Practical Remedial Design Optimization for Large Complex Plumes

Abstract

Powerful simulation/optimization (S/O) models exist for designing groundwater well systems and pumping strategies. However, it can be challenging to use S/O modeling effectively for large, complex, and computationally intensive problems within project time and cost constraints. Here, we present a generic two-stage optimization procedure for making S/O modeling more practical. Application is illustrated for developing optimal transient 30-year pump-and-treat designs for Blaine Naval Ammunition Depot (NAD), Nebraska, and using an innovative hybrid advanced genetic algorithm with tabu search features (AGT). AGT includes standard genetic algorithm and tabu search features plus healing, elitism, threshold acceptance, and a new subset/subspace decomposition optimization. The screening stage simplifies the optimization problem, and selects desirable remediation wells from among many candidates. During this stage, computational effort is lessened by reducing the number of state variables needing evaluation, and the solution space dimensionality (including temporal dimensions). Subset/subspace decomposition optimization of steady flow rates is used to identify desirable sets of candidate wells. The transient optimization stage develops mathematically optimal time-varying pumping rates for well subsets identified by the screening stage. It also includes reoptimization using the original objective function plus goal programming to increase strategy robustness. Initializing the AGT with feasible solutions reduces computational effort. Within a short period the procedure developed optimal pump and treat system designs for NAD. The procedure yields better objective function values than trial and error. Because optimization causes tight constraints, the computed strategy is sensitive to changes in model parameters. Increasing strategy robustness via AGT and goal programming degrades the value of the initial objective function.