Remediation of heterogeneous aquifers based on multiobjective optimization and adaptive determination of critical realizations

[1] A method for optimal remediation of heterogeneous aquifers based on stochastic simulation with adaptive determination of critical realizations is developed. Multiple hydraulic conductivity realizations are generated using the turning bands method while Monte Carlo simulation is incorporated into a multiobjective genetic optimization algorithm. In order to reduce the computational burden of Monte Carlo simulations, an adaptive procedure is developed for identifying “critical realizations” which are the ones that have an effect on the optimal solution depending on desired reliability level. The adaptive procedure is embedded into the genetic optimization algorithm where noncritical realizations are eliminated step by step during the advancement of optimization. When the number of remaining realizations reaches a minimum, some new realizations are added. The specific realizations which are removed or added in the critical set are selected by an automatic procedure controlled by a similarity criterion based on rankings of realizations. This is a significant improvement over the nonadaptive methodology for identification of critical realizations and does not require using a number of initial designs and a safety threshold. The methodology is applied in a remediation problem with two objectives (reduction of contaminant mass and cleanup cost). The applications indicate significant savings in computer time without loss of performance.

[1]  Weiqi Wang,et al.  Optimal groundwater remediation with well location as a decision variable: Model development , 1994 .

[2]  J. Guan,et al.  Optimal remediation with well locations and pumping rates selected as continuous decision variables , 1999 .

[3]  J. Eheart,et al.  Neural network-based screening for groundwater reclamation under uncertainty , 1993 .

[4]  David E. Goldberg,et al.  Combining Reliability and Pareto Optimality—An Approach Using Stochastic Multi-Objective Genetic Algorithms , 2003 .

[5]  Domenico Baù,et al.  Stochastic management of pump-and-treat strategies using surrogate functions , 2006 .

[6]  J. Guan,et al.  Optimal design of groundwater remediation systems using fuzzy set theory , 2004 .

[7]  A. Mantoglou,et al.  The Turning Bands Method for simulation of random fields using line generation by a spectral method , 1982 .

[8]  S. Gorelick,et al.  Optimal groundwater quality management under parameter uncertainty , 1987 .

[9]  Peter Bayer,et al.  Computationally efficient stochastic optimization using multiple realizations , 2008 .

[10]  David E. Dougherty,et al.  Optimal groundwater management: 2. Application of simulated annealing to a field-scale contamination site , 1993 .

[11]  G. Pinder,et al.  Enhancement of the outer approximation method for the solution of concentration‐constrained optimal‐design groundwater‐remediation problems , 2003 .

[12]  Barbara S. Minsker,et al.  Optimal groundwater remediation design using an Adaptive Neural Network Genetic Algorithm , 2006 .

[13]  George Kourakos,et al.  Optimal Groundwater Remediation Under Uncertainty Using Multi-objective Optimization , 2006 .

[14]  Mustafa M. Aral,et al.  Remediation System Design with Multiple Uncertain Parameters Using Fuzzy Sets and Genetic Algorithm , 2005 .

[15]  David E. Goldberg,et al.  Adaptive Hybrid Genetic Algorithm for Groundwater Remediation Design , 2005 .

[16]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[17]  N. Chan.  Robustness of the multiple realization method for stochastic hydraulic aquifer management , 1993 .

[18]  Patrick M. Reed,et al.  Comparing state-of-the-art evolutionary multi-objective algorithms for long-term groundwater monitoring design , 2005 .

[19]  C. Zheng,et al.  An integrated global and local optimization approach for remediation system design , 1999 .

[20]  Daene C. McKinney,et al.  Pump-and-treat ground-water remediation system optimization , 1996 .

[21]  Abhishek Singh,et al.  Uncertainty‐based multiobjective optimization of groundwater remediation design , 2008 .

[22]  Dimitri P. Solomatine,et al.  Groundwater Remediation Strategy Using Global Optimization Algorithms , 2002 .

[23]  Albert J. Valocchi,et al.  A game-theoretic parameter configuration technique for aquifer restoration design , 1990 .

[24]  Thomas Harter.  Unconditional and conditional simulation of flow and transport in heterogeneous, variably saturated porous media , 1994 .

[25]  Jianfeng Wu,et al.  A comparative study of Monte Carlo simple genetic algorithm and noisy genetic algorithm for cost-effective sampling network design under uncertainty , 2006 .

[26]  Jeffrey Horn,et al.  Multi-objective optimal design of groundwater remediation systems: application of the niched Pareto genetic algorithm (NPGA) , 2002 .

[27]  B. Minsker,et al.  Uncertainty Based Multi-Objective Optimization of Groundwater Remediation at the Umatilla Chemical Depot , 2004 .

[28]  David E. Goldberg,et al.  A niched Pareto genetic algorithm for multiobjective optimization , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[29]  Richard C. Peralta,et al.  Optimal design of aquifer cleanup systems under uncertainty using a neural network and a genetic algorithm , 1999 .

[30]  S. Gorelick,et al.  Reliable aquifer remediation in the presence of spatially variable hydraulic conductivity: From data to design , 1989 .

[31]  Evan J. Hughes,et al.  Evolutionary Multi-objective Ranking with Uncertainty and Noise , 2001, EMO.

[32]  P. Bayer,et al.  Evolutionary algorithms for the optimization of advective control of contaminated aquifer zones , 2004 .

[33]  Amy B. Chan Hilton,et al.  Groundwater Remediation Design under Uncertainty Using Genetic Algorithms , 2005 .

[34]  G. Pinder,et al.  Efficient Groundwater Remediation System Design Subject to Uncertainty Using Robust Optimization , 2007 .

[35]  Paul P. Wang,et al.  MT3DMS: A Modular Three-Dimensional Multispecies Transport Model for Simulation of Advection, Dispersion, and Chemical Reactions of Contaminants in Groundwater Systems; Documentation and User's Guide , 1999 .

[36]  P. Kitanidis,et al.  Optimization of the pumping schedule in aquifer remediation under uncertainty , 1990 .

[37]  Arlen W. Harbaugh,et al.  A modular three-dimensional finite-difference ground-water flow model , 1984 .

[38]  Hamid R. Nemati,et al.  Groundwater quality management under uncertainty: stochastic programming approaches and the value of information , 1992 .

[39]  Tahir Husain,et al.  An overview and analysis of site remediation technologies. , 2004, Journal of environmental management.

[40]  Peter Bayer,et al.  Optimization of concentration control by evolution strategies: Formulation, application, and assessment of remedial solutions , 2007 .