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.

PARAMETER IDENTIFICATION AND TERMINAL STEADY-STATE OPTIMIZATION FOR S SYSTEM IN MICROBIAL CONTINUOUS FERMENTATION

2012 ◽  
Vol 05 (02) ◽  
pp. 1250020
Author(s):  
XIAOFANG LI ◽  
RONGNING QU
Keyword(s):  
Experimental Data ◽  
Steady State ◽  
Klebsiella Pneumoniae ◽  
Parameter Identification ◽  
State Constraints ◽  
Continuous Fermentation ◽  
Commercial Production ◽  
Maximum Volume ◽  
Glycerol Fermentation ◽  
Identification Model

In this paper, we propose a special S system to describe the process of continuous glycerol fermentation to 1,3-propanediol by Klebsiella pneumoniae. Some properties of solution for S system are discussed. Moreover, in order to identify the values of parameters such that S system can simulate the fermentation as exactly as possible, we develop a parameter identification model, and prove the identifiability of parameters. Numerical results show that the average relative error between computational value and experimental data is 20.0875% lower than 24.4225% in previous work, which demonstrates that S system is better in describing continuous fermentation. Finally, we present a terminal steady-state optimization model with state constraints. Optimization results show that the maximum volume yields of 1,3-propanediol at terminal moment increased from 114.3 to 124.911 mmolL-1h-1. The results provide reference for the commercial production of 1,3-propanediol.

The parameter identification model considering both geometric parameters and joint stiffness

2019 ◽  
Vol 47 (1) ◽  
pp. 76-81
Author(s):  
Jing Bai ◽  
Le Fan ◽  
Shuyang Zhang ◽  
Zengcui Wang ◽  
Xiansheng Qin
Keyword(s):  
Parameter Identification ◽  
Industrial Robot ◽  
Joint Stiffness ◽  
Geometric Parameters ◽  
Least Square ◽  
Experimental Result ◽  
Positional Accuracy ◽  
Content Type ◽  
The Absolute ◽  
Identification Model

Purpose Both geometric and non-geometric parameters have noticeable influence on the absolute positional accuracy of 6-dof articulated industrial robot. This paper aims to enhance it and improve the applicability in the field of flexible assembling processing and parts fabrication by developing a more practical parameter identification model. Design/methodology/approach The model is developed by considering both geometric parameters and joint stiffness; geometric parameters contain 27 parameters and the parallelism problem between axes 2 and 3 is involved by introducing a new parameter. The joint stiffness, as the non-geometric parameter considered in this paper, is considered by regarding the industrial robot as a rigid linkage and flexible joint model and adds six parameters. The model is formulated as the form of error via linearization. Findings The performance of the proposed model is validated by an experiment which is developed on KUKA KR500-3 robot. An experiment is implemented by measuring 20 positions in the work space of this robot, obtaining least-square solution of measured positions by the software MATLAB and comparing the result with the solution without considering joint stiffness. It illustrates that the identification model considering both joint stiffness and geometric parameters can modify the theoretical position of robots more accurately, where the error is within 0.5 mm in this case, and the volatility is also reduced. Originality/value A new parameter identification model is proposed and verified. According to the experimental result, the absolute positional accuracy can be remarkably enhanced and the stability of the results can be improved, which provide more accurate parameter identification for calibration and further application.

scholarly journals Material parameter identification using finite elements with time-adaptive higher-order time integration and experimental full-field strain information

2021 ◽  
Author(s):  
Stefan Hartmann ◽  
Rose Rogin Gilbert
Keyword(s):  
Experimental Data ◽  
Finite Elements ◽  
Parameter Identification ◽  
Material Parameter ◽  
Measurement Data ◽  
Least Square ◽  
Image Correlation ◽  
Field Strain ◽  
Material Parameter Identification ◽  
Full Field

AbstractIn this article, we follow a thorough matrix presentation of material parameter identification using a least-square approach, where the model is given by non-linear finite elements, and the experimental data is provided by both force data as well as full-field strain measurement data based on digital image correlation. First, the rigorous concept of semi-discretization for the direct problem is chosen, where—in the first step—the spatial discretization yields a large system of differential-algebraic equation (DAE-system). This is solved using a time-adaptive, high-order, singly diagonally-implicit Runge–Kutta method. Second, to study the fully analytical versus fully numerical determination of the sensitivities, required in a gradient-based optimization scheme, the force determination using the Lagrange-multiplier method and the strain computation must be provided explicitly. The consideration of the strains is necessary to circumvent the influence of rigid body motions occurring in the experimental data. This is done by applying an external strain determination tool which is based on the nodal displacements of the finite element program. Third, we apply the concept of local identifiability on the entire parameter identification procedure and show its influence on the choice of the parameters of the rate-type constitutive model. As a test example, a finite strain viscoelasticity model and biaxial tensile tests applied to a rubber-like material are chosen.

Improved Particle Swarm Optimizer with Dynamically Adjusted Search Space and Velocity Limits for Global Optimization

2015 ◽  
Vol 24 (05) ◽  
pp. 1550017 ◽  
Author(s):  
Aderemi Oluyinka Adewumi ◽  
Akugbe Martins Arasomwan
Keyword(s):  
Global Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Search Space ◽  
Particle Swarm Optimizer ◽  
Search Range ◽  
Original Algorithm ◽  
Inertia Weight ◽  
And Performance

This paper presents an improved particle swarm optimization (PSO) technique for global optimization. Many variants of the technique have been proposed in literature. However, two major things characterize many of these variants namely, static search space and velocity limits, which bound their flexibilities in obtaining optimal solutions for many optimization problems. Furthermore, the problem of premature convergence persists in many variants despite the introduction of additional parameters such as inertia weight and extra computation ability. This paper proposes an improved PSO algorithm without inertia weight. The proposed algorithm dynamically adjusts the search space and velocity limits for the swarm in each iteration by picking the highest and lowest values among all the dimensions of the particles, calculates their absolute values and then uses the higher of the two values to define a new search range and velocity limits for next iteration. The efficiency and performance of the proposed algorithm was shown using popular benchmark global optimization problems with low and high dimensions. Results obtained demonstrate better convergence speed and precision, stability, robustness with better global search ability when compared with six recent variants of the original algorithm.

Optimal parameter identification strategy applied to lithium‐ion battery model

2021 ◽  
Author(s):  
Seydali Ferahtia ◽  
Ali Djeroui ◽  
Hegazy Rezk ◽  
Aissa Chouder ◽  
Azeddine Houari ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Lithium Ion Battery ◽  
Optimal Parameter ◽  
Lithium Ion ◽  
Battery Model ◽  
Identification Strategy
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