Characterization of transuranic waste using artificial intelligence techniques

Charles A. Sparrow; Susan M. Bridges; Julia E. Hodges; Jun Chen

doi:10.1117/12.339064

29 January 1999 Characterization of transuranic waste using artificial intelligence techniques

Charles A. Sparrow, Susan M. Bridges, Julia E. Hodges, Jun Chen

Proceedings Volume 3536, Nuclear Waste Instrumentation Engineering; (1999) https://doi.org/10.1117/12.339064
Event: Photonics East (ISAM, VVDC, IEMB), 1998, Boston, MA, United States

Abstract

Characterization of containers of transuranic waste is accomplished by passive and active neutron interrogation. In the passive approach, neutrons from spontaneous fission of ²⁴⁰Pu are detected by coincidence counting. An inherent limitation of this measurement is the presence of alpha particles from ²³⁹Pu, and from other radionuclides, which induce neutron emission by way of the ((alpha) ,n) reaction. Since detection of neutrons does not necessarily imply the presence of ²⁴⁰Pu, the content of plutonium is not uniquely determined. In the active mode, neutron pulses induce fission events, producing neutrons. Gated counting permits some discrimination between the pulse and the fission neutrons. Several different artificial intelligence techniques for representing uncertainty have been investigated to determine which ones might yield additional insight in a manner similar to the judgment of experts. Three experts were asked to evaluate two sets of data, a training set and a test set, and to provide an estimate of their confidence in the results. A neural-genetic optimizer was employed to evolve a neural network to mimic each expert's characterization. The conclusions of this study are useful in refinement of the logic for both Bayesian and fuzzy techniques.

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Charles A. Sparrow, Susan M. Bridges, Julia E. Hodges, and Jun Chen "Characterization of transuranic waste using artificial intelligence techniques", Proc. SPIE 3536, Nuclear Waste Instrumentation Engineering, (29 January 1999); https://doi.org/10.1117/12.339064

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