Evaluation of a distributed numerical simulation optimization approach applied to aquifer remediation

AbstractIn this paper we evaluate a distributed approach which uses numerical simulation and optimization techniques to automatically find remediation solutions to a hypothetical contaminated aquifer. The repeated execution of the numerical simulation model of the aquifer through the optimization cycles tends to be computationally expensive. To overcome this drawback, the numerical simulations are executed in parallel using a network of heterogeneous workstations. Performance metrics for heterogeneous environments are not trivial; a new way of calculating speedup and efficiency for Bag-of-Tasks (BoT) applications is proposed. The performance of the parallel approach is evaluated

Development and evaluation of models for assessing geochemical pollution sources with multiple reactive chemical species for sustainable use of aquifer systems: source characterization and monitoring network design

Michael designed a groundwater flow and reactive transport optimization model. He applied this model to characterize contaminant sources in Australia's first large scale uranium mine site in the Northern Territory. He identified the contamination sources to the groundwater system in the area. His findings will assist planning actions and steps needed to implement the mitigation strategy of this contaminated aquifer

Linking a simulated annealing based optimization model with PHT3D simulation model for chemically reactuve transport processes to optimally characterize unknown contaminant sources in a former mine site in Australia

Historical mining activities often lead to continuing wide spread contaminants in both groundwater and surface water in previously operational mine site areas. The contamination may continue for many years after closing down the mining activities. The essential first step for sustainable management of groundwater and development of remediation strategies is the unknown contaminant source characterization. In a mining site, there are multiple species of contaminants involving complex geochemical processes. It is difficult to identify the potential sources and pathways incorporating the chemically reactive multiple species of contaminants making the source characterization process more challenging. To address this issue, a reactive transport simulation model PHT3D is linked to a Simulated Annealing based the optimum decision model. The numerical simulation model PHT3D is utilized for numerically simulating the reactive transport process involving multiple species in the former mine site area. The simulation results from the calibrated PHT3D model are illustrated, with and without incorporating the chemical reactions. These comparisons show the utility of using a reactive, geochemical transport process’ simulation model. Performance evaluation of the linked simulation optimization methodology is evaluated for a contamination scenario in a former mine site in Queensland, Australia. These performance evaluation results illustrate the applicability of linked simulation optimization model to identify the source characteristics while using PHT3D as a numerical reactive chemical species’ transport simulation model for the hydro-geochemically complex aquifer study area

Application of Simulated Annealing and Adaptive Simulated Annealing in Search for Efficient Optimal Solutions of a Groundwater Contamination related Problem

Characterization of groundwater contamination sources is a complex inverse problem. This inverse problem becomes complicated, due to the nonlinear nature of the groundwater flow and transport processes and the associated natural uncertainties. The mathematical challenges arise due to the nonunique characteristics of this problem resulting from the nonunique response of the aquifer system to a set of stresses and the possibility of instead locating only local optimal solutions. The linked simulation‐optimization model is an efficient approach to identifying groundwater contamination source characteristics. Efficiency and accuracy of the search for optimum solutions of a linked simulation‐optimization depend on the utilized optimization algorithm. This limited study focuses on the application and efficiency of simulated annealing (SA) as the optimization algorithm for solving the source characterization problem. The advantages in using adaptive simulated algorithm (ASA) as an alternative are then evaluated. The possibility of identifying a local optimal solution rather than a global optimal solution when using SA implies failure to solve the source characterization inverse problem. The cost of such inaccurate characterization may be enormous when a remediation strategy is based on the model inferences. ASA is shown to provide a reliable and acceptable alternative for solving this challenging aquifer contamination problem

Application of simulated annealing in search for efficient optimal solutions of a groundwater contamination related problem

Characterization of groundwater contamination sources is a complex inverse problem. This inverse problem becomes complicated, due to the nonlinear nature of the groundwater flow and transport processes and the associated natural uncertainties. The mathematical challenges arise due to the nonunique characteristics of this problem resulting from the nonunique response of the aquifer system to a set of stresses and the possibility of instead locating only local optimal solutions. The linked simulation‐optimization model is an efficient approach to identifying groundwater contamination source characteristics. Efficiency and accuracy of the search for optimum solutions of a linked simulation‐optimization depend on the utilized optimization algorithm. This limited study focuses on the application and efficiency of simulated annealing (SA) as the optimization algorithm for solving the source characterization problem. The advantages in using adaptive simulated algorithm (ASA) as an alternative are then evaluated. The possibility of identifying a local optimal solution rather than a global optimal solution when using SA implies failure to solve the source characterization inverse problem. The cost of such inaccurate characterization may be enormous when a remediation strategy is based on the model inferences. ASA is shown to provide a reliable and acceptable alternative for solving this challenging aquifer contamination problem

Optimization of Palladium-Catalyzed in Situ Destruction of Trichloroethylene-Contaminated Groundwater Using a Genetic Algorithm

