HOUGHTON, MI -- Researchers are looking at a new method that would give decision-makers a multi-objective tool to help them solve groundwater remediation problems.
"Selecting the optimal design for a soil or groundwater remediation strategy is currently an enormous challenge for decision-makers due to the number of potential alternatives, the complexity of contaminated subsurface environments, and the need to weigh conflicting objectives such as risk and cost," says Project Leader Dr. Alex Mayer of Michigan Tech's Department of Geological Engineering & Sciences.
Mayer says simulation/optimization models have been applied to remediation design, but current approaches don't allow for multi-objective optimization.
"The aim of this project," he says, "is to develop, apply, and test new procedures to solve multi-objective groundwater remediation problems, with the goal of creating a new set of tools for decision-makers."
Mayer says that when cleanup systems were designed in the past, they were focused on the least expensive solution to reduce a toxic compound to the lowest feasible level.
"If we assume there is a fixed amount of money available to clean up contaminated sites, we should be prioritizing cleanup of sites where the return, in terms of risk reduction, is the greatest for the minimum expected cost."
Mayer says the efforts of researchers will now focus on developing procedures for producing tradeoff curves, or surfaces, consisting of solutions that are optimal with respect to at least one objective. Decision-makers will be able to examine the tradeoff curves and select a solution or solutions based on their judgments as to what tradeoffs are acceptable. These alternatives will utilize a new technique called the Niched Pareto procedure, pioneered by Mayer's co-investigator, Dr. Jeffrey Horn of Northern Michigan University's Department of Math and Computer Sciences.
"These new algorithms will allow decision-makers to determine the importance of competing objectives in a given situation," explains Mayer. "An iterative process will be used to guide the decision-maker towards a preferred weighting or ranking of the multiple objectives. We will apply the algorithms to a series of test problems based on real sites to evaluate and compare the performance or each algorithm."
Dr. Carl Enfield of the Environmental Protection Agency's Risk Management Lab in Cincinnati will provide field expertise in evaluating all remediation methods used.
Mayer expects the project to result in remediation designs that are significantly less expensive than those provided by traditional design approaches.
"In previous approaches where optimization has been used for remediation system design, cleanup goals were specified as static constraints," he says. "This project will involve the direct incorporation of risk assessment into the remediation design process. The decision-maker will be able to view the full range of potential remediation designs in terms of the risk they would impose, while weighing the risk against estimated cost and cleanup time."
The project is being funded for three years by a $253,000 grant from the Environmental Protection Agency.