ADEMAR JOSE POTIENS JUNIOR

ADEMAR JOSE POTIENS JUNIOR

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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.
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.
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.
Comparing deep learning architectures on gamma-spectroscopy analysis for nuclear waste characterization
2019 - OTERO, ANDRE G.L.; POTIENS JUNIOR, ADEMAR J.; CALZETA, EDUARDO P.; MARUMO, JULIO 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, we explore the capabilities of deep learning on a new field: gamma-spectroscopy analysis, comparing the classification performance of different deep neural networks architectures. We choose VGG-16, VGG-19, Xception, ResNet, InceptionV3 and MobileNet architectures which are available through the Keras Deep Learning framework to identify several different radionuclides (Am-241, Ba- 133, 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 created a dataset that was used for the training and validation of the neural networks comparison. This study demonstrates the strengths and weakness of applying deep learning on gamma-spectroscopy analysis for nuclear waste characterization.
Studies of equilibrium and kinetics of adsorption of cesium ions by graphene oxide
2017 - OLIVEIRA, FERNANDO M.; BUENO, VANESSA N.; OSHIRO, MAURICIO T.; POTIENS JUNIOR, ADEMAR J.; HIROMOTO, GORO; RODRIGUES, DEBORA F.; SAKATA, SOLANGE K.
Cesium is one of the fission products of major radiological concern, it is often found in nuclear radioactive waste generated at nuclear power plants. Graphene Oxide (GO) has attracted great attention due to its functionalized surface, which includes hydroxyl, epoxy, carbonyl and carboxyl groups, with great capacity of complexation with metal ions and can be used as adsorbent to remove cations from aqueous solutions. In this work, a treatment of radioactive waste containing 137Cs was studied. For the batch experiments of Cs+ removal, 133Cs concentrations remained after the adsorption were determined by Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES) and the results obtained were analyzed according to the Langmuir and Freundlich isotherms models. The kinetics of adsorption and Gibbs free energy were also determined. The Langmuir model was the best fit and defined a favorable adsorption. The cesium adsorption process is the pseudo-second model and the Gibbs free energy calculation indicated that the adsorption process is spontaneous.
Estudo das isotermas de adsorção e constante de distribuição par a remoção de sup(241)Am em rejeitos radioativos líquidos com magnetita
2014 - OSHIRO, M.T.; SAKATA, S.K.; POTIENS JUNIOR, A.J.
Caracterização primária de rejeitos radioativos líquidos da GRR
2015 - COUVO, NATHALIA da S.; POTIENS JUNIOR, ADEMAR J.
Characterization of the water filters cartridges from the IEA-R1 reactor using the Monte Carlo Method
2015 - COSTA, PRISCILA; POTIENS JUNIOR, ADEMAR J.
XRD and SEM/EDS characterization of coconut fibers in raw and treated forms used in the tratment of strontium in aqueous solution
2015 - FONSECA, HEVERTON C.O.; GARCIA, RAFAEL H.L.; FERREIRA, ROBSON J.; SILVA, FLAVIA R.O.; POTIENS JUNIOR, ADEMAR J.; SAKATA, SOLANGE K.