Document Type
Article
Version Deposited
Published Version
Publication Date
9-20-2019
Publication Title
Processes
DOI
10.3390/pr7100640
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
Recommended Citation
Torlapati, J. & Clement, T.P. (2019). Using Parallel Genetic Algorithms for Estimating Model Parameters in Complex Reactive Transport Problems. Processes 2019, 7, 640.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Comments
Processes is an Open Access journal published by MDPI.