ADEMAR JOSE POTIENS JUNIOR

ADEMAR JOSE POTIENS JUNIOR

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Study on the radiation exposure of Portable X-ray Fluorescence
2023 - DIAS, F.S.; GIOVANNI, D.N.S.; POTIENS JUNIOR, A.J.; RODRIGUES JUNIOR, O.; POTIENS, M.P.A.; ZAMBONI, C.B.
A methodology for automated radioactive waste characterization
2023 - OTERO, ANDRE G.L.; MARUMO, JULIO T.; JUNIOR POTIENS, ADEMAR J.
A desktop application for automatic gamma spectroscopy analysis with deep learning
2022 - OTERO, A.G.L.; POTIENS, A.J.; MARUMO, J.T.
Estudo do processamento de rejeitos radioativos sólidos compactáveis por plasma térmico
2021 - PRADO, EDUARDO S.P.; MIRANDA, FELIPE de S.; RITA, CRISTIAN C.P.; SILVA, ROBERSON J. da; ESSIPTCHOUK, ALEXEI M.; PETRACONI FILHO, GILBERTO; BALDAN, MAURICIO R.; POTIENS JUNIOR, ADEMAR J.
O uso de radioisótopos para as mais diversas finalidades tem se intensificado e destacado pelos benefícios que proporcionam. A geração de energia elétrica, a indústria, a agricultura, a medicina diagnóstica e terapêutica, são alguns exemplos. Porém, essas aplicações têm como desvantagem gerar rejeitos radioativos e estes requerem tratamento apropriado para deposição final. Neste âmbito, entre as tecnologias promissoras para o tratamento de rejeitos radioativos sólidos compactáveis, a utilização de plasma térmico para gerar uma descarga de arco transferido por meio de eletrodos de grafite se mostra uma tecnologia capaz de reduzir substancialmente a massa e o volume de rejeitos radioativos após expô-los a temperaturas superiores a 3.000ºC. Os resultados obtidos se mostraram bastante satisfatórios, alcançando aproximadamente 100% de redução em 30 min de processo. Esforços futuros devem ser empregados para maior confiabilidade do sistema, eliminação de radionuclídeos voláteis no efluente gasoso e otimização completa da operação.
Experimental study on treatment of simulated radioactive waste by thermal plasma
2021 - 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.
A desktop application for automatic gamma spectroscopy analysis with deep learning
2021 - OTERO, ANDRE G.L.; POTIENS JUNIOR, ADEMAR J.; LINO, JULIANA dos S.; MARUMO, JULIO T.
Comparing deep learning architectures on gamma-spectroscopy analysis for nuclear waste characterization
2021 - 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.
Um comparativo entre a utilização de redes neurais perceptron e redes neurais profundas na identificação de radionuclídeos em espectrometria gama
2020 - 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.
Tandem KAP meters calibration parameters by Monte Carlo Simulation using reference RQR radiation qualities
2016 - POTIENS JUNIOR, ADEMAR; COSTA, NATHALIA; CORREA, EDUARDO; SANTOS, LUCAS; VIVOLO, VITOR; POTIENS, MARIA da P.
The Kerma-area product quantity can be obtained by measurements carried out with a kerma-area product meter (KAP) with a plane-parallel transmission ionization chamber mounted on the X ray system. It is the integral of the air kerma over the area of the X ray beam in a plane perpendicular to the beam axis. This quantity has been important to establish the diagnostic reference levels (DRLs) all over the word. In this work the MCNP5 code was used to calculate the imparted energy in the air cavity of KAP meter and the spatial distribution of the air collision kerma in entrance plan of the KAP meter. From these data, the air kerma-area product (KAP) and the calibration coefficient for the KAP meter were calculated and compared with those obtained experimentally. The X-ray tube was easily modelled as well the complete tandem calibration set up was possible. The spectra of the diagnostic radiology RQR reference qualities measured were used as a source definition in the input card for the Monte Carlo simulation. The clinical KAP meter calibration coefficients were obtained experimentally and by Monte Carlo simulation. The differences between those values were about 2%, except for RQR 10 (5.45%). The uncertainties in Monte Carlo simulation were less than 0.5% in all cases and the FOM (Figure of Merit) was constant for a number of histories of 1 million.
Use of plasma reactor to viabilise the volumetric reduction of radioactive wastes
2020 - PRADO, E.S.P.; MIRANDA, F.S.; PETRACONI, G.; POTIENS JUNIOR, A.J.
Nuclear reactors, hospitals, industries and research institutes generate considerable amounts of radioactive waste every day. To dispose this waste in a safe and costeffective manner, it must be treated by immobilising the radionuclides and, for better stocking capacity, it must be volumetrically reduced as much as possible. To this end, plasma technology, among other promising technologies for radioactive waste treatment, exposes radioactive waste to temperatures above 1400 °C, thereby substantially reducing its volume. In the planning and managing of radioactive waste, the challenges related to plasma technology are presented as a motivation factor for the possible implantation of plasma reactors in nuclear plants and research centres, thereby improving radioactive waste management. In this study, a thermal plasma treatment process was established, and a plasma reactor was used for compactable waste processing. After 30 min of thermal plasma treatment, the volume reduction factor reached 1:99. The results demonstrate the viability of using a thermal plasma process for the volumetric reduction of radioactive waste in a safe and cost-effective manner.