Kinetic parameter estimation from TGA: Optimal design of TGA experiments

This work presents a general methodology to determine kinetic models of solid thermal decomposition with thermogravimetric analysis (TGA) instruments. The goal is to determine a simple and robust kinetic model for a given solid with the minimum of TGA experiments. From this last point of view, this work can be seen as an attempt to find the optimal design of TGA experiments for kinetic modelling. Two computation tools were developed. The first is a nonlinear parameter estimation procedure for identifying parameters in nonlinear dynamical models. The second tool computes the thermogravimetric experiment (here, the programmed temperature profile applied to the thermobalance) required in order to identify the best kinetic parameters, i.e. parameters with a higher statistical reliability. The combination of the two tools can be integrated in an iterative approach generally called sequential strategy. The application concerns the thermal degradation of cardboard in a Setaram TGA instrument and the results that are presented demonstrate the improvements in the kinetic parameter estimation process

Kinetic Parameter Estimation Using Modified Differential Evolution

For the development of mathematical models in chemical engineering, the parameter estimation methods are very important as design, optimization and advanced control of chemical processes depend on values of model parameters obtained from experimental data. Nonlinearity in models makes the estimation of parameter more difficult and more challenging. This paper presents an evolutionary computation approach for solving such problems. In this work, a modified version of Differential Evolution (DE) algorithm [named Modified Differential evolution (MDE)] is used to solve a kinetic parameter estimation problem from chemical engineering field. The computational efficiency of MDE is compared with that of original DE and Trigonometric Differential Evolution (TDE). Results indicate that performance of MDE algorithm is better than that of DE and TDE

Mathematical modelling of baculovirus infection process: Kinetic parameter estimation

Although there are several mathematical models present for baculovirus infection, the specific functions for insect cell growth and cell death during infection processes remain unknown. Specifically, it is challenging to identify the most suitable model from a large set of plausible models and estimate the kinetic parameters to account for the day to day variability present in the infection experiments. In this context, identification of an unstructured model that can predict the day to day variability in cell growth and cell viability can be useful in determining the optimal operating conditions in fermenters at industrial scale. The major objectives of the present work were to develop a model screening framework that can be used to select the best model and identify the growth and death mechanisms during viral infection through non-linear programming. We then constructed a series of plausible models based on system of ordinary differential equations and performed the model selection using experimental data obtained from shaker flasks. The proposed scheme was tested for selecting the model for uninfected cell growth profiles. The objective function used was the root mean square error between the predicted values and experimental data points obtained from triplicate dataset. The computational scheme was validated using two types of virus, the WT AcMNPV and stabilized AcMNPV. Additionally, we propose a numerical scheme to simulate the cell growth and cell viability during viral passaging. The kinetic parameters were estimated in case of growth of uninfected cells, cells infected with WT virus as well as stabilized AcMNPV. The result shows that Monods equation fits the best for insect cell growth without infection and infection with WT AcMNPV. Whereas, the Contois model fits the best for the stabilized virus. The simulated results also indicate that the day to day variability in cell growth and cell viability profile can be explained through the variation in the specific growth rate and the death rate. The estimated kinetic parameters indicate that the growth and death parameters undergo specific modifications during the passaging of viruses associated to infection process. Additionally, we propose an integrated model for the infection process that simulates the DNA replication, mRNA and protein expression as well as polyhedra production. Specifically, we present the comparison between the unstructured model and the structured integrated model with respect to accuracy and computation time. Current study provides a predictive framework that has a potential application for large scale production of baculovirus

PSO-based Parameter Estimation of Nonlinear Kinetic Models for β-Mannanase Fermentation

Particle swarm optimization (PSO), as a novel evolutionary algorithm involved in social interaction for global space search, was firstly used in kinetic parameter estimation. Based on three developed nonlinear kinetic equations for bacterial cell growth, total sugar utilization and β-mannanase production by Bacillus licheniformis under the support of a batch fermentation process, various PSO algorithms as well as gene algorithms (GA) were developed to estimate kinetic parameters. The performance comparison among these algorithms indicates the improved PSO (Trelea 1) is most suitable for kinetic parameter estimation of β-mannanase fermentation. In order to find the physical-chemical-meanings of kinetic parameters from many optimized results, multiobjective optimization with a normalized weight method was adopted. The 9 desired parameters in equations were obtained by the Trelea 1 type PSO with two batches fermentation data, and the results predicted by the models were also in good agreement with the experimental observations

CADLIVE Optimizer: Web-based Parameter Estimation for Dynamic Models

Computer simulation has been an important technique to capture the dynamics of biochemical networks. In most networks, however, few kinetic parameters have been measured in vivo because of experimental complexity. We develop a kinetic parameter estimation system, named the CADLIVE Optimizer, which comprises genetic algorithms-based solvers with a graphical user interface. This optimizer is integrated into the CADLIVE Dynamic Simulator to attain efficient simulation for dynamic models

CADLIVE optimizer: web-based parameter estimation for dynamic models

Computer simulation has been an important technique to capture the dynamics of biochemical networks. In most networks, however, few kinetic parameters have been measured in vivo because of experimental complexity. We develop a kinetic parameter estimation system, named the CADLIVE Optimizer, which comprises genetic algorithms-based solvers with a graphical user interface. This optimizer is integrated into the CADLIVE Dynamic Simulator to attain efficient simulation for dynamic models

A Semi-parametric Technique for the Quantitative Analysis of Dynamic Contrast-enhanced MR Images Based on Bayesian P-splines

Dynamic Contrast-enhanced Magnetic Resonance Imaging (DCE-MRI) is an important tool for detecting subtle kinetic changes in cancerous tissue. Quantitative analysis of DCE-MRI typically involves the convolution of an arterial input function (AIF) with a nonlinear pharmacokinetic model of the contrast agent concentration. Parameters of the kinetic model are biologically meaningful, but the optimization of the non-linear model has significant computational issues. In practice, convergence of the optimization algorithm is not guaranteed and the accuracy of the model fitting may be compromised. To overcome this problems, this paper proposes a semi-parametric penalized spline smoothing approach, with which the AIF is convolved with a set of B-splines to produce a design matrix using locally adaptive smoothing parameters based on Bayesian penalized spline models (P-splines). It has been shown that kinetic parameter estimation can be obtained from the resulting deconvolved response function, which also includes the onset of contrast enhancement. Detailed validation of the method, both with simulated and in vivo data, is provided