scholarly journals Modeling and Parameter Identification Involving 3-Hydroxypropionaldehyde Inhibitory Effects in Glycerol Continuous Fermentation

2012 ◽  
Vol 2012 ◽  
pp. 1-18
Author(s):  
Zhaohua Gong ◽  
Chongyang Liu ◽  
Yongsheng Yu
Keyword(s):  
Parameter Identification ◽  
Fermentation Process ◽  
Continuous Fermentation ◽  
Robustness Analysis ◽  
Function Technique ◽  
Improved Genetic Algorithm ◽  
Proposed Model ◽  
Biotechnological Processes ◽  
Extracellular Substances ◽  
Identification Model

Mathematical modeling and parameter estimation are critical steps in the optimization of biotechnological processes. In the 1,3-propanediol (1,3-PD) production by glycerol fermentation process under anaerobic conditions, 3-hydroxypropionaldehyde (3-HPA) accumulation would arouse an irreversible cessation of the fermentation process. Considering 3-HPA inhibitions to cells growth and to activities of enzymes, we propose a novel mathematical model to describe glycerol continuous cultures. Some properties of the above model are discussed. On the basis of the concentrations of extracellular substances, a parameter identification model is established to determine the kinetic parameters in the presented system. Through the penalty function technique combined with an extension of the state space method, an improved genetic algorithm is then constructed to solve the parameter identification model. An illustrative numerical example shows the appropriateness of the proposed model and the validity of optimization algorithm. Since it is difficult to measure the concentrations of intracellular substances, a quantitative robustness analysis method is given to infer whether the model is plausible for the intracellular substances. Numerical results show that the proposed model is of good robustness.

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.

MULTISTAGE DYNAMIC SYSTEM OF MICROBIAL BATCH FERMENTATION AND ITS PARAMETER IDENTIFICATION

2013 ◽  
Vol 06 (06) ◽  
pp. 1350044
Author(s):  
YAN WANG ◽  
XIAOHONG LI ◽  
ENMIN FENG ◽  
ZHILONG XIU
Keyword(s):  
Particle Swarm Optimization ◽  
Klebsiella Pneumoniae ◽  
Parameter Identification ◽  
Dynamic System ◽  
Relative Error ◽  
Batch Fermentation ◽  
Fermentation Process ◽  
Swarm Optimization ◽  
Proposed Model ◽  
Three Stages

Based on 1,3-propanediol production from batch fermentation of glycerol by Klebsiella pneumoniae, a multistage dynamic system and its parameter identification are discussed in this paper. The batch fermentation process is divided into three stages exhibiting different dynamic behaviors and characteristics, from which a corresponding nonlinear multistage dynamic system is built. We then propose a parameter identification optimization model whose objective function is the average relative error. The model is solved by particle swarm optimization weighted by inertia, and the result shows that the relative error of our proposed model is 2–10% smaller than those of existing models.

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 THE INFLUENCE OF YEAST RACES ON THE AROMA-FORMING SUBSTANCES OF TABLE WINES

2021 ◽  
Vol 447 (3) ◽  
pp. 13-18
Author(s):  
Z.А. Anarbekova ◽  
G.I. Baigazieva
Keyword(s):  
Direct Contact ◽  
Fermentation Process ◽  
Alcoholic Fermentation ◽  
Grape Juice ◽  
Yeast Cells ◽  
Higher Alcohols ◽  
Long Time ◽  
Speed Up ◽  
Biotechnological Processes

Wine is a product of biochemical transformations, compounds present in grape juice, by controlled alcoholic fermentation, that is, effervescence. Grape and yeast enzymes play a key role in the processing of grapes and the preparation of wine, influencing all biotechnological processes of winemaking. Adding liquid or dry active yeast to the wort allows better control of the fermentation process. Under the influence of these yeasts, sugar is converted mainly into alcohol or carbon dioxide, but the yeast itself during fermentation produces many molecules (higher alcohols, esters) that affect the aroma and taste of wine. These transformations take about two weeks and lead to a significant increase in temperature, which must be regulated, not allowing it to rise above 18-20°C: otherwise, some of the aromatic substances may evaporate and the fermentation process itself will stop. The amount of yeast that determines the correct and complete fermentation depends both on the quality of the wort itself, and on the more or less prolonged access of air, the ambient temperature. The air, or rather the oxygen of the air, has a beneficial effect on fermentation as long as there are still many nutrients (sugars) in the wort; as the latter are consumed, extremely small yeast cells are formed, which persist for a long time in the form of turbidity. The rapid course of fermentation can be greatly facilitated by the periodic stirring of yeast, which, settling to the bottom, lose direct contact with nutrients — the lower layers almost do not function. You can mix the wort mechanically or by adding healthy whole grapes to it; in this case, the wort is constantly and automatically mixed: the berries, rising up in the fermenting liquid, carry the yeast with them. In order to speed up the fermentation, the wort is sometimes ventilated, that is, air is introduced into it, by mixing. This article shows the influence of the yeast race on the fermentation dynamics of white grape must, the composition of organic acids and aroma-forming components. The races that ensure the production of highquality wine materials are identified.

Discrete Preisach Model of a Shape Memory Alloy Actuator

2016 ◽  
Vol 248 ◽  
pp. 227-234
Author(s):  
Waldemar Rączka ◽  
Jarosław Konieczny ◽  
Marek Sibielak ◽  
Janusz Kowal
Keyword(s):  
Mathematical Model ◽  
Shape Memory Alloy ◽  
Parameter Identification ◽  
Shape Memory ◽  
Preisach Model ◽  
Discrete Form ◽  
Nonlinear Characteristics ◽  
Shape Memory Alloy Actuator ◽  
Sma Actuator ◽  
Identification Model

Shape Memory Alloy is a material used to designing actuators. These actuators have many advantages. They are light, strong and silent. They are building in laboratory and tested because beside advantages they have disadvantages too. SMA actuators have nonlinear characteristics with hysteresis loop.In the first part of the paper Shape Memory Alloys are shortly described. Next mathematical model was formulated. In the paper the Preisach model was developed. Discrete form of the model was considered and implemented. After parameter identification model was implemented in LabView. Tests of the model were conducted and results were worked. Obtained characteristics of the SMA actuator are shown in the paper. At the end of the paper the conclusions were formulated.

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