Optimal groundwater remediation network design using selective membranes

This work deals with the optimal synthesis of groundwater remediation networks for the valorization of anionic pollutants by means of emulsion pertraction technology using hollow fiber modules (HFMs). Superstructures that incorporate all possible design alternatives are proposed. The aim of this work is to obtain a minimum cost groundwater remediation network that allows treatment of groundwater to required levels and, also, a contaminant rich solution that can be used for further processing. The optimization of the superstructure is initially formulated as a nonconvex nonlinear programming (NLP) problem. This rigorous NLP model is simplified using some assumptions to get a simplified model which is globally optimized using a Lagrangean decomposition algorithm. This globally optimal solution is used as an initialization point for optimizing the rigorous NLP problem. Three cases involving different numbers of HFMs are studied to determine a cost optimized network with an optimal number of modules.