Column and batch reactive transport experiment parameter estimation using a genetic algorithm

This paper focuses on the application of a genetic algorithm (GA) in estimating the fate and transport parameters of a reacting solute from the column and batch experiments involving a saturated porous medium. A program is developed using C++ to model the column and batch data using kinetically controlled one- or two-site sorption models including linear and/or nonlinear forms. The objective of the algorithm is to minimize the sum of squared differences between the measured and modeled solute concentration data associated with column effluent (i.e., “breakthrough curves”). The GA is capable of estimating transport and reactions parameters such as forward and reverse reaction rates and parameters of the nonlinear reaction models, from a given set of measured data. Further simulations have been performed to estimate the appropriate configurations of the GA, which assist the method in estimating the fate and transport parameters more efficiently. It is shown that a wide range of the GA parameters can lead to convergence to appropriate estimations. The results obtained from this study show that the capability of GAs to fit the column and batch experiment data is promising.