Atwood, D., Gorelick, S. (1985) "Hydraulic Gradient Control for Groundwater Contaminant Removal" J. Hydrology 76:85-106
In this paper Atwood and Gorelick explore the applications of linear programming and dispersion modeling to the remediation of a contaminated aquifer. The study focused on a contaminated aquifer under the Rocky Mountain Arsenal in Colorado. The work was divided into two sections to simplify the modeling and to avoid non-linearities within the system. The first section of the work modeled the behavior of a contaminant plume under the influence of a neutral groundwater gradient and a single centrally located remedial pump. Once the optimal site for the remedial pump was identified the change in the plumes geometry over time was simulated using dispersion modeling and groundwater flow equations. The second portion of the work focused on how to use the existing well fields to create a neutral hydraulic gradient around the contaminant plume and effectively hold it in place around the remedial pump. By using linear programming the authors found the optimal combination of pumping and recharge patterns to stabilize and contain the contaminant plume. This modeling required extensive knowledge of the existing hydraulic gradients, transmissivity and saturated depth of the aquifer. The authors used two different approaches in their optimization models. The first divided the mediation project into two management periods and used the knowledge of the contaminant plume geometry in each period to optimize the pumping and recharge operations of each period based on the plumes geometry. The second approach used no advanced knowledge of the plume's expected behavior and optimized based the plume's position in each time point. In both cases the systems were optimized to minimize the amount of pumping or recharge needed to stabilize the plume.
Interestingly, in both cases the overall pumping/recharge was similar although the two methods recommended different patterns of pumping and recharge stabilize the plume. The authors point out that the results of each method would have to be evaluated in an economic context to determine which was ultimately optimal (taking into account the costs of recharge water and the pumping expenses). This reinforces the point made by Liebman that in complex systems the optimization models can serve as decision making tools but not as the final word in decision making process.