Practical Remedial Design Optimization for Large Complex Plumes
Publication: Journal of Water Resources Planning and Management
Volume 134, Issue 5
Abstract
Powerful simulation/optimization (S/O) models exist for designing groundwater well systems and pumping strategies. However, it can be challenging to use S/O modeling effectively for large, complex, and computationally intensive problems within project time and cost constraints. Here, we present a generic two-stage optimization procedure for making S/O modeling more practical. Application is illustrated for developing optimal transient 30-year pump-and-treat designs for Blaine Naval Ammunition Depot (NAD), Nebraska, and using an innovative hybrid advanced genetic algorithm with tabu search features (AGT). AGT includes standard genetic algorithm and tabu search features plus healing, elitism, threshold acceptance, and a new subset/subspace decomposition optimization. The screening stage simplifies the optimization problem, and selects desirable remediation wells from among many candidates. During this stage, computational effort is lessened by reducing the number of state variables needing evaluation, and the solution space dimensionality (including temporal dimensions). Subset/subspace decomposition optimization of steady flow rates is used to identify desirable sets of candidate wells. The transient optimization stage develops mathematically optimal time-varying pumping rates for well subsets identified by the screening stage. It also includes reoptimization using the original objective function plus goal programming to increase strategy robustness. Initializing the AGT with feasible solutions reduces computational effort. Within a short period the procedure developed optimal pump and treat system designs for NAD. The procedure yields better objective function values than trial and error. Because optimization causes tight constraints, the computed strategy is sensitive to changes in model parameters. Increasing strategy robustness via AGT and goal programming degrades the value of the initial objective function.
Get full access to this article
View all available purchase options and get full access to this article.
References
Aly, A. H., and Peralta, R. C. (1999a). “Comparison of a genetic algorithm and mathematical programming to the design of groundwater cleanup systems.” Water Resour. Res., 35(8), 2415–2425.
Aly, A. H., and Peralta, R. C. (1999b). “Optimal design of aquifer cleanup systems under uncertainty using a neural network and a genetic algorithm.” Water Resour. Res., 35(8), 2523–2532.
Becker, D., Minsker, B., Greenwald, R., Zhang, Y., Harre, K., Yager, K., Zheng, C., and Peralta, R. C. (2006). “Reducing long-term remedial costs by transport modeling optimization.” Ground Water, 44(6), 864–875.
Cieniawski, S. E., Etheart, J. W., and Ranjithan, S. (1995). “Using genetic algorithms to solve a multiobjective groundwater monitoring problem.” Water Resour. Res., 31(2), 399–409.
Environmental Security Technology Certification Program (ESTCP). (2004a). “Application of flow and transport optimization codes to groundwater pump-and-treat systems.” Factsheet ER-0010, Dept. of Defense, Washington, D.C.
Environmental Security Technology Certification Program (ESTCP). (2004b). “Cost and performance report, application of flow and transport optimization codes to groundwater P&T systems.” Rep. No. CU-0010, Dept. of Defense, Washington, D.C.
Espinoza, F. P., Minsker, B. S., and Goldberg, D. E. (2005). “Adaptive hybrid genetic algorithm for groundwater remediation design.” J. Water Resour. Plann. Manage., 131(1), 14–24.
Geotrans (2002). “Transport optimization.” Hastings Naval Ammunition Depot, Hastings formulations documentation.
Glover, F., and Laguna, M. (1997). Tabu search, Kluwer Academic, Norwell, Mass.
Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning, Addison-Wesley, Reading, Mass.
Hsiao, C., and Chang, L. (2002). “Dynamic optimal groundwater management with inclusion of fixed costs.” J. Water Resour. Plann. Manage., 128(1), 57–65.
Johnson, V. M., and Rogers, L. L. (1995). “Location analysis in groundwater remediation using neural networks.” Ground Water, 33(5), 749–758.
Liu, Y., and Minsker, B. S. (2004). “Full multiscale approach for optimal control of in-situ bioremediation.” J. Water Resour. Plann. Manage., 130(1), 26–32.
Marryott, R. A., Dougherty, D. E., and Stollar, R. L. (1993). “Optimal groundwater management. 2: Application of simulated annealing to a field-scale contamination site.” Water Resour. Res., 29(4), 847–860.
McDonald, M. D., and Harbaugh, A. W. (1988). “A modular three-dimensional finite-difference groundwater flow model.” U.S. Geological Survey techniques of water-resources investigations, Book 6, Chap. A1, USGS, Denver.
McKinney, D. C., and Lin, M.-D. (1994). “Genetic algorithm solution of groundwater management models.” Water Resour. Res., 30(6), 1897–1906.
McKinney, D. C., and Lin, M.-D. (1995). “Approximate mixed-integer nonlinear programming methods for optimal aquifer remediation design.” Water Resour. Res., 31(3), 731–740.
Minsker, B. S., and Shoemaker, C. A. (1998). “Dynamic optimal control of in situ bioremediation of ground water.” J. Water Resour. Plann. Manage., 124(3), 149–161.
Peralta, R. C., Kalwij, I., and Wu, S. (2003). “Practical simulation/optimization modeling for groundwater quality and quantity management.” Proc., MODFLOW and More: Understanding through Modeling, International Groundwater Modeling Center, Golden, Colo., 784–788.
Reed, P., and Minsker, B. S. (2004). “Striking the balance: Long term groundwater monitoring design for conflicting objectives.” J. Water Resour. Plann. Manage., 130(2), 140–149.
Shieh, H. J., and Peralta, R. C. (2005). “Optimal in-situ bioremediation design by hybrid genetic algorithm-simulated annealing.” J. Water Resour. Plann. Manage., 131(1), 67–78.
Smalley, J. B., Minsker, B. S., and Goldberg, D. E. (2000). “Risk-based in situ bioremediation design using a noisy genetic algorithm.” Water Resour. Res., 36(10), 3043–3052.
van Laarhoven, P. J. M., and Aarts, E. H. L. (1987). Simulated annealing: Theory and applications, D. Reidel, Dordrecht, The Netherlands.
Wagner, B. J. (1999). “Evaluating data worth for groundwater management under uncertainty.” J. Water Resour. Plann. Manage., 125(5), 281–288.
Yoon, J.-H., and Shoemaker, C. A. (1999). “Comparison of optimization methods for groundwater bioremediation.” J. Water Resour. Plann. Manage., 125(1), 54–63.
Yoon, J.-H., and Shoemaker, C. A. (2001). “An improved real-coded GA for groundwater bioremediation.” J. Comput. Civ. Eng., 15(3), 224–231.
Zheng, C., and Wang, P. P. (1998). “MT3DMS: A modular three-dimensional multispecies transport model for simulation of advection, dispersion and chemical reactions of contaminants in groundwater systems.” Technical Rep. Prepared for Waterways Experiment Station. US Army Corps of Engineers, Dept. of Geology and Mathematics, Univ. of Alabama, Tuscaloosa, Ala.
Zheng, C., and Wang, P. P. (1999). “An integrated global and local optimization approach for remediation system design.” Water Resour. Res., 35(1), 137–148.
Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 134 • Issue 5 • September 2008
Pages: 422 - 431
Copyright
© 2008 ASCE.
History
Received: May 26, 2006
Accepted: Aug 20, 2007
Published online: Sep 1, 2008
Published in print: Sep 2008
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.