Optimal System Design of In-Situ Bioremediation Using Parallel Recombinative Simulated Annealing

Abstract

We present a simulation/optimization model combining optimization with BIOPLUME II simulation for optimizing in-situ bioremediation system design. In-situ bioremediation of contaminated groundwater has become widely accepted because of its cost-effective ability to achieve satisfactory cleanup. We use parallel recombinative simulated annealing to search for an optimal design and apply the BIOPLUME II model to simulate aquifer hydraulics and bioremediation. Parallel recombinative simulated annealing is a general-purpose optimization approach that has the good convergence of simulated annealing and the efficient parallelization of a genetic algorithm. This is the first time that parallel recombinative simulated annealing has been applied to groundwater management. The design goal of the in-situ bioremediation system is to minimize system installation and operation cost. System design decision variables are pumping well locations and pumping rates. The problem formulation is mixed-integer and nonlinear. The system design must satisfy constraints on pumping rates, hydraulic heads, contaminant concentration at the plume source and at downstream monitoring wells. For the posed problem, the parallel recombinative simulated annealing obtains an optimal solution that minimizes system cost, reduces contaminant concentration and prevents plume migration