Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.5194/acp-17-8021-2017 |
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 | |
Berkemeier, Thomas1,2; Ammann, Markus3; Krieger, Ulrich K.4; Peter, Thomas4; Spichtinger, Peter5; Pschl, Ulrich1; Shiraiwa, Manabu1,6; Huisman, Andrew J.7 | |
2017-06-30 | |
发表期刊 | ATMOSPHERIC CHEMISTRY AND PHYSICS
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ISSN | 1680-7316 |
EISSN | 1680-7324 |
出版年 | 2017 |
卷号 | 17期号:12 |
文章类型 | Article |
语种 | 英语 |
国家 | Germany; USA; Switzerland |
英文摘要 | 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. |
领域 | 地球科学 |
收录类别 | SCI-E |
WOS记录号 | WOS:000404771300009 |
WOS关键词 | SECONDARY ORGANIC AEROSOL ; GAS-PARTICLE INTERACTIONS ; MOLECULAR-DYNAMICS SIMULATIONS ; CLOUD SURFACE-CHEMISTRY ; MULTILAYER MODEL ; TROPOSPHERIC DEGRADATION ; HETEROGENEOUS REACTIONS ; ATMOSPHERIC AEROSOLS ; PHASE CHEMISTRY ; BULK DIFFUSION |
WOS类目 | Environmental Sciences ; Meteorology & Atmospheric Sciences |
WOS研究方向 | Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/20548 |
专题 | 地球科学 |
作者单位 | 1.Max Planck Inst Chem, Multiphase Chem Dept, D-55128 Mainz, Germany; 2.Georgia Inst Technol, Sch Chem & Biomol Engn, Atlanta, GA 30320 USA; 3.Paul Scherrer Inst, Lab Environm Chem, CH-5232 Villigen, Switzerland; 4.Swiss Fed Inst Technol, Inst Atmospher & Climate Sci, CH-8092 Zurich, Switzerland; 5.Johannes Gutenberg Univ Mainz, Inst Atmospher Phys, D-55128 Mainz, Germany; 6.Univ Calif Irvine, Dept Chem, Irvine, CA 92697 USA; 7.Union Coll, Dept Chem, Schenectady, NY 12308 USA |
推荐引用方式 GB/T 7714 | Berkemeier, Thomas,Ammann, Markus,Krieger, Ulrich K.,et al. 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[J]. ATMOSPHERIC CHEMISTRY AND PHYSICS,2017,17(12). |
APA | Berkemeier, Thomas.,Ammann, Markus.,Krieger, Ulrich K..,Peter, Thomas.,Spichtinger, Peter.,...&Huisman, Andrew J..(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). |
MLA | Berkemeier, Thomas,et al."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(2017). |
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