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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
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Volume 17, issue 12 | Copyright
Atmos. Chem. Phys., 17, 8021-8029, 2017
https://doi.org/10.5194/acp-17-8021-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Technical note 30 Jun 2017

Technical note | 30 Jun 2017

Technical note: Monte Carlo genetic algorithm (MCGA) for model analysis of multiphase chemical kinetics to determine transport and reaction rate coefficients using multiple experimental data sets

Thomas Berkemeier1,2, Markus Ammann3, Ulrich K. Krieger4, Thomas Peter4, Peter Spichtinger5, Ulrich Pöschl1, Manabu Shiraiwa1,6, and Andrew J. Huisman7 Thomas Berkemeier et al.
  • 1Multiphase Chemistry Department, Max Planck Institute for Chemistry, 55128 Mainz, Germany
  • 2School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, 30320, Atlanta, GA, USA
  • 3Laboratory of Environmental Chemistry, Paul Scherrer Institute, 5232 Villigen, Switzerland
  • 4Institute for Atmospheric and Climate Science, ETH Zurich, 8092 Zurich, Switzerland
  • 5Institute for Atmospheric Physics, Johannes Gutenberg University, 55128 Mainz, Germany
  • 6Department of Chemistry, University of California, Irvine, 92697, Irvine, CA, USA
  • 7Department of Chemistry, Union College, 12308, Schenectady, NY, USA

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.

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Kinetic process models are efficient tools used to unravel the mechanisms governing chemical and physical transformation in multiphase atmospheric chemistry. However, determination of kinetic parameters such as reaction rate or diffusion coefficients from multiple data sets is often difficult or ambiguous. This study presents a novel optimization algorithm and framework to determine these parameters in an automated fashion and to gain information about parameter uncertainty and uniqueness.
Kinetic process models are efficient tools used to unravel the mechanisms governing chemical and...
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