Nuclear spectral analysis via artificial neural networks for waste handling
- Pacific Northwest Lab., Richland, WA (United States). Environmental Molecular Sciences Lab.
- Westinghouse Hanford Co., Richland, WA (United States)
Enormous amounts of hazardous waste were generated by more than 40 years of plutonium production at the US Department of Energy`s Hanford site. A major national and international mission is to manage the existing waste and to restore the surrounding environment in a cost-effective manner. The objective of their research is to demonstrate the information processing capabilities of the neural network paradigm in real-time, automated identification of contaminants. In this paper two applications of artificial neural networks (ANNs) in nuclear spectroscopy analysis are discussed. In the first application, an ANN assigns quality coefficients to alpha particle energy spectra. These spectra are used to detect plutonium contamination in the work environment. The quality coefficients represent the levels of spectral degradation caused by miscalibration and foreign matter affecting the instruments. A set of spectra was labeled with quality coefficients by an expert and used to train the ANN expert system. The investigation shows that the expert knowledge of spectral quality can be transferred to an ANN system. The second application combines a portable gamma-ray spectrometer with an ANN to automatically identify radioactive isotopes in real-time. Two neural network paradigms are examined and compared: the linear perceptron and the optimal linear associative memory (OLAM). Both networks have a linear response and are useful in determining the composition of an unknown sample when the spectrum of the unknown is a linear superposition of known spectra.
- DOE Contract Number:
- AC06-76RL01830
- OSTI ID:
- 136891
- Report Number(s):
- CONF-941061--
- Journal Information:
- IEEE Transactions on Nuclear Science, Journal Name: IEEE Transactions on Nuclear Science Journal Issue: 4Pt1 Vol. 42; ISSN 0018-9499; ISSN IETNAE
- Country of Publication:
- United States
- Language:
- English
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