ADEMAR JOSE POTIENS JUNIOR

ADEMAR JOSE POTIENS JUNIOR

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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.
Plasma reactor to viabilize the volumetric reduction of radioactive wastes
2019 - PRADO, EDUARDO S.P.; GONÇALVES, MAX F.S.; MIRANDA, FELIPE de S.; PETRACONI FILHO, GILBERTO; MASSI, MARCOS; POTIENS JUNIOR, ADEMAR J.
Introduction According of the International Atomic Energy Agency – IAEA, nuclear waste, also known as, radioactive waste, is any material containing a higher concentration of radionuclides than those considered safe by the national authorities. In Brazil, there is a National Nuclear Energy Commission to regulate. These wastes can be generated in nuclear power plants, industries, hospitals and research institutes. To permanently dispose of these radioactive wastes of low and medium level of radioactivity safely and cost effectively, these should be transformed into the physical and chemical compounds suitable for radionuclides immobilization with maximum volume and exhaust gaseous reduction. Incineration is used as a treatment for a very wide range of wastes. Incineration itself is commonly only one part of a complex waste treatment system that altogether, provides for the overall management of the broad range of wastes that arise in society. The objective of waste incineration, in common with most waste treatments, is to treat waste so as to reduce its volume and hazard, whilst capturing (and thus concentrating) or destroying potentially harmful substances. The incineration of waste is one of the most widespread and effective technologies allowing considerably to reduce waste volume. In this scope, among the promising technologies for the radioactive waste treatment is the plasma technology that allows reducing substantially the waste volume after exposing them to temperatures above 2500ºC. In the planning and management 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 centers with the objective of improving the process of radioactive waste treatment. In this way, this work aims to evaluate the use of plasma technology for the incineration of radioactive waste for volumetric reduction and immobilization of this waste. Methods In this work, a plasma reactor was used for waste incineration, and all reactor parameters (electric energy ranges, maximum arc current, maximum working voltage, air fl ow, maximum energy conversion effi ciency, average temperature of heated gas, heated enthalpy) was controlled based on literature. The experiment was carried out in the plasma reactor (laboratory scale) of LPP in the ITA, using plasma torch transferred arc and with gaseous argon oxidizing agent. The electrical and thermal characteristics of the auxiliary systems of the plasma reactor were obtained using transducers and thermocouples. The composition of the gases in the process was analyzed using mass spectrometer and spectrophotometer. Results The accuracy of the data was important to ensure good results in the process, which allowed the extraction of relevant information from the experiments performed. The volumetric reduction reached 92% in relation to the sample before being processed, with a peak temperature of 1800ºC. Although a larger amount of argon fl ow intensify the cooling of the inner wall of the reactor, and further promote the dilution of the plasma, the arc voltage increases, resulting in higher power operation. Conclusions In the present work a high effi ciency thermal transfer torch was characterized , able to validate the use of the plasma jet for the treatment of radioactive waste.
Applying deep-learning in gamma-spectroscopy for radionuclide identification
2019 - OTERO, ANDRE G.L.; MARUMO, JULIO T.; POTIENS JUNIOR, ADEMAR J.
Introduction Neural networks, particularly deep neural networks, are used nowadays with great success in several tasks, such as image classifi cation, 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 fi eld: gamma-spectroscopy analysis. Using a well-known deep neural network architecture with gamma spectroscopy data, we successfully identify the radionuclides (Am-241, Ba-133, Cd-109, Co-60, Cs-137, Eu-152, Mn- 54, Na-24 and Pb-210) contained in several experiments. This neural network is also capable to identify different mixed radionuclide in the same source, demonstrating that deep neural networks can be successfully applied on gamma-spectroscopy analysis. Methods Using a HPGe detector to acquire several gamma spectra, from different sealed sources, we created a dataset that was used for the training and validation of the neural network. We created our deep neural network using python as programing language, alongside with Keras, a deep learning framework. Applying the VGG19 network architecture, except by the last layer which using softmax as activation function, we used sigmoid in order to allow classifi cation of not mutually exclusive classes in the same instance. Results After 250 epochs of training the classifi cation error on the training and test datasets reached a minimum, the same occurred with accuracy. As a fi nal test we used a spectrum from a triple sealed source, containing Am-241, Cs-137 and Co-60. As this kind of data was never seen by the network before we expect that the network generalizes well and correctly classify the spectra as containing the three isotopes. When applying the new data, the model correctly classifi ed the spectra as containing the tree radionuclide. Conclusions The model successfully classifi es different spectra with different radionuclides and his performance is good on never seen before data (the triple source sealed) demonstrating that deep learning can be used on a new domain.
Retrieval and conditioning of radium sources-containing package in Panama
2019 - MOURÃO, ROGERIO P.; SILVA, EDSON P. da; FERREIRA, MARCIO D.C.; POTIENS JUNIOR, ADEMAR J.
A team of CNEN experts successfully conducted an operation in Panama to recover and condition disused radioactive sources stored in an unsafe condition. The sources, containing the Ra-226 isotope, were used in the past to treat tumors using the technique known as brachytherapy and were immobilized in a complex package buried in an old hospital wing destined for demolition. The compartment where the package stayed for decades, built under the floor of a hospital laboratory, was contaminated with radon and daughters, including Pb-210, responsible for the contamination found. The operation consisted of extracting the package from this compartment, placing it in a cylindrical metal overpack, transporting it to the temporary storage site and carry out site decontamination. Besides the package with the sources, three 200L drums containing contaminated debris from the demolition of walls and floor were generated. No relevant event of radiological protection, such as occupational dose above the established limits, contamination of personnel or place, etc, was observed. The package produced, together with those containing the contaminated debris, was transferred to the facilities of the National Oncology Institute.
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.
Tratamento de rejeitos radioativos compactáveis aplicando a tecnologia plasma para redução volumétrica
2019 - PRADO, E.S.P.; MIRANDA, F.S.; PETRACONI, G.; POTIENS JUNIOR, A.J.
Adsorption isotherms for the removal of Am-241 in radioactive liquid wastes using magnetite nanoparticles
2017 - OSHIRO, MAURICIO T.; SAKATA, SOLANGE K.; POTIENS JUNIOR, ADEMAR J.
Americium-241 (Am-241) is a radionuclide with half-life of 432 years, emitting alpha particles and low gamma energy and it is also considered radiotoxic. Am-241 is produced, in a low level, from nuclear fuel and laboratory wastes. Magnetite nanoparticles (Fe3O4) are iron oxides that possess highly magnetic properties, and its application for removal of water contaminants refers due to its high surface area which allows the adsorption capability and the facility to be prepared and removed from the aqueous medium. In This study, magnetite was synthesized by coprecipitation method largely described. Batch experiments were accomplished at room temperature, at pH 6 and the contacts varying from 2.5, 5, 10, 20, 30, 40, 50, 60 minutes and at 30 minutes for the isotherms experiments. The solid containing magnetite and Am-241 were removed with a magnet and the solution analyzed in a gamma-ray spectrometer (Canberra Model GX2518) which could be quantified. Results show that magnetite possess a capability of removal up to 80% of Am-241 at room temperature, indicating that magnetite nanoparticles are a good sorbent for the removal of radionuclides. Langmuir and Freundlich Isotherms models were investigated and the parameters obtained. Langmuir’s isotherm showed constants of KL (75.7575 L/mg), Q (0.1617 mg/g) and R2 (0.9892) and Freundlich’s isotherm exhibited values of KF (2.6416 [(mg/g).(L/mg)1/n]), 1/n (0.7853 mg/g) and R2 (0.8395), which indicates that the Am- 241 removal from magnetite fits more suitable the Langmuir isotherm model. The thermodynamics parameters, such as the enthalpy and entropy of adsorption, the activation energy, as well as, the kinetics studies are under development.
Immobilization of graphene oxide in a poly(divinylbenzene) matrix for the treatment of liquid radioactive waste containing 137Cs
2017 - OLIVEIRA, FERNANDO M.; POTIENS JUNIOR, ADEMAR J.; FEJFAR, JOSE L.; RODRIGUES, DEBORA F.; DI VITTA, PATRICIA B.; 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. Several methods are used to treat radioactive aqueous waste, especially adsorption, which is a technique that combines cost and efficiency and is widely used in preconcentration of radionuclides. 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 of metal ions and it can be used as adsorbent to remove cesium from radioactive liquid wastes.This work, GO was immobilized in poly(divinylbenzene) to increase the specific mass and grain size of the adsorbent, that can be easily remove from solution by vaccum filtration or being used in a fixed bed column. The incorporation of the GO on the polymer surface was confirmed by electron scanning electron microscopy (SEM) Figure 1.
Technical feasibility study on volumetric reduction of radioactive wastes using plasma technology
2017 - PRADO, E.S.P.; DELLAMANO, J.C.; CARNEIRO, A.L.G.; SANTOS, R.C.; PETRACONI, G.; POTIENS JUNIOR, A.J.
The radioactive waste arising from nuclear reactors, hospitals, industry and research institutes are generated daily with a considerable amount. To final dispose of these radioactive waste safely and cost effectively, they must be transformed into physical and chemical compounds suitable for radionuclides immobilization with maximum volume and exhaust gaseous reduction. In this scope, among the promising technologies for the radioactive waste treatment, plasma technology allows reducing substantially the waste volume after exposing them to temperatures above 2,500ºC. In the planning and management 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 centers aiming at improving the process of radioactive waste management.
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.