Conventional technologies for the treatment of groundwater contaminated with chlorinated solvents have limitations that have motivated development of innovative technologies. One such technology currently under development involves using palladium-on-alumina (Pd/Al) as a catalyst to promote dechlorination. Pd/Al catalyst may be used in-well as part of a re-circulating horizontal flow treatment well (HFTW) system. An HFTW system involves two or more dual-screened wells, with in-well reactors, to capture and treat contaminated groundwater without the need to pump the water to the surface. In this study, objective and fitness functions, based on system costs and TCE concentration requirements, were developed to optimize a dual-well HFTW system with in-well Pd/Al reactors in a two-aquifer remediation scenario. A genetic algorithm (GA) was coupled with a three dimensional numerical model of contaminant fate and transport to determine optimized HFTW control parameters (well location, pumping rate, and reactor size). The GA obtained a solution within the specified constraints, but the solution was an artificial solution, as contaminated groundwater in one of the two aquifers received no treatment. Based on these results, new objective and fitness functions were developed in an effort to determine the most cost effective solution to remove contaminant mass from the aquifer. The solution arrived at using this approach, while resulting in minimized values of cost per contaminant mass destroyed, produced unacceptably high downgradient contaminant concentration levels. We conclude that by specifying that only two wells could be used in the HFTW system, we overconstrained the problem and that a multi-well HFTW solution is required

Coupled simulation-optimization model for coastal aquifer management using genetic programming-based ensemble surrogate models and multiple-realization optimization

Approximation surrogates are used to substitute the numerical simulation model within optimization algorithms in order to reduce the computational burden on the coupled simulation-optimization methodology. Practical utility of the surrogate-based simulation-optimization have been limited mainly due to the uncertainty in surrogate model simulations. We develop a surrogate-based coupled simulation-optimization methodology for deriving optimal extraction strategies for coastal aquifer management considering the predictive uncertainty of the surrogate model. Optimization models considering two conflicting objectives are solved using a multiobjective genetic algorithm. Objectives of maximizing the pumping from production wells and minimizing the barrier well pumping for hydraulic control of saltwater intrusion are considered. Density-dependent flow and transport simulation model FEMWATER is used to generate input-output patterns of groundwater extraction rates and resulting salinity levels. The nonparametric bootstrap method is used to generate different realizations of this data set. These realizations are used to train different surrogate models using genetic programming for predicting the salinity intrusion in coastal aquifers. The predictive uncertainty of these surrogate models is quantified and ensemble of surrogate models is used in the multiple-realization optimization model to derive the optimal extraction strategies. The multiple realizations refer to the salinity predictions using different surrogate models in the ensemble. Optimal solutions are obtained for different reliability levels of the surrogate models. The solutions are compared against the solutions obtained using a chance-constrained optimization formulation and single-surrogate-based model. The ensemble-based approach is found to provide reliable solutions for coastal aquifer management while retaining the advantage of surrogate models in reducing computational burden

Integrated embedding optimization applied to Salt Lake Valley aquifers

The embedding optimization modeling approach is adapted to aid sustainable groundwater quantity and quality management of complex nonlinear multilayer aquifers. Implicit block-centered finite difference approximations of the quasi three-dimensional unsteady flow equation and Galerkin finite element approximations of the two-dimensional advection-dispersion transport equation are embedded directly as constraints in the model. Also used are nonlinear constraints describing river-aquifer interflow, evapotranspiration, and vertical flow reduction due to unconfinement. These circumvent use of large numbers of integer variables. The use of both linear and nonlinear formulations in a cyclical manner reduces execution time and improves confidence in solution optimality. The methodology is demonstrated for Salt Lake valley where groundwater quantity and quality management are needed, the proportion of pumping cells and cells needing head constraint is large, and many flows are described by discrete nonlinear or piece wise linear functions

Groundwater Management Optimization and Saltwater Intrusion Mitigation under Uncertainty

Groundwater is valuable to supply fresh water to the public, industries, agriculture, etc. However, excessive pumping has caused groundwater storage degradation, water quality deterioration and saltwater intrusion problems. Reliable groundwater flow and solute transport modeling is needed for sustainable groundwater management and aquifer remediation design. However, challenges exist because of highly complex subsurface environments, computationally intensive groundwater models as well as inevitable uncertainties. The first research goal is to explore conjunctive use of feasible hydraulic control approaches for groundwater management and aquifer remediation. Water budget analysis is conducted to understand how groundwater withdrawals affect water levels. A mixed integer multi-objective optimization model is constructed to derive optimal freshwater pumping strategies and investigate how to promote the optimality through regulating pumping locations. A solute transport model for the Baton Rouge multi-aquifer system is developed to assess saltwater encroachment under current condition. Potential saltwater scavenging approach is proposed to mitigate the salinization issue in the Baton Rouge area. The second research goal aims to develop robust surrogate-assisted simulation-optimization modeling methods for saltwater intrusion mitigation. Machine learning based surrogate models (response surface regression model, artificial neural network and support vector machine) were developed to replace a complex high-fidelity solute transport model for predicting saltwater intrusion. Two different methods including Bayesian model averaging and Bayesian set pair analysis are used to construct ensemble surrogates and quantify model prediction uncertainties. Besides. different optimization models that incorporate multiple ensemble surrogates are formulated to obtain optimal saltwater scavenging strategies. Chance-constrained programming is used to account for model selection uncertainty in probabilistic nonlinear concentration constraints. The results show that conjunctive use of hydraulic control approaches would be effective to mitigate saltwater intrusion but needs decades. Machine learning based ensemble surrogates can build accurate models with high computing efficiency, and hence save great efforts in groundwater remediation design. Including model selection uncertainty through multimodel inference and model averaging provides more reliable remediation strategies compared with the single-surrogate assisted approach