Simulation and Optimization of Large Scale Subsurface Environmental Impacts; Investigations, Remedial Design and Long Term Monitoring

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@Article{Deschaine:2003:JMMS,
  author =       "Larry M. Deschaine",
  title =        "Simulation and Optimization of Large Scale Subsurface
                 Environmental Impacts; Investigations, Remedial Design
                 and Long Term Monitoring",
  journal =      "Journal of Mathematical Machines and Systems",
  year =         "2003",
  number =       "3-4",
  pages =        "201--218",
  address =      "Kiev",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.immsp.kiev.ua/publications/eng/2003_3_4/index.html",
  abstract =     "The global impact to human health and the environment
                 from large scale chemical / radionuclide releases is
                 well documented. Examples are the wide spread release
                 of radionuclides from the Chernobyl nuclear reactors
                 and the mobilisation of arsenic in Bangladesh. The
                 seriousness of these issues is represented by the
                 activities of the World Health Organisation, the
                 Environmental Protection Agencies in Europe, the United
                 States, and the like. The fiscal costs of addressing
                 and remediating these issues on a global scale are
                 astronomical, but then so are the fiscal and human
                 health costs of ignoring them. An integrated complete
                 methodology for optimising the response(s) to these
                 issues is presented. This work addresses development of
                 global optimal response policy design for large scale,
                 complex, environmental issues. It is important to note
                 that optimization does not singularly refer to cost
                 minimisation, but to the effective and efficient
                 balance of cost, performance, risk, management, and
                 societal priorities along with uncertainty analysis.
                 This tool integrates all of these elements into a
                 single decision framework. It provides a consistent
                 approach to designing optimal solutions that are
                 tractable, traceable, and defensible.

                 Subsurface environmental processes are represented by
                 linear and non-linear, elliptic and parabolic
                 equations. The state equations for multi-phase flow
                 (water, soil gas, NAPL), and multicomponent transport
                 (radionuclides, heavy metals, volatile organics,
                 explosives, etc.) are solved using numerical methods
                 such as finite elements. Genetic programming is used to
                 generate simulators from data when simulation models do
                 not exist, to extend the accuracy of them, or to
                 replace slow ones. To define and monitor the subsurface
                 impacts, geostatistical numerical models, Kalman
                 filtering and optimisation tools are integrated.
                 Optimal plume finding is the estimation of the plume
                 fringe(s) at a specified time using the least amount of
                 sensors (i.e. monitoring wells). Long term monitoring
                 extends this approach concept, and integrates the
                 spatial-time correlations to optimise the decision
                 variables of where to sample and when to sample over
                 the project life cycle for least cost of achieving
                 specified accuracy.

                 The remediation optimization solves the
                 multi-component, multiphase system of equations and
                 incorporates constraints on life-cycle costs, maximum
                 annual costs, maximum allowable annual discharge (for
                 assessing the monitored natural attenuation solution)
                 and constraints on where remedial system component(s)
                 can be located. It includes management overrides to
                 force certain solutions be chosen or precluded from the
                 solution design. It uses a suite of optimization
                 techniques, including the outer approximation method,
                 lipschitz global optimization, genetic algorithms, and
                 the like. A discussion of using the WAVE-WP algorithm
                 for distributed optimisation is included. This system
                 process provides the full capability to optimise
                 multi-source, multiphase, and multicomponent sites.

                 The results of applying just components of these
                 algorithms have produced savings of as much as
                 $90,000,000(US), when compared to alternative
                 solutions. This was done without loss of effectiveness,
                 and received an award from the Vice President of the
                 United States.",
  notes =        "UDC 681.3, Refs.: 45 titles. Extended Chalmers version
                 56 pages",
}

Genetic Programming entries for Larry M Deschain

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