scholarly journals Robust dynamic experiments for the precise estimation of respiration and fermentation parameters of fruit and vegetables

2022 ◽  
Vol 18 (1) ◽  
pp. e1009610
Author(s):  
Arno Strouwen ◽  
Bart M. Nicolaï ◽  
Peter Goos
Keyword(s):  
Dynamic Models ◽  
Initial Guess ◽  
Fruit And Vegetables ◽  
Model Parameters ◽  
Pear Fruit ◽  
Dynamic Experiments ◽  
Linear Differential ◽  
Fermentation Parameters ◽  
Kinetics Of ◽  
True Values

Dynamic models based on non-linear differential equations are increasingly being used in many biological applications. Highly informative dynamic experiments are valuable for the identification of these dynamic models. The storage of fresh fruit and vegetables is one such application where dynamic experimentation is gaining momentum. In this paper, we construct optimal O2 and CO2 gas input profiles to estimate the respiration and fermentation kinetics of pear fruit. The optimal input profiles, however, depend on the true values of the respiration and fermentation parameters. Locally optimal design of input profiles, which uses a single initial guess for the parameters, is the traditional method to deal with this issue. This method, however, is very sensitive to the initial values selected for the model parameters. Therefore, we present a robust experimental design approach that can handle uncertainty on the model parameters.

Output Adaptive Observers Design for Linear Non-Stationary Systems with Polynomial Parameters

2021 ◽  
Vol 22 (8) ◽  
pp. 404-410
Author(s):  
K. B. Dang ◽  
A. A. Pyrkin ◽  
A. A. Bobtsov ◽  
A. A. Vedyakov ◽  
S. I. Nizovtsev
Keyword(s):  
Linear Time ◽  
State Observer ◽  
Observer Design ◽  
Model Parameters ◽  
Time Varying ◽  
State Variables ◽  
Unknown Constant ◽  
Established Method ◽  
Time Varying Parameters ◽  
True Values

The article deals with the problem of state observer design for a linear time-varying plant. To solve this problem, a number of realistic assumptions are considered, assuming that the model parameters are polynomial functions of time with unknown coefficients. The problem of observer design is solved in the class of identification approaches, which provide transformation of the original mathematical model of the plant to a static linear regression equation, in which, instead of unknown constant parameters, there are state variables of generators that model non-stationary parameters. To recover the unknown functions of the regression model, we use the recently well-established method of dynamic regressor extension and mixing (DREM), which allows to obtain monotone estimates, as well as to accelerate the convergence of estimates to the true values. Despite the fact that the article deals with the problem of state observer design, it is worth noting the possibility of using the proposed approach to solve an independent and actual estimation problem of unknown time-varying parameters.

Inversion of helicopter electromagnetic data to a magnetic conductive layered earth

Geophysics ◽  
2003 ◽  
Vol 68 (4) ◽  
pp. 1211-1223 ◽  
Author(s):  
Haoping Huang ◽  
Douglas C. Fraser
Keyword(s):  
Magnetic Permeability ◽  
Field Data ◽  
Model Parameters ◽  
Relative Importance ◽  
Magnetic Polarization ◽  
Inversion Algorithm ◽  
Layered Earth ◽  
Electromagnetic Data ◽  
Airborne Electromagnetic ◽  
True Values

Inversion of airborne electromagnetic (EM) data for a layered earth has been commonly performed under the assumption that the magnetic permeability of the layers is the same as that of free space. The resistivity inverted from helicopter EM data in this way is not reliable in highly magnetic areas because magnetic polarization currents occur in addition to conduction currents, causing the inverted resistivity to be erroneously high. A new algorithm for inverting for the resistivity, magnetic permeability, and thickness of a layered model has been developed for a magnetic conductive layered earth. It is based on traditional inversion methodologies for solving nonlinear inverse problems and minimizes an objective function subject to fitting the data in a least‐squares sense. Studies using synthetic helicopter EM data indicate that the inversion technique is reasonably dependable and provides fast convergence. When six synthetic in‐phase and quadrature data from three frequencies are used, the model parameters for two‐ and three‐layer models are estimated to within a few percent of their true values after several iterations. The analysis of partial derivatives with respect to the model parameters contributes to a better understanding of the relative importance of the model parameters and the reliability of their determination. The inversion algorithm is tested on field data obtained with a Dighem helicopter EM system at Mt. Milligan, British Columbia, Canada. The output magnetic susceptibility‐depth section compares favorably with that of Zhang and Oldenburg who inverted for the susceptibility on the assumption that the resistivity distribution was known.

scholarly journals Modeling of the adsorption kinetics of zinc onto granular activated carbon and natural zeolite

