Composite Materials ◽  
Parameter Identification ◽  
Model Updating ◽  
Computational Cost ◽  
Experimental Tests ◽  
Element Model ◽  
Pso Algorithm ◽  
Spectral Element ◽  
Spectral Model ◽  
Real Material

A model updating approach based on a spectral element model and solved with a particle swarm optimization (PSO) method is proposed to identify the vibration-damping properties of composite materials. In comparison with conventional finite element model updating, a composite beam is modeled in a unified way by using a spectral approach whose computational cost is significantly reduced due to its simplicity. In this way, the dynamic response can be captured accurately by using a very limited number of elements. To identify the material properties, experimental tests are carried out to get the initial parameters that are introduced to initialize the spectral model; then, a model updating process solved with a PSO algorithm is implemented to obtain the real material parameters. It has been demonstrated that the proposed spectral model is a potential tool for model updating and parameter identification.

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