Title: A mixed-integer non-linear programming with surrogate model for optimal remediation design of NAPLs contaminated aquifer

Authors: Jiannan Luo; Wenxi Lu

Addresses: Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun, 130021, China; College of Environment and Resources, Jilin University Changchun, 130021, China ' Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun, 130021, China; College of Environment and Resources, Jilin University Changchun, 130021, China

Abstract: A mixed-integer non-linear programming (MINLP) with surrogate model was introduced to derive the optimal surfactant enhanced aquifer remediation (SEAR) process (remediation cost minimisation and removal rate maximisation) at a nitrobenzene-contaminated site. First, a 3D multi-phase flow simulation model was developed to simulate the SEAR process; using a radial basis function artificial neural network (RBFANN), the surrogate model was built which was an approximation of the simulation model; a MINLP was built to identify the optimal remediation strategies and genetic algorithm (GA) and penalty function were combined to solve the model; at last, the optimal remediation strategies were obtained. The approximation result of RBFANN was compared with that of back-propagation artificial neural network (BPANN), mean absolute error, mean relative error and coefficient of determination of the developed RBFANN model were 0.01, 2.27% and 0.85 respectively, which indicated much higher approximation accuracy than BPANN. The MINLP with surrogate model is a powerful tool for non-aqueous phase liquids (NAPLs) contaminated site remediation optimisation problem and it can greatly improve computational efficiency.

Keywords: groundwater remediation; mixed integer nonlinear programming; MINLP; non-aqueous phase liquids; NAPLs; surrogate models; remediation optimisation; remediation design; aquifer contamination; aquifer pollution; water pollution; nitrobenzene; multi-phase flow simulation; 3D modelling; radial basis function; RBF ANNs; artificial neural network.

DOI: 10.1504/IJEP.2014.064047

International Journal of Environment and Pollution, 2014 Vol.54 No.1, pp.1 - 16

Accepted: 11 Mar 2014
Published online: 30 Aug 2014 *

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