Optimization of Process Variables for Co-decontamination Process Using Genetic Algorithms.

• OMORI Ryota

  The Department of Quantum Engineering and Systems Science, University of Tokyo

• SAKAKIBARA Yasushi

  The Department of Quantum Engineering and Systems Science, University of Tokyo

• SUZUKI Atsuyuki

  The Department of Quantum Engineering and Systems Science, University of Tokyo

Abstract

The process variables of the co-decontamination processes, such as flow rates and feed concentrations of elements involved, were optimized by using Genetic Algorithms (GAs). The concentration profiles and the objective function for a given flowsheet were calculated with the SOLE code. Because the mass balance of elements involved in a contactor is expressed in nonlinear simultaneous equations, derivatives of the objective function cannot be calculated explicitly. On the other hand, GAs require no derivatives of objective function and can be well applied to such a problem of optimization.<BR>In the present study, two types of spent fuel for the optimization were considered. One was the spent fuel of LWR, and the other was that of FBR. In the latter case, because the concern for criticality and third phase formation becomes more important, a penalty term, which was a function of the maximum Pu concentration in aqueous phase, was incorporated into the objective function. In both the cases, optimal values obtained were well within the range of practicability and the average fitness of a generation increased steadily as the generation proceeded, which means that GAs are appropriate methods for these problems of optimization.

Journal

• Journal of Nuclear Science and Technology

  Journal of Nuclear Science and Technology 32 (12), 1243-1249, 1995

  Atomic Energy Society of Japan

Keywords

• genetic algorithms
• process flowsheet
• spent fuel elements
• fuel reprocessing
• optimization
• objective function
• Purex process
• co-decontamination process
• solvent extraction
• simulation code

Details

• CRID: 1390001204096361472
• NII Article ID: 10002073807
• NII Book ID: AA00703720
• DOI: 10.3327/jnst.32.1243
• COI: 1:CAS:528:DyaK28XlslChtA%3D%3D
• ISSN: 18811248
• ISSN: 00223131
• Text Lang: en
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
• Abstract License Flag: Disallowed