Optimal Design of the Groundwater Treatment System Using Hybrid Bioremediation and Pump and Treat Technologies

Optimal groundwater bioremediation formulation are often complex, nonlinear, and involve intensive computations. Population-based optimization techniques allow solution of more complex nonlinear problems compared to the traditional gradient-based approaches, but computationally they are more intensive. For the first time, this study, proposed ant colony optimization (ACO) algorithm for groundwater bioremediation problems. Performance is compared to the hybrid genetic algorithm and simulated annealing. Rates of injection and extraction pumping and well locations are decision variables. The BIOPLUMEII is used as simulation model. Results showed the phenomenal capability of ACO algorithm in cutting calculation cost, in comparison to Hybrid simulated annealing and genetic algorithm, with a minor calculation error of 1.8%. Optimization results showed that ACO note worthily reduces computational time (56%) compared to the hybrid genetic algorithm and simulated annealing. ACO can be used as an alternative method for solving larger-scale groundwater remediation problems.