Optimal In Situ Bioremediation Design by Hybrid Genetic Algorithm-Simulated Annealing

Presented is a simulation/optimization (S/O) model combining optimization with BIOPLUME II for optimizing in situ bioremediation system design. The S/O model uses a new hybrid method combining genetic algorithms and simulated annealing to search for an optimal design and applies the BIOPLUME II model to simulate aquifer hydraulics and bioremediation. This new hybrid method is parallel recombinative simulated annealing, which is a general-purpose optimization approach that has the good convergence of simulated annealing and the efficient parallelization of a genetic algorithm. We propose a two-stage management approach. The first-stage design goal is to minimize total system cost (pumping/treatment, well installation, and facility capital costs). The second-stage design goal is to minimize the cost of a time-varying pumping strategy using the optimal system chosen by the first-stage optimization. Optimization results show that parallel recombinative simulated annealing performs better than simulated annealing and genetic algorithms for optimizing system design when including installation costs. New explicit well installation coding improves algorithm convergence. Threshold accepting reduces computation time 43% by eliminating unnecessary simulation runs. Applying the optimal time-varying pumping strategy in the second stage reduces pumping cost by 31%.