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FRTR > RPO > Simulation Optimization > Transport Optimization > Methods & Codes

Transport Optimization: Methods & Codes

This page lists both computational methods and codes that are used for to optimize systems with simulation of transport of contaminants. Since there are new methods and codes are emerging in this field in addition to revisions to existing programs, references and supporting research have also been included.

ModGA

 Developed by Chunmiao Zheng of the University of Alabama, ModGA, a simulation-optimization model, can be used for optimal design of groundwater hydraulic control and remediation systems under general field conditions. The model couples genetic algorithms (GA), a global search technique inspired by biological evolution, with MODFLOW and MT3D.

MGO (Modular Groundwater Optimizer)

 Developed by Chunmiao Zheng of the University of Alabama, the MGO code couples the MODFLOW and MT3D simulators with three global optimization methods, i.e., genetic algorithm, simulated annealing, and tabu search, which are linked by a common input/output structure and integrated with a gradient-based optimization module to reduce the computational burden.

References (for both ModGA and MGO):

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ASAP – Adaptive Simulated Annealing Package

 Douglas Belscher, Belscher@waterstoneinc.com; WaterStone Environmental Hydrology and Engineering, Inc. ASAP provides rigorously optimized water resource and environmental management designs. By combining well-accepted simulation packages (e.g., MODFLOW and MT3D) with artificial neural network (ANN) and adaptive simulated annealing (ASA) techniques, ASAP provides formally optimized engineering designs which explicitly incorporate management goals and constraints.

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ATOPT – Advective Transport Optimization

Ann E. Mulligan, amulligan@whoi.edu, Woods Hole Oceanographic Institution, and David P. Ahlfeld, ahlfeld@ecs.umass.edu, University of Massachusetts

ATOPT is an advective control model that uses both contaminant pathline and capture zone simulation to constrain plume capture designs. The advective transport model explicitly represents advective transport while neglecting dispersion and contaminant decay reactions. ATOPT couples MODFLOW and MODPATH, and allows constraints to be placed on advective transport, time to capture, hydraulic head, and pumping rates.
 

  • Mulligan, A. E., and D. P. Ahlfeld, 2001, Optimal plume capture design in unconfined aquifers, J. Smith and S. Burns, eds., Physicochemical groundwater remediation, Kluwer Academic, p. 23-44.

  • A New Code for MODFLOW-Coupled Groundwater Management of Unconfined Aquifers
    Ahlfeld, D.P., R.G. Riefler, and A.E. Mulligan, 1998, Proceedings of MODFLOW'98 Conference, October 4-8, 1998, Golden, Colorado, pp. 431-438

  • Optimal Management of Flow in Groundwater Systems
    Ahlfeld, D.P. and A.E. Mulligan, 2000, Academic Press, San Diego

  • Advective Control of Groundwater Contaminant Plumes: Model Development and Comparison to Hydraulic Control
    Mulligan, A.E. and D.P. Ahlfeld, 1999, Water Resources Research, 35(8), pp. 2285-2294.
  • Mulligan, A.E., 2001, Water supply planning in contaminated aquifers, Proceedings of the World Water and Environmental Resources Congress, American Society of Civil Engineers, Orlando, FL, May 2001.
  • Mulligan, A. E., and D. P. Ahlfeld, 2002, A new interior point boundary projection method for solving nonlinear groundwater pollution control problems, Operations Research, 50(4): 636-644.
  • Mulligan, A.E. and D.P. Ahlfeld, 1998, Optimal plume control based on advective transport, Computational Methods in Water Resources, Volume I, Proceedings of the XII International Conference, Crete, Greece, June 15-19, 1998, p. 83-89.

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iTOUGH2

 From Earth Sciences Division of Lawrence Berkeley National Laboratory. iTOUGH2 (inverse TOUGH2) is a computer program that provides inverse modeling capabilities for the TOUGH2 code, a simulator for multiphase, multicomponent, non-isothermal flows in fractured-porous media, for applications in geothermal reservoir engineering, nuclear waste disposal, and unsaturated zone hydrology.

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Gradient Methods

The gradient optimization solution techniques include 1) nonlinear programming (NLP); 2) nonlinear programming (NLP); 3) mixed integer linear programming (MILP); 4) mixed integer nonlinear programming (MINLP); and 5) differential dynamic programming (DDP). These methods evaluate the derivatives (or gradients) of the objective function with respect to the variables to be optimized; this is the reason that these methods are often referred to as “gradient” methods.

