Identifiability of rate coefficients in linear reaction networks from isothermal transient experimental data

2010 ◽  
Vol 65 (7) ◽  
pp. 2333-2343 ◽  
Author(s):  
Raf Roelant ◽  
Denis Constales ◽  
Roger Van Keer ◽  
Guy B. Marin
2021 ◽  
pp. 108760
Author(s):  
Ariane Ernst ◽  
Christof Schütte ◽  
Stephan J. Sigrist ◽  
Stefanie Winkelmann

1998 ◽  
Vol 16 (7) ◽  
pp. 838-846 ◽  
Author(s):  
A. S. Kirillov

Abstract. The first-order perturbation approximation is applied to calculate the rate coefficients of vibrational energy transfer in collisions involving vibrationally excited molecules in the absence of non-adiabatic transitions. The factors of molecular attraction, oscillator frequency change, anharmonicity, 3-dimensionality and quasiclassical motion have been taken into account in the approximation. The analytical expressions presented have been normalized on experimental data of VT-relaxation times in N2 and O2 to obtain the steric factors and the extent of repulsive exchange potentials in collisions N2-N2 and O2-O2. The approach was applied to calculate the rate coefficients of vibrational-vibrational energy transfer in the collisions N2-N2, O2-O2 and N2-O2. It is shown that there is good agreement between our calculations and experimental data for all cases of energy transfer considered.Key words. Ionosphere (Auroral ionosphere; ion chemistry and composition). Atmospheric composition and structure (Aciglow and aurora).


SIMULATION ◽  
1967 ◽  
Vol 8 (3) ◽  
pp. 133-137 ◽  
Author(s):  
Richard A. Nesbit ◽  
Robert D. Engel

A program for matching experimental data to the com puted concentrations of various components of a dynamic chemical process is implemented. The digital subsection of the computer is programmed to execute a steepest descent search procedure. The analog section is programmed to solve the chemical rate equations which simulate the re action dynamics. These equations form a two-point bound ary value problem. The search procedure changes param eters in the rate equations, and it compares the computed concentrations to the experimental ones. The squared error is summed over all data points, and this sum is minimized by the search. Significant speedup of the solution to this type of problem is possible with the hy brid system due to the fast solution of the differential equations on the analog and the automated search pro cedure on the digital.


2017 ◽  
Vol 17 (12) ◽  
pp. 8021-8029 ◽  
Author(s):  
Thomas Berkemeier ◽  
Markus Ammann ◽  
Ulrich K. Krieger ◽  
Thomas Peter ◽  
Peter Spichtinger ◽  
...  

Abstract. We present a Monte Carlo genetic algorithm (MCGA) for efficient, automated, and unbiased global optimization of model input parameters by simultaneous fitting to multiple experimental data sets. The algorithm was developed to address the inverse modelling problems associated with fitting large sets of model input parameters encountered in state-of-the-art kinetic models for heterogeneous and multiphase atmospheric chemistry. The MCGA approach utilizes a sequence of optimization methods to find and characterize the solution of an optimization problem. It addresses an issue inherent to complex models whose extensive input parameter sets may not be uniquely determined from limited input data. Such ambiguity in the derived parameter values can be reliably detected using this new set of tools, allowing users to design experiments that should be particularly useful for constraining model parameters. We show that the MCGA has been used successfully to constrain parameters such as chemical reaction rate coefficients, diffusion coefficients, and Henry's law solubility coefficients in kinetic models of gas uptake and chemical transformation of aerosol particles as well as multiphase chemistry at the atmosphere–biosphere interface. While this study focuses on the processes outlined above, the MCGA approach should be portable to any numerical process model with similar computational expense and extent of the fitting parameter space.


2002 ◽  
Vol 2 (3) ◽  
pp. 669-687 ◽  
Author(s):  
D. A. Knopf ◽  
T. Koop ◽  
B. P. Luo ◽  
U. G. Weers ◽  
T Peter

