Presentation on theme: "Criteria for spent fuel site selection Relating to: Population Density Transportation Climate Erosion Dissolution Tectonics Human Interference: Natural."— Presentation transcript:
Criteria for spent fuel site selection Relating to: Population Density Transportation Climate Erosion Dissolution Tectonics Human Interference: Natural Resources and Site Ownership and Control Surface Characteristics Host Rock Characteristics Meteorology Hydrology Geohydrology Geochemistry Environmental Quality Socioeconomics Ease and Cost of Siting, Construction, Operation, and Closure Site Ownership and Control Offsite Installation and Operations Acknowledgements This research was funded by the Aggie Challenge Program at Texas A&M University under the supervision of Dr. Justin Yates ( email@example.com). It would not have be possible without the assistance of Dr. William Charlton (firstname.lastname@example.org), the City of College Station GIS Department, the Nuclear Regulatory Commission, U.S. Census Bureau, U.S. Geological Survey, ArcGIS Support/Help Menu.**************** email@example.com@tamu.edu Citations 1. Holland, John. Adaption in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. Cambridge: MIT Press, 1992. Print. 2. "TIGER/Line Shapefiles." Census Bureau Homepage. Web. 15 Apr. 2013. 3. Hakimi, S. “Optimum Locations of Switching Centers and the Absolute Centers and Medians of a Graph”, Operations Research, 12 (1964), 450–59. 4. "Welcome to the USGS - U.S. Geological Survey." Welcome to the USGS - U.S. Geological Survey. Web. 15 Apr. 2013. *Authors listed in alphabetical order. Nuclear Material Storage Site Selection Using Geo-Cyber Analysis Undergraduate Students at Texas A&M University Industrial and Systems Engineering: Michelle Bermudez firstname.lastname@example.org Thomas Gray email@example.com Dustin Hirner firstname.lastname@example.org@email@example.com@gmail.com Nuclear Engineering: Christina Kalich firstname.lastname@example.org_kalich@tamu.edu Jacob Landman email@example.com@tamu.edu Can Pu firstname.lastname@example.org@gmail.com Figure 2. Example of genetic algorithm with the number of generations on the x-axis and the value of the objective function on the y-axis. After fifteen generations, the objective function begins to plateau. The best fitness is the largest value, which for this example is 2.70466 with a 0.5% error with respect to the theoretical value e. Our model is based off the p-median problem. 3 This type of model is a facility location problem that locates P facilities in relation to customers so that the shortest distance is chosen between a facility and repository. It minimizes the product of the weighted Euclidian distance between repositories and facilities and facility production. As the problem size increases, the time required to solve the problem exponentially increases (NP hard) which requires a heuristic algorithm to solve it. For our model it is assumed that high level nuclear repositories are un-capacitated and production of high level nuclear waste from nuclear facilities is based off of net megawatts of electrical energy produced by each facility. A Genetic algorithm is a heuristic algorithm based on the principle of survival of the fittest. 1 For our model this type of algorithm is useful because it is NP hard. Independent variables are transformed into binary numbers, where 0 and 1 stand for two different forms of a parent gene (allele). This algorithm has high global search ability and robustness, and is widely used in non-linear optimization because of the use of crossover, mutation and selection to maximize the fitness value of the solution. For our final solution, a MatLab script of this algorithm will be used as opposed to our proof of concept model made in Microsoft Excel.. Future Considerations Sensitivity Analysis: Determine which variables in the objective function affect the final location the most Move from proof of concept model to Matlab Genetic Algorithm that includes more site selection criteria (seismic activity and proximity to water) Expand the model to include capacity and production considerations which have already been collected Expand the model from a regional (Texas) to national scale (United States) Figure 3. Map of the United States: Potential waste site locations using the coordinates of 103 power reactors and 31 research reactors to minimize the weighted Euclidian distance objective function. While the model is simplistic, it still produces sites (like Death Valley) that are well supported.\ Abstract Nuclear Material Storage Site Selection Using Geo-Cyber Analysis will combine facility location and site selection methods to analyze appropriate locations for the storage of nuclear materials, including spent fuel and radioactive waste, under multiple criteria. This project will combine traditional techniques in facility location (mathematical modeling, network optimization) with spatial analysis tools and Geographic Information Science (GIS). New nuclear material storage facility locations must balance the location’s potential for cyber exposure with its physical (geographic, environmental) vulnerabilities. Students on this project will work to develop measures for site suitability and facility vulnerability as well as mathematical models for the location of a single nuclear materials storage facility and a set of nuclear materials storage facilities. Figure 1. ArcGIS screen shot of points that represent suitable locations for a nuclear waste repository. In this figure, no points overlap Native American reservations, aquifers, or counties consisting of population densities greater than 83 per square mile. Below is a sample of a Python script to execute ArcGIS tools. Figure 4. Using a multiple facility approach decreases the objective function considerably when at least four locations are used on a national scale. ArcGIS Methods The first step of the process, which determined which locations would be suitable for site selection, was to create a fishnet that overlaid Texas. The fishnet created a grid with points in between each web that contained an x and y coordinate. Then, counties with high population density and points on Native American reservations, aquifers, salt domes, and National and State Parks were deleted off the fishnet grid. Using the near tool, points by rivers were deleted as well. The remaining points are the suitable locations.