2006 ◽  
Vol 71 (8-9) ◽  
pp. 957-967 ◽  
Author(s):  
Ljiljana Markovska ◽  
Vera Meshko ◽  
Mirko Marinkovski
Keyword(s):  
Mass Transfer ◽  
Activated Carbon ◽  
Simulation Study ◽  
Natural Zeolite ◽  
Granular Activated Carbon ◽  
Model Parameters ◽  
Mass Transfer Resistance ◽  
External Mass Transfer ◽  
Transfer Resistance ◽  
Kinetics Of

The isotherms and kinetics of zinc adsorption from aqueous solution onto granular activated carbon (GAC) and natural zeolite were studied using an agitated batch adsorber. The maximum adsorption capacities of GAC and natural zeolite towards zinc(II) from Langmuir adsorption isotherms were determined using experimental adsorption equilibrium data. The homogeneous solid diffusion model (HSD-model) combined with external mass transfer resistance was applied to fit the experimental kinetic data. The kinetics simulation study was performed using a computer program based on the proposed mathematical model and developed using gPROMS. As the two-mass transfer resistance approach was applied, two model parameters were fitted during the simulation study. External mass transfer and solid phase diffusion coefficients were obtained to predict the kinetic curves for varying initial Zn(II) concentration at constant agitation speed and constant adsorbent mass. For any particular Zn(II) - adsorbent system, k f was constant, except for the lowest initial concentration, while D s was found to increase with increasing initial Zn(II) concentration.

scholarly journals Performance Evaluation for Repair of HSGc-C5 Carcinoma Cell Using Geant4-DNA

Cancers ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 6046
Author(s):  
Dousatsu Sakata ◽  
Masao Suzuki ◽  
Ryoichi Hirayama ◽  
Yasushi Abe ◽  
Masayuki Muramatsu ◽  
...  
Keyword(s):  
Dna Damage ◽  
Radiation Treatment ◽  
Carcinoma Cell ◽  
Carcinoma Cells ◽  
Model Parameters ◽  
Track Structure ◽  
V79 Cells ◽  
Damage Repair ◽  
Kinetics Of ◽  
Mev Protons

Track-structure Monte Carlo simulations are useful tools to evaluate initial DNA damage induced by irradiation. In the previous study, we have developed a Gean4-DNA-based application to estimate the cell surviving fraction of V79 cells after irradiation, bridging the gap between the initial DNA damage and the DNA rejoining kinetics by means of the two-lesion kinetics (TLK) model. However, since the DNA repair performance depends on cell line, the same model parameters cannot be used for different cell lines. Thus, we extended the Geant4-DNA application with a TLK model for the evaluation of DNA damage repair performance in HSGc-C5 carcinoma cells which are typically used for evaluating proton/carbon radiation treatment effects. For this evaluation, we also performed experimental measurements for cell surviving fractions and DNA rejoining kinetics of the HSGc-C5 cells irradiated by 70 MeV protons at the cyclotron facility at the National Institutes for Quantum and Radiological Science and Technology (QST). Concerning fast- and slow-DNA rejoining, the TLK model parameters were adequately optimized with the simulated initial DNA damage. The optimized DNA rejoining speeds were reasonably agreed with the experimental DNA rejoining speeds. Using the optimized TLK model, the Geant4-DNA simulation is now able to predict cell survival and DNA-rejoining kinetics for HSGc-C5 cells.

scholarly journals Modular Dynamic Biomolecular Modelling: The Unification of Stoichiometry, Thermodynamics, Kinetics and Data

2021 ◽  
Author(s):  
Peter J. Gawthrop ◽  
Michael Pan ◽  
Edmund J. Crampin
Keyword(s):  
Dynamic Models ◽  
System Development ◽  
Kinetic Modelling ◽  
Simulation Models ◽  
Bond Graph ◽  
Model Parameters ◽  
Biomolecular Systems ◽  
Genome Wide ◽  
Estimation Of Model Parameters ◽  
Genome Scale

AbstractRenewed interest in dynamic simulation models of biomolecular systems has arisen from advances in genome-wide measurement and applications of such models in biotechnology and synthetic biology. In particular, genome-scale models of cellular metabolism beyond the steady state are required in order to represent transient and dynamic regulatory properties of the system. Development of such whole-cell models requires new modelling approaches. Here we propose the energy-based bond graph methodology, which integrates stoichiometric models with thermo-dynamic principles and kinetic modelling. We demonstrate how the bond graph approach intrinsically enforces thermodynamic constraints, provides a modular approach to modelling, and gives a basis for estimation of model parameters leading to dynamic models of biomolecular systems. The approach is illustrated using a well-established stoichiometric model of E. coli and published experimental data.

