Abstract
In this study, we present the details of an optimization method for parameter estimation of one-dimensional groundwater reactive transport problems using a parallel genetic algorithm (PGA). The performance of the PGA was tested with two problems that had published analytical solutions and two problems with published numerical solutions. The optimization model was provided with the published experimental results and reasonable bounds for the unknown kinetic reaction parameters as inputs. Benchmarking results indicate that the PGA estimated parameters that are close to the published parameters and it also predicted the observed trends well for all four problems. Also, OpenMP FORTRAN parallel constructs were used to demonstrate the speedup of the code on an Intel quad-core desktop computer. The parallel code showed a linear speedup with an increasing number of processors. Furthermore, the performance of the underlying optimization algorithm was tested to evaluate its sensitivity to the various genetic algorithm (GA) parameters, including initial population size, number of generations, and parameter bounds. The PGA used in this study is generic and can be easily scaled to higher-order water quality modeling problems involving real-world applications.
Highlights
Reactive transport models have been commonly used to simulate the fate and transport of contaminants in both laboratory and field-scale problems
Based on our literature review, we found that only a limited amount of information is available in the hydrogeology literature in analyzing problems in an OpenMP platform to optimally use a genetic algorithm (GA) for estimating model parameters in multicomponent reactive transport models
The benchmark problems chosen in this study have analytical solutions or published numerical solutions for unknown parameters
Summary
Reactive transport models have been commonly used to simulate the fate and transport of contaminants in both laboratory and field-scale problems. The accuracy and reliability of these models would strongly depend on the values of model parameters, which are commonly estimated from controlled laboratory and/or field experiments. These experiments are often conducted by isolating certain reaction steps to fully understand the complex biogeochemical interactions occurring in the subsurface. Some type of numerical inverse routine is employed (e.g., CXTFIT [5]) to automatically estimate these unknown model parameters Several of these inverse methods can converge to a local minimum and their overall performance depends on the robustness of the search algorithm and the choice of the initial parameters supplied by the user [5]. Doherty and Hunt [6] developed a robust parameter estimator, Processes 2019, 7, 640; doi:10.3390/pr7100640 www.mdpi.com/journal/processes
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