24,799 research outputs found

    Parameter identification problems in the modelling of cell motility

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    We present a novel parameter identification algorithm for the estimation of parameters in models of cell motility using imaging data of migrating cells. Two alternative formulations of the objective functional that measures the difference between the computed and observed data are proposed and the parameter identification problem is formulated as a minimisation problem of nonlinear least squares type. A Levenberg–Marquardt based optimisation method is applied to the solution of the minimisation problem and the details of the implementation are discussed. A number of numerical experiments are presented which illustrate the robustness of the algorithm to parameter identification in the presence of large deformations and noisy data and parameter identification in three dimensional models of cell motility. An application to experimental data is also presented in which we seek to identify parameters in a model for the monopolar growth of fission yeast cells using experimental imaging data. Our numerical tests allow us to compare the method with the two different formulations of the objective functional and we conclude that the results with both objective functionals seem to agree

    Inferring rate coefficents of biochemical reactions from noisy data with KInfer

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    Dynamical models of inter- and intra-cellular processes contain the rate constants of the biochemical reactions. These kinetic parameters are often not accessible directly through experiments, but they can be inferred from time-resolved data. Time resolved data, that is, measurements of reactant concentration at series of time points, are usually affected by different types of error, whose source can be both experimental and biological. The noise in the input data makes the estimation of the model parameters a very difficult task, as if the inference method is not sufficiently robust to the noise, the resulting estimates are not reliable. Therefore "noise-robust" methods that estimate rate constants with the maximum precision and accuracy are needed. In this report we present the probabilistic generative model of parameter inference implemented by the software prototype KInfer and we show the ability of this tool of estimating the rate coefficients of models of biochemical network with a good accuracy even from very noisy input data

    Structural sensitivity analysis: Methods, applications, and needs

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    Some innovative techniques applicable to sensitivity analysis of discretized structural systems are reviewed. These techniques include a finite-difference step-size selection algorithm, a method for derivatives of iterative solutions, a Green's function technique for derivatives of transient response, a simultaneous calculation of temperatures and their derivatives, derivatives with respect to shape, and derivatives of optimum designs with respect to problem parameters. Computerized implementations of sensitivity analysis and applications of sensitivity derivatives are also discussed. Finally, some of the critical needs in the structural sensitivity area are indicated along with Langley plans for dealing with some of these needs

    Design and Control of Intensified Membrane Reactor Systems Through Module-Based Design Approach

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    As interest in the modularization and intensification of chemical processes continues to grow, more research must be directed towards the modeling and analysis of intensified process units. Intensified process units such as membrane reactors pose unique challenges pertaining to design and operation that have not been fully addressed in the reported literature. This work aims to address the design and control challenges caused by the integration of phenomena and the loss of degrees of freedom (DOF) that occur in the intensification of modular membrane reactor units. First, a novel first-principles approach for modeling membrane reactors is developed using the AVEVA Process Simulation Platform’s equation-oriented capabilities. The produced model allows for the simulation of generalized membrane reactors under nonisothermal and countercurrent operation for the first time. This model is then applied to generate an operability input-output mapping to study how operating points translate to overall unit performance. This work demonstrates how operability analyses can be used to identify areas of improvement in membrane reactor design, other than just using operability mapping studies to identify optimal input conditions for process operations. Next, a novel approach to designing membrane reactor units is proposed. This approach consists of designing smaller modules based on specific phenomena such as heat exchange, reactions, and mass transport and combining them in series to produce the final modular membrane-based unit. This module-based approach to designing membrane reactors is then assessed using a process operability analysis to maximize the operability index, as a way of quantifying the operational performance of intensified processes. This work demonstrates that by designing membrane reactors in this way, the operability of the original membrane reactor design can be significantly enhanced, translating to an improvement in achievability for a potential control structure implementation. Although the demonstrated novel module-based design approach to membrane reactors could improve the operability index of membrane reactor systems, the computational time to determine such an optimal design made this class of design problems intractable to solve in a reasonable amount of time. So lastly, this work proposes a set of design heuristics for this new module-based design approach for membrane reactors. These heuristics are used in combination with a genetic algorithm to produce a novel, two-staged algorithm for the design and control of membrane reactor systems. The proposed algorithm leads to a reduction in computational time by about 2 orders of magnitude while also improving the operability index of the original membrane reactor design by 21%

    An Integrated Business and Engineering Framework for Synthesis and Design of Processing Networks

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