Reducing Long‐Term Remedial Costs by Transport Modeling Optimization

Abstract

The Department of Defense (DoD) Environmental Security Technology Certification Program and the Environmental Protection Agency sponsored a project to evaluate the benefits and utility of contaminant transport simulation-optimization algorithms against traditional (trial and error) modeling approaches. Three pump-and-treat facilities operated by the DoD were selected for inclusion in the project. Three optimization formulations were developed for each facility and solved independently by three modeling teams (two using simulation-optimization algorithms and one applying trial-and-error methods). The results clearly indicate that simulation-optimization methods are able to search a wider range of well locations and flow rates and identify better solutions than current trial-and-error approaches. The solutions found were 5% to 50% better than those obtained using trial-and-error (measured using optimal objective function values), with an average improvement of~20%. This translated into potential savings ranging from $600,000 to $10,000,000 for the three sites. In nearly all cases, the cost savings easily outweighed the costs of the optimization. To reduce computational requirements, in some cases the simulation-optimization groups applied multiple mathematical algorithms, solved a series of modified subproblems, and/or fit ''meta-models'' such as neural networks or regression models to replace time-consuming simulation models in the optimization algorithm. The optimal solutions did not account for the uncertainties inherent in the modeling process. This project illustrates that transport simulation-optimization techniques are practical for real problems. However, applying the techniques in an efficient manner requires expertise and should involve iterative modification to the formulations based on interim results.

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

“…Becker et al, 2006;Wang et al, 2009;Yang and Cai, 2010;Yin et al, 2012), they are often limited by the ability to model these systems properly. In addition, the decision variables, objective functions and constraints are generally assumed to be known (e.g.…”

“…Even when optimisation is undertaken, the process could be considered to be fluid and iterative. For example, Becker et al (2006), in an application of EAs to three real-world groundwater remediation sites, found that decision makers often asked for modifications to the formulations (e.g. new constraints) after viewing initial results that gave new insights into the remediation systems.…”

“…Although it does not directly change the optimisation algorithm, the results of preconditioning often improve the computational efficiency of the methodology. Examples from the field of the design of water distribution networks (Kang and Lansey, 2011;Keedwell and Khu, 2005), sewer networks (Guo et al, 2007), and groundwater remediation (Becker et al, 2006) use a set of domain specific rules to improve a solution before using it to seed a GA run. Alternative approaches are based on global sensitivity analysis , the use of engineering judgement and historical information (Pasha and Lansey, 2010), or graph decomposition methods.…”

Integration of Optimal Dynamic Control and Neural Network for Groundwater Quality Management

“…Previous studies demonstrated an ANN methodology as a groundwater simulator in the optimization model. An ANN embedded within an optimization model can accurately predict ground water state in response to management alternatives (Rogers and Dowla 1994;Rao et al 2005;Becker et al 2006;Coppola et al 2007;Chu and Chang 2009a). Rogers and Dowla (1994) used an ANN-Genetic Algorithm (GA) methodology to replace a traditional simulation-optimization model.…”

“…The ANN-GA methodology optimizes the set of pumping patterns for meeting remediation objectives (Rogers et al 1995). Becker et al (2006) coupled optimization and transport simulation models to three pump-andtreat systems for real problems. They used ANN-derived state transition equations to replace time-consuming simulation models in the optimization algorithm.…”