Optimal design of in-situ bioremediation system using the meshless element-free Galerkin method and particle swarm optimization

Abstract

An optimal design of in-situ bioremediation system for contaminated aquifer sites mainly depends on the injection/extraction well locations and their pumping rates. Previous studies have used discretization approaches such as finite difference method (FDM) or finite element method (FEM) to solve the equations of groundwater flow and contaminant transport (GFCT) that characterize the bioremediation processes. In these numerical models, well locations can only lie at the grid nodes and therefore, the locations other than the grid nodes remain unexplored for the optimal in-situ bioremediation. To explore such locations, in this study a meshless simulation model called BIOEFGM is developed using the element-free Galerkin method (EFGM) and coupled with the particle swarm optimization (PSO). BIOEFGM model provides flexibility in adding injection/extraction wells anywhere in the computational domain during the entire optimization procedure. However, FDM/FEM requires meshing and re-meshing of the computational domain for each set of wells and it increases the computational efforts significantly for solving the optimization problem. In this paper, the proposed BIOEFGM-PSO simulation-optimization (S/O) model is first applied to a well-known bioremediation problem and then to a field type large aquifer problem. The simulation results of BIOEFGM are verified with those from the RT3D simulations. The estimated bioremediation cost from the BIOEFGM-PSO model for the first problem is found to be lesser in comparison to the same calculated with different S/O models in the previous studies. The results of this problem also show that optimized in-situ bioremediation system designed by proposed S/O model takes less remediation time and also favor effective biodegradation of contaminants. For both the problems, BIOEFGM-PSO identified optimal well locations at positions other than that of discretized nodes and it leads to efficient bioremediation. It indicates the effectiveness of the BIOEFGM-PSO model and therefore, can be applied for designing the better optimized in-situ bioremediation systems for field problems.