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Reactive transport parameter estimation: Genetic algorithm vs. Monte carlo approach
Author(s) -
Majdalani Samer,
Fahs Marwan,
Carrayrou Jérôme,
Ackerer Philippe
Publication year - 2009
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.11796
Subject(s) - monte carlo method , maxima and minima , genetic algorithm , chemical equilibrium , mathematical optimization , monte carlo algorithm , statistical physics , algorithm , computer science , chemistry , mathematics , physics , statistics , mathematical analysis
Abstract This article concerns reactive transport in porous media with an emphasis on the optimization of the chemical parameters. The transport of Cadmium (Cd) and tributyltin (TBT) in column experiments were used as test cases. The reactive transport model is described by a set of chemical reactions with equilibrium constants as the main adjustable parameters. As such a problem is highly nonlinear and can have multiple minima, global parameter estimation methods are more suitable than local gradient‐based methods. This article focuses on the application of a genetic algorithm (GA) in estimating chemical equilibrium parameters of a reactive transport model. The GA is capable of minimizing the difference between the measured and modeled breakthrough curves for both Cd and TBT. A comparison between GA and Monte‐Carlo approaches shows that the GA performance is better than the Monte‐Carlo, especially for a small number of evaluations of the cost function. The results of this study show that the use of GA to estimate the parameters of reactive transport models is promising. © 2009 American Institute of Chemical Engineers AIChE J, 2009