HR: 10:20h
AN: H11I-01    [PDF]
TI: A Self-Adaptive Hybrid Genetic Algorithm for Optimal Groundwater Remediation Design
AU: * Espinoza, F P
EM: fespinoz31@yahoo.com
AF: NRC Post-Doctoral Researcher, 26 W Martin Luther King Dr, Cincinnati, OH 45220 United States
AU: Minsker, B S
EM: minsker@uiuc.edu
AF: Department of Civil and Environmental Engineering, University of Illinois, 3230 Newmark Lab, MC-250, 205 N. Mathews Ave., Urbana, IL 61801 United States
AB: Identifying optimal designs for a groundwater remediation system is computationally intensive, especially for complex, nonlinear problems such as enhanced in situ bioremediation technology. To improve performance, we apply a hybrid genetic algorithm (HGA), which is a two-step solution method: a genetic algorithm (GA) for global search using the entire population and then a local search (LS) to improve search speed for only a few individuals in the population. The inclusion of local search helps to speed up the solution process and to make the solution technique more robust. The result of this research is a highly reliable numerical tool, the enhanced self-adaptive hybrid genetic algorithm (e-SAHGA) to more efficiently and effectively solve problems using simple genetic algorithms (SGAs). With this tool, the designer can evaluate different solution alternatives in a more timely fashion. The application of the e-SAHGA algorithm to a hypothetical groundwater remediation design problem showed 90% reliability in identifying the solution faster than the SGA, with average savings of 64% across 100 runs with different random initial populations. Finally, e-SAHGA was tested on a field-scale remediation design problem, re-evaluation of the remediation system for Umatilla Army Depot, by means of a domain decomposition approach. In this approach, well locations are identified first and then pumping rates are identified subsequently in separate GA runs. The domain decomposition approach was shown to be much faster than the full solution approach with no loss in accuracy of the final solution for this problem, with computational savings between 30% and 60%.
DE: 1831 Groundwater quality
DE: 1832 Groundwater transport
DE: 1871 Surface water quality
DE: 1884 Water supply
DE: 1899 General or miscellaneous
SC: Hydrology [H]
MN: 2003 Fall Meeting