Abstract. The nucleation of NAD and NAT from HNO3/H2O and HNO3/H2O/H2SO4 solution droplets is investigated both theoretically and experimentally with respect to the formation of polar stratospheric clouds (PSCs). Our analysis shows that homogeneous NAD and NAT nucleation from liquid aerosols is insufficient to explain the number densities of large nitric acid containing particles recently observed in the Arctic stratosphere. This conclusion is based on new droplet freezing experiments employing optical microscopy combined with Raman spectroscopy. The homogeneous nucleation rate coefficients of NAD and NAT in liquid aerosols under polar stratospheric conditions derived from the experiments are < 2 x 10-5 cm-3 s-1 and < 8 x 10-2 cm-3 s-1 , respectively. These nucleation rate coefficients are smaller by orders of magnitude than the value of ~ 103 cm-3 s-1 used in a recent denitrification modelling study that is based on a linear extrapolation of laboratory nucleation data to stratospheric conditions (Tabazadeh et al., Science, 291, 2591--2594, 2001). We show that this linear extrapolation is in disagreement with thermodynamics and experimental data and, therefore, must not be used in microphysical models of PSCs. Our analysis of the experimental data yields maximum hourly production rates of nitric acid hydrate particles per cm3 of air of about 3 x 10-10 cm-3 h-1 under polar stratospheric conditions. Assuming PSC particle production to proceed at this rate for two months we arrive at particle number densities of < 5 x 10-7 cm-3, much smaller than the value of ~ 10-4 cm-3 reported in recent field observations. This clearly shows that homogeneous nucleation of NAD and NAT from liquid supercooled ternary solution aerosols cannot explain the observed polar denitrification.


2019 ◽  
Vol 490 (1) ◽  
pp. 709-717
Author(s):  
Tetsuo Yamamoto ◽  
Hitoshi Miura ◽  
Osama M Shalabiea

ABSTRACT We propose a new mechanism of desorption of molecules from dust surface heated by exothermic reactions and derive a formula for the desorption probability. This theory includes no parameter that is physically ambiguous. It can predict the desorption probabilities not only for one-product reactions but also for multiproduct reactions. Furthermore, it can predict desorption probability of a pre-adsorbed molecule induced by a reaction at a nearby site. This characteristic will be helpful to verify the theory by the experiments which involve complex reaction networks. We develop a quantitative method of comparing the predicted desorption probability with the experiments. This method is also applied to the theories proposed so far. It is shown that each of them reproduces the experiments with similar precision, although the amount of systematic experimental data that give definite desorption probability are limited at present. We point out the importance of clarifying the nature of the substrate used in the experiment, in particular, its thermal diffusivity. We show a way to estimate the substrate properties from systematic desorption experiments without their direct measurements.


1989 ◽  
Vol 8 ◽  
pp. 369-374 ◽  
Author(s):  
T. J. Millar

ABSTRACTChemical models of dense interstellar clouds are reviewed with particular emphasis on recent results. The need for theoretical and experimental data on rate coefficients is pointed out and some observational studies are suggested.


2017 ◽  
Author(s):  
Thomas Berkemeier ◽  
Markus Ammann ◽  
Ulrich K. Krieger ◽  
Thomas Peter ◽  
Peter Spichtinger ◽  
...  

Abstract. We present a Monte-Carlo Genetic Algorithm (MCGA) for efficient, automated and unbiased global optimization of model input parameters by simultaneous fitting to multiple experimental data sets. The algorithm was developed to address the inverse modelling problems associated with fitting large sets of model input parameters encountered in state-of-the-art kinetic models for heterogeneous and multiphase atmospheric chemistry. The MCGA approach utilizes a sequence of optimization methods to find the solution of an optimization problem and to explore the space of solutions with similar model output. It addresses a problem inherent to complex models whose extensive input parameter sets might not be uniquely determined from limited input data. Such ambiguity in the derived parameter values can be reliably detected using this new set of tools. The MCGA algorithm has been used successfully to constrain parameters such as reaction rate coefficients, diffusion coefficients and Henry's law solubility coefficients in kinetic models of gas uptake and chemical transformation of aerosol particles as well as multiphase chemistry at the atmosphere-biosphere interface. It should be portable to any numerical model with similar computational expense and extent of the fitting parameter space.


SIMULATION ◽  
1968 ◽  
Vol 10 (2) ◽  
pp. 70-72
Author(s):  
Richard A. Nesbit ◽  
Robert D. Engel

A program for matching experimental data to the com puted concentrations of various components of a dynamic chemical process is implemented. The digital subsection of the computer is programmed to execute a steepest descent search procedure. The analog section is programmed to solve the chemical rate equations which simulate the re action dynamics. These equations form a two-point bound ary value problem. The search procedure changes param eters in the rate equations, and it compares the computed concentrations to the experimental ones. The squared error is summed over all data points, and this sum is minimized by the search. Significant speedup of the solution to this type of problem is possible with the hybrid system due to the fast solution of the differential equations on the analog and the automated search pro cedure on the digital.


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