Using remediation time as an optimization variable in groundwater remediation systems
Optimization by the use of computer simulations is a useful tool for designing subsurface remediation systems. Most optimization studies focus on minimizing cost while meeting a cleanup goal within a given time frame. However, decision-makers may be interested in analyzing tradeoffs between cost and time. In this work, we employ a multi-objective optimization to minimize cost and time simultaneously. The optimization procedure uses a niched Pareto genetic algorithm with state variables (hydraulic, head and concentration) generated from a finite difference flow simulator and a particle tracking contaminant simulator. Computational experiments were performed to verify the mutli-objective trade-off curve with the use of single objective optimization runs. The effect of interest rate on cost-time tradeoffs was investigated with two financial management scenarios. The result of this work showed only a weak relationship between remediation cost and time. Further investigation of the results produced insight in to the aquifer and treatment efficiency impacts of remediation time. Interest rate experiments showed that the effect is dependent on the financial methodology and has little impact on the technical selection of the remediation design. © 2004 Elsevier B.V.
Developments in Water Science
Using remediation time as an optimization variable in groundwater remediation systems.
Developments in Water Science,
55(PART 2), 1171-1180.
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/7420