ADEMAR JOSE POTIENS JUNIOR
3 resultados
Resultados de Busca
Agora exibindo 1 - 3 de 3
Artigo IPEN-doc 28406 Experimental study on treatment of simulated radioactive waste by thermal plasma2021 - PRADO, E.S.P.; MIRANDA, F.S.; ARAUJO, L.G.; PETRACONI, G.; BALDAN, M.R.; ESSIPTCHOUK, A.; POTIENS JUNIOR, A.J.Thermal plasma technology is a process that demonstrates high performance for the processing of different types of waste. This technology can also be applied in the treatment of radioactive wastes, which requires special care. Beyond that, volumetric reduction, inertization, as well as a cheap and efficient process are necessary. In this context, the purpose of this paper is to demonstrate the application of thermal plasma technology for the treatment of solid radioactive waste. For this, stable Co and Cs were used to simulate compactable and non-compactable radioactive waste; about 0.8 g Co and 0.6 g Cs were added in each experimental test. The experimental tests were conducted using plasma of transferred arc electric discharge generated by the graphite electrode inside the process reactor. The behavior and distribution of the radionuclides present in the waste were assessed during the plasma process. The results show that the significant amounts of Co and Cs leave the melt by volatilization and are transferred to the gas phase with a small portion retained in the molten slag. The retention rate of Co in the slag phase is about 0.03% and 0.30% for compactable and non-compactable waste, respectively. On the other hand, Cs is completely transferred to the gas phase when added to the compactable waste. Conversely, when in the non-compactable waste, only 1.4% Cs is retained.Artigo IPEN-doc 27853 Comparing deep learning architectures on gamma-spectroscopy analysis for nuclear waste characterization2021 - OTERO, A.G.L.; POTIENS JUNIOR, A.J.; MARUMO, J.T.Neural networks, particularly deep neural networks, are used nowadays with great success in several tasks, such as image classification, image segmentation, translation, text to speech, speech to text, achieving super-human performance. In this study, the capabilities of deep learning are explored on a new field: gamma-spectroscopy analysis, comparing the classification performance of different deep neural network architectures. The following architectures where tested: VGG-16, VGG-19, Xception, ResNet, InceptionV3, and MobileNet, which are available through the Keras Deep Learning framework to identify several different radionuclides (Am-241, Ba133, Cd-109, Co-60, Cs-137, Eu-152, Mn-54, Na-24, and Pb-210). Using an HPGe detector to acquire several gamma spectra from different sealed sources to create a dataset used for the training and validation of the neural network's comparison. This study demonstrates the strengths and weaknesses of applying deep learning on gamma-spectroscopy analysis for nuclear waste characterization.Artigo IPEN-doc 27217 Um comparativo entre a utilização de redes neurais perceptron e redes neurais profundas na identificação de radionuclídeos em espectrometria gama2020 - OTERO, A.G.L.; POTIENS JUNIOR, A.J.; MARUMO, J.T.Apresentamos os resultados da comparação entre uma Rede Neural Profunda e uma Rede Neural Perceptron na classificação de espectros gama obtidos utilizando um detector de germânio hiper-puro. Utilizando dados de diversas fontes seladas (Am-241, Ba-133, Cd-109, Co-57, Co-60, Cs-137, Eu-152, Mn-54, Na-24, and Pb-210) foram gerados uma lista extensa de espectros para treino e validação contendo, respectivamente, 500 e 160 espectros, onde foram mesclados até três radionuclídeos em um único espectro. Depois de 250 épocas de treino foram validadas a exatidão de cada um dos modelos utilizando o conjunto de validação. O modelo de rede neural profunda obteve uma exatidão de classificação de 96,25% enquanto a rede neural perceptron obteve uma exatidão de 80,62%. Os resultados mostram um desempenho robusto e consistentemente melhor das redes neurais profundas, frente as redes neurais perceptron.