Potential Well Locations in In Situ Bioremediation Design Using Neural Network Embedded Monte Carlo Approach

Abstract

An optimal groundwater remediation design problem generally requires determination of the location of extraction and injection wells and their pumping and injection rates once the well locations are selected. According to many researchers, the determination of an optimal well location is even more important than the optimal pumping rate in groundwater remediation problems. The objective of the study is to apply a neural network embedded Monte Carlo approach to determine potential well locations for in situ bioremediation of contaminated groundwater. The methodology developed in this study has three important components: a bioremediation simulation model, an artificial neural network, and an application of Monte Carlo simulations. This method has been further applied to an in situ bioremediation problem, for which data are adopted from the available literature. The results show that the proposed approach can successfully identify potential well locations from a set of preselected well locations, which can be further used in optimization algorithms to identify optimal well locations and the corresponding pumping rates. The advantage of this approach is that it reduces the size of the problem considerably by eliminating redundant well locations and hence the computational burden involved. Furthermore, the computational burden of Monte Carlo simulations is managed within a practical time frame by replacing the bioremediation simulation model with a trained neural network.