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Simulated Annealing

Simulated annealing mimics process in which a solid is initially heated up to its melting temperature and then is cooled down slowly so that all the particles arrange themselves in the state of minimum energy where crystallization occurs. In optimization, the objective function to be minimized represents the energy in the thermodynamic process, while the optimal solution corresponds to the crystal configuration. The basic concept lies in allowing the search procedure to move occasionally “uphill”.

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Artificial Neural Network

An artificial neural network (ANN) is a biological inspired computational system that (1) comprises individual processing or computational elements with associated memory, (2) is interconnected in some information-passing topology, (3) operates largely in parallel, and (4) has some ability to adapt its functioning to its inputs and outputs. The belief that “intelligent” system might be developed from the collective behavior of many of these interconnected processing elements has led to the development of neural net models. Most neural network applications use the neural network to approximate the simulation model in the optimization model. Another optimization method is still needed to solve the optimization problem.

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Outer Approximation

The outer approximation is an algorithm for Mixed-Integer Nonlinear Programming (MINLP) that relies on accumulation of linearizations to bound the objective function and feasible region. This method solves a sequence of approximations to a mathematical program where the approximating problem contains the original feasible region. Examples are cutting plane algorithms and relaxation. This method has the potential to solve groundwater management problems related to hydraulic gradient control and/or mass transport optimization problems.

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Genetic Algorithm

Genetic algorithm mimics the biological evolution based on Darwinist theory (survival of the fittest), where the strongest (or any selected) offspring in a generation are more likely to survive and reproduce. The method starts with a number of possible solutions, referred to as the first generation of the population. Each of the possible solutions is referred to as an individual, then encoded as either binary or real-coded string (called chromosome). For each individual, the objective function is evaluated. During the course of the search, new generations of individuals are reproduced from the old generations through random selection, crossover, and mutation based on certain probabilistic rules. The selection is in favor of those interim solutions with lower objective function values (in a minimization problem). Gradually, the population will evolve toward the optimal solution.

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SOMOS

SOMOS has been developed by Dr. Richard Peralta, the Systems Simulation and Optimization Laboratory (SS/OL) of Utah State University, and HydroGeoSystems Group. SOMOS is a powerful, broadly applicable, and adaptable family of Simulation/Optimization (S/O) modules for hydraulic and transport optimization. SOMOS can be used to optimize cleanup and containment of any plume that can be modeled by MODFLOW and MT3DMS. SOMOS employs wide ranges of constraints and objective functions (maximize mass removal, minimize mass remaining, minimize maximum concentration remaining, minimize cost and many others). SOMOS includes genetic algorithm linked with tabu search, simulated annealing linked with tabu search, artificial neural network (ANN), and response function options. SOMOS has been used on many sites, all successfully. All pump and treat systems constructed based on SOMOS designs have operated successfully and per design. At: http://www.usurf.org/units/wdl you will be able to find directions to the most recent SOMOSWEB updates and additional references.

  • Aly, A.H. and R.C. Peralta. 1999. Optimal design of aquifer clean up systems under uncertainty using a neural network and a genetic algorithm. Water Resources Research, 15(8):2523-2532.

  • Peralta, R. C. 2001. Remediation sim./opt. demonstrations. In Proceedings of MODFLOW and Other Modeling Odysseys. 2001. Eds, Seo, Poeter, Zheng and Poeter, Pub. IGWMC. p. 651-657.

  • Peralta, R. C. 2001. Simulation/optimization applications and software for optimal ground-water and conjunctive water management In Proc. MODFLOW and Other Modeling Odysseys 2001. (ed. by Seo, et al.), Pub. IGWMC, Golden, CO. p. 691-694.

  • Peralta, R. C., Kalwij, I. M. and S. Wu. 2003. Practical simulation/optimization modeling for groundwater quality and quantity man. In MODFLOW & More 2003: Understanding through Modeling. (ed. by Poeter, et al.) IGWMC, Golden, CO. p 784-788.

  • Peralta, R. C. 2003. SOMOS Simulation/Optimization Modeling System. In Proceedings, MODFLOW and More 2003: Understanding through Modeling. 819-823.

  • Peralta, R. C., Kalwij, I. M. and B. Timani. 2004. Optimizing complex plume pump and treat systems for Blaine Naval Ammunition Depot, Nebraska. In Proceedings of EWRI 2004 World Congress. Am. Soc. Civil Eng. 7 p.

  • Systems Simulation/Optimization Laboratory and HydroGeoSystems Group. 2001. Simulation/Optimization MOdeling System (SOMOS)users manual. SS/OL, Dept. of Biological and Irrigation Engineering, Utah State University, Logan, Utah. 457 p.

  • For additional references go to: http://www.usurf.org/units/wdl

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