English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  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

Berkemeier, T., Ammann, M., Krieger, U. K., Peter, T., Spichtinger, P., Pöschl, U., et al. (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. Atmospheric Chemistry and Physics, 17(12), 8021-8029. doi:10.5194/acp-17-8021-2017.

Item is

Files

show Files

Locators

show

Creators

show
hide
 Creators:
Berkemeier, T.1, Author           
Ammann, Markus2, Author
Krieger, Ulrich K.2, Author
Peter, Thomas2, Author
Spichtinger, Peter2, Author
Pöschl, U.1, Author           
Shiraiwa, M.1, Author           
Huisman, Andrew J.2, Author
Affiliations:
1Multiphase Chemistry, Max Planck Institute for Chemistry, Max Planck Society, ou_1826290              
2external, ou_persistent22              

Content

show
hide
Free keywords: -
 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.

Details

show
hide
Language(s): eng - English
 Dates: 2017
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: ISI: 000404771300009
DOI: 10.5194/acp-17-8021-2017
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Atmospheric Chemistry and Physics
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Katlenburg-Lindau, Germany : European Geosciences Union
Pages: - Volume / Issue: 17 (12) Sequence Number: - Start / End Page: 8021 - 8029 Identifier: ISSN: 1680-7316
CoNE: https://pure.mpg.de/cone/journals/resource/111030403014016