Experimental and Kinetic Production of Ethanol Using Mucilage Juice Residues from Cocoa Processing

2018 ◽  
Vol 16 (11) ◽  
Author(s):  
Teresa Romero Cortes ◽  
Jaime A. Cuervo-Parra ◽  
Víctor José Robles-Olvera ◽  
Eduardo Rangel Cortes ◽  
Pablo A. López Pérez
Keyword(s):  
Ethanol Yield ◽  
Scale Up ◽  
Pilot Scale ◽  
Product Formation ◽  
Model Parameters ◽  
Pichia Kudriavzevii ◽  
Proposed Model ◽  
Analysis Of Results ◽  
Residual Plots ◽  
Kinetics Of

AbstractEthanol was produced using mucilage juice residues from processed cocoa with Pichia kudriavzevii in batch fermentation. Experimental results showed that maximum ethanol concentration was 13.8 g/L, ethanol yield was 0.50 g-ethanol/g glucose with a productivity of 0.25 g/L h. Likewise, a novel phenomenological model based on the mechanism of multiple parallel coupled reactions was used to describe the kinetics of substrate, enzyme, biomass and product formation. Model parameters were optimized by applying the Levenberg-Marquardt approach. Analysis of results was based on statistical metrics (such as confidence interval), sensitivity and by comparing calculated curves with the experimental data (residual plots). The efficacy of the proposed mathematical model was statistically evaluated using the dimensionless coefficient for efficiency. Results indicated that the proposed model can be applied as a way of augmenting bioethanol production from laboratory scale up to semi-pilot scale.

Genetic Algorithm-Based Maximum Likelihood Parameter Estimation for Transit Buses

2001 ◽  
Author(s):  
Jie Xiao ◽  
Bohdan T. Kulakowski
Keyword(s):  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Dynamic Models ◽  
Estimation Method ◽  
Optimization Techniques ◽  
Model Parameters ◽  
Parameter Estimation Method ◽  
Transit Buses ◽  
Simulation And Control ◽  
And Control

Abstract Vehicle dynamic models include parameters that qualify the dependence of input forces and moments on state and control variables. The accuracy of the model parameter estimates is important for modeling, simulation, and control. In general, the most accurate method for determining values of model parameters is by direct measurement. However, some parameters of vehicle dynamics, such as suspension damping or moments of inertia, are difficult to measure accurately. This study aims at establishing an efficient and accurate parameter estimation method for developing dynamic models for transit buses, such that this method can be easily implemented for simulation and control design purposes. Based on the analysis of robustness, as well as accuracy and efficiency of optimization techniques, a parameter estimation method that integrates Genetic Algorithms and the Maximum Likelihood Estimation is proposed. Choices of output signals and estimation criterion are discussed involving an extensive sensitivity analysis of the predicted output with respect to model parameters. Other experiment-related aspects, such as imperfection of data acquisition, are also considered. Finally, asymptotic Cramer-Rao lower bounds for the covariance of estimated parameters are obtained. Computer simulation results show that the proposed method is superior to gradient-based methods in accuracy, as well as robustness to the initial guesses and measurement uncertainty.

scholarly journals Suboptimal Bacillus licheniformis and Bacillus weihenstephanensis Spore Incubation Conditions Increase Heterogeneity of Spore Outgrowth Time

2020 ◽  
Vol 86 (6) ◽  
Author(s):  
C. Trunet ◽  
N. Mtimet ◽  
A.-G. Mathot ◽  
F. Postollec ◽  
I. Leguerinel ◽  
...  
Keyword(s):  
Heat Treatment ◽  
Flow Cytometry ◽  
Bacillus Licheniformis ◽  
Model Parameters ◽  
Vegetative Cells ◽  
Content Type ◽  
Bacillus Weihenstephanensis ◽  
Spore Outgrowth ◽  
Growth Limits ◽  
Kinetics Of

ABSTRACT Changes with time of a population of Bacillus weihenstephanensis KBAB4 and Bacillus licheniformis AD978 dormant spores into germinated spores and vegetative cells were followed by flow cytometry, at pH ranges of 4.7 to 7.4 and temperatures of 10°C to 37°C for B. weihenstephanensis and 18°C to 59°C for B. licheniformis. Incubation conditions lower than optimal temperatures or pH led to lower proportions of dormant spores able to germinate and extended time of germination, a lower proportion of germinated spores able to outgrow, an extension of their times of outgrowth, and an increase of the heterogeneity of spore outgrowth time. A model based on the strain growth limits was proposed to quantify the impact of incubation temperature and pH on the passage through each physiological stage. The heat treatment temperature or time acted independently on spore recovery. Indeed, a treatment at 85°C for 12 min or at 95°C for 2 min did not have the same impact on spore germination and outgrowth kinetics of B. weihenstephanensis despite the fact that they both led to a 10-fold reduction of the population. Moreover, acidic sporulation pH increased the time of outgrowth 1.2-fold and lowered the proportion of spores able to germinate and outgrow 1.4-fold. Interestingly, we showed by proteomic analysis that some proteins involved in germination and outgrowth were detected at a lower abundance in spores produced at pH 5.5 than in those produced at pH 7.0, maybe at the origin of germination and outgrowth behavior of spores produced at suboptimal pH. IMPORTANCE Sporulation and incubation conditions have an impact on the numbers of spores able to recover after exposure to sublethal heat treatment. Using flow cytometry, we were able to follow at a single-cell level the changes in the physiological states of heat-stressed spores of Bacillus spp. and to discriminate between dormant spores, germinated spores, and outgrowing vegetative cells. We developed original mathematical models that describe (i) the changes with time of the proportion of cells in their different states during germination and outgrowth and (ii) the influence of temperature and pH on the kinetics of spore recovery using the growth limits of the tested strains as model parameters. We think that these models better predict spore recovery after a sublethal heat treatment, a common situation in food processing and a concern for food preservation and safety.

Innovative Computing Techniques for Development of an Integrated Computer Control System

1992 ◽  
Vol 26 (5-6) ◽  
pp. 1365-1374 ◽  
Author(s):  
G. G. Patry ◽  
M. W. Barnett
Keyword(s):  
Wastewater Treatment ◽  
Object Oriented Programming ◽  
Plant Performance ◽  
Organic Load ◽  
Model Parameters ◽  
Process Data ◽  
Wastewater Treatment Process ◽  
Deterministic Modelling ◽  
Linear Differential ◽  
And Control

Over the past decade there has been a shift in emphasis from design and construction of wastewater treatment facilities to operation. Poor plant performance, high costs and damage to the environment have resulted from operational problems. Wastewater treatment consists of a complex sequence of inter-dependent biological, physical and chemical processes subject to time-varying hydraulic and organic load conditions. Wastewater treatment process operation and control is a knowledge intensive task. Research on improving operation and control has centred on identifying important mechanisms responsible for observed behaviour and modelling both the process and optimum ways of operating the process. These models have served as useful tools for improving operation and control. Many different approaches have been used, including deterministic modelling, stochastic modelling and, more recently, linguistic modelling. Complex mathematical models of wastewater treatment processes consisting of large numbers of non-linear differential equations can be constructed using tools such as the General Purpose Simulator (GPS) and, given appropriate data, model parameters can be evaluated and updated using existing optimization routines. Object oriented programming (OOP) and a model based reasoning (MBR) approach provides a useful framework for development of deep-knowledge expert systems (ES). Data-driven modelling methods, including both time series analysis and artificial neural network (ANN) techniques, can also be employed to make maximum use of information contained in process data. Each of these model types is a necessary component of a computer system for operational control of wastewater treatment but, in isolation, none are sufficient for making the system robust. An integrated environment for combining these techniques has been developed for this purpose and the basis for its development is described.

Topology Optimization for Piecewise Dynamic Models Using a Genetic Algorithm

2010 ◽  
Author(s):  
Yi Liu ◽  
Dragan Djurdjanovic
Keyword(s):  
Genetic Algorithm ◽  
Topology Optimization ◽  
Dynamic Model ◽  
Dynamic Models ◽  
Optimization Procedure ◽  
Nonlinear Dynamic System ◽  
Local Model ◽  
Model Parameters ◽  
Highly Nonlinear ◽  
Node Connectivity

It has been demonstrated in the previous research that the node connectivity in the graph encoding the topological neighborhood relationships between local models in a piecewise dynamic model may significantly affect the cooperative learning process. It was shown that a graph with a larger connectivity leads to a quicker learning adaption due to more rapidly decaying transients of the estimation of local model parameters. In the same time, it was shown that the accuracy could be degraded by a larger bias in the asymptotic portion of the estimations of local model parameters. The efforts in topology optimization should therefore strive towards a high accuracy of the asymptotic portion of the estimator of local model parameters while simultaneously accelerating the decay of the estimation transients. In this paper, we pursue minimization of the residual sum of squares of a piecewise dynamic model after a predetermined number of training steps. The optimization of inter-model topology is implemented via a genetic algorithm that manipulates adjacency matrices of the graph underlying the piecewise dynamic model. An example of applying the topology optimization procedure on a peicewise linear model of a highly nonlinear dynamic system is provided to show the efficacy of the new method.

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