Collaborative data processing in developing predictive models of complex reaction systems

Frenklach, Michael; Packard, Andrew; Seiler, Peter; Feeley, Ryan

doi:10.1002/kin.10172

Cited by 123 publications

(142 citation statements)

References 9 publications

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“…We also increase the state-space adequately, so that the "escape" probability is reduced significantly. As σ is varied in this modified problem, our methodology gives the following results, which we compare with probability estimates obtained by direct simulations of the stochastic differential equation in (40). When σ = 1, 98% of the simulations enter the region around the other equilibrium, whereas our methodology gives an upper bound of β = 0.99.…”

Section: Validation/invalidation Of the Model Of Heat Shock Response mentioning

confidence: 95%

“…The presence of this noise source poses new questions. For example, while in the deterministic description Figure 5: Setup for computing the bound of the probability of false switching because of noise, for system (40).…”

Section: Validation/invalidation Of the Model Of Heat Shock Response mentioning

confidence: 99%

“…Indeed, the way the noise enters in (40) means that if the state-space X is large enough (say the whole real line) and the time is infinite, the probability of reaching every point in the state-space should be 1.…”

Section: Validation/invalidation Of the Model Of Heat Shock Response mentioning

confidence: 99%

“…However, we can use SOS methods if we first approximate M (u, p) by a polynomial S(u, p) in the region of interest. S is referred to as the surrogate model [35,40]. The difference between M and S can be made arbitrarily small if the order of S is high enough.…”

Section: Model Validation/invalidation For the G-protein Signaling Inmentioning

confidence: 99%

“…The difference between M and S can be made arbitrarily small if the order of S is high enough. Different types of surrogate models can be considered; one is to use response surface methods to generate a quadratic surrogate model as in [40]. If we assume the approximation error is bounded on the feasible parameter set, i.e.…”

Section: Model Validation/invalidation For the G-protein Signaling Inmentioning

confidence: 99%

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Enlightened Multiscale Simulation of Biochemical Networks. Core Theory, Validating Experiments, and Implementation in Open Software

Doyle¹,

Hucka²

2006

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Section: Validation/invalidation Of the Model Of Heat Shock Response mentioning

confidence: 95%

Section: Validation/invalidation Of the Model Of Heat Shock Response mentioning

confidence: 99%

Section: Validation/invalidation Of the Model Of Heat Shock Response mentioning

confidence: 99%

Section: Model Validation/invalidation For the G-protein Signaling Inmentioning

confidence: 99%

Section: Model Validation/invalidation For the G-protein Signaling Inmentioning

confidence: 99%

See 3 more Smart Citations

Enlightened Multiscale Simulation of Biochemical Networks. Core Theory, Validating Experiments, and Implementation in Open Software

Doyle¹,

Hucka²

2006

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Computational issues in hybrid multizonal/computational fluid dynamics models

2005

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in Wiley InterScience (www.interscience.wiley.com). Hybrid multizonal/computational fluid dynamics (CFD) models provide a means of introducing a more realistic description of fluid mechanics and mixing phenomena within process models. The solution of the CFD submodel implicitly defines a function relating one or more of the variables in the multizonal model (the function outputs) in terms of another subset of the variables (the function inputs). This paper is concerned with the accurate and efficient evaluation of this function using local approximate models (LAMs) that may have a general mathematical structure, or be based on physical correlations.Practical issues relating to the robustness of the solution of hybrid models are also considered, and a general architecture for the software interface between the multizonal and CFD submodels is presented. The effectiveness and efficiency of the overall approach are tested by two applications relating, respectively, to a stirred-tank chemical reactor fitted with a cooling jacket and to a stirred-tank bioreactor.

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Uncertainty analysis of NO production during methane combustion

Zsély

Zádor

Turányi

2008

Int J of Chemical Kinetics

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Local and Monte Carlo uncertainty analyses of NO production during methane combustion were carried out, investigating the effect of uncertainties of kinetic parameters and enthalpies of formation. In Case I, the original Leeds methane oxidation mechanism with the NO x reaction block was used, but the enthalpies of formation of all species were updated. In Case II, the NCN-containing reactions of the prompt NO formation route were added and the rate parameters of several reactions were also updated. The NO production was examined at the conditions of the Bartok et al. experiments (PSR, T = 1565-1989 K, ϕ = 0.8-1.2, residence time 3 ms). The Monte Carlo analysis provided the approximate probability density function and the variance of the calculated NO concentration, and also its attainable minimum and maximum values. Both mechanisms provided similarly good to acceptable agreement with the experimental results for lean and stoichiometric mixtures, whereas only mechanism Case II could reproduce the experimental data for rich mixtures after a realistic tuning of the parameters. Local uncertainty analysis was used to assess the contribution of the uncertainty of each parameter to the uncertainty of the calculated NO concentration. Enthalpies of formation of NNH and HCCO, and rate parameters of 20 reaction steps cause most of the uncertainty of the calculated NO concentrations at all conditions. The relative importance of the four main NO formation routes was investigated via the inspection of the reaction rates, embedded in the Monte Carlo analysis. NO formation in rich mixtures was dominated by the prompt route, whereas in leaner mixtures the share of the NO formation routes depended very much on the values of rate parameters, when varied within the uncertainty limits of kinetic data evaluations.

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Collaborative data processing in developing predictive models of complex reaction systems

Cited by 123 publications

References 9 publications

Enlightened Multiscale Simulation of Biochemical Networks. Core Theory, Validating Experiments, and Implementation in Open Software

Enlightened Multiscale Simulation of Biochemical Networks. Core Theory, Validating Experiments, and Implementation in Open Software

Computational issues in hybrid multizonal/computational fluid dynamics models

Uncertainty analysis of NO production during methane combustion

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