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Open AccessArticle

Using Parallel Genetic Algorithms for Estimating Model Parameters in Complex Reactive Transport Problems

1
Civil and Environmental Engineering Department, Rowan University, Glassboro, NJ 08550, USA
2
Civil, Construction and Environmental Engineering Department, University of Alabama, Tuscaloosa, AL 35487, USA
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Author to whom correspondence should be addressed.
Processes 2019, 7(10), 640; https://doi.org/10.3390/pr7100640
Received: 6 June 2019 / Revised: 6 September 2019 / Accepted: 16 September 2019 / Published: 20 September 2019
(This article belongs to the Special Issue Water Quality Modelling)
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. View Full-Text
Keywords: parallel computing; genetic algorithms; reactive transport; parallel genetic algorithm; groundwater; water quality parallel computing; genetic algorithms; reactive transport; parallel genetic algorithm; groundwater; water quality
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MDPI and ACS Style

Torlapati, J.; Clement, P. Using Parallel Genetic Algorithms for Estimating Model Parameters in Complex Reactive Transport Problems. Processes 2019, 7, 640.

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