Submitted by Claudinei Pracidelli (cpracide@ipen.br) on 2016-06-22T14:16:56Z No. of bitstreams: 0 / Made available in DSpace on 2016-06-22T14:16:56Z (GMT). No. of bitstreams: 0 / A demanda crescente por água tem feito de seu reúso planejado um tema atual e de grande importância, já citada na Agenda 21, que recomendou implementação de políticas de gestão dirigidas para o uso e reciclagem de efluentes, integrando proteção de saúde pública de grupos de risco com práticas ambientais adequadas. De acordo com as Resoluções CONAMA nº 357 e 420 os efluentes somente podem ser descartados em corpos d´água se os seus parâmetros característicos se situarem de acordo com o balizamento dado para cada classe de corpo de água. Íons metálicos podem ser removidos de soluções aquosas por diferentes processos sendo a adsorção em argilas um método que pode ser considerado efetivo e barato quando comparado aos demais. Neste estudo foi avaliada a capacidade de adsorção dos íons metálicos Cr3+, Zn2+, Cd2+, Pb2+, Cu2+ e Ni2+ em solução utilizando-se caulinita comercial com a finalidade de reúso e/ou descarte. A caracterização mineralógica e química das amostras comerciais obtidas indicou que aquela denominada caulinita C foi a que mais se adequou ao estudo visto que apresenta elevado teor de pureza mineralógica, baixos teores de elementos traço, e maior capacidade de troca catiônica. O estudo da remoção dos íons em solução indicou que o aumento razão adsorvente:adsorvato aumenta a eficiência de adsorção. O estudo da influência do pH indicou que a maior adsorção ocorre em pH levemente alcalino, pH 8. E o estudo do tempo de contato indicou que o equilíbrio de adsorção é atingido em menos de trinta minutos para todos os íons, exceto para o Ni. A análise das isotermas de adsorção indicou que a caulinita empregada neste estudo é adequada principalmente à remoção dos íons Zn (II), Cu (II) e PB (II). / Dissertação (Mestrado em Tecnologia Nuclear) / IPEN/D / Instituto de Pesquisas Energeticas e Nucleares - IPEN-CNEN/SP

Submitted by Claudinei Pracidelli (cpracide@ipen.br) on 2016-11-11T11:09:07Z No. of bitstreams: 0 / Made available in DSpace on 2016-11-11T11:09:07Z (GMT). No. of bitstreams: 0 / Este trabalho apresenta uma análise da viabilidade econômica e ambiental do processo de reciclagem de Resíduos de Construção Civil em um município. Utilizou-se como base o Município de Guarulhos onde foi efetuado um levantamento das quantidades de resíduos de construção civil produzidos, seu gerenciamento e o processo de reciclagem e reutilização dos agregados reciclados produzidos. O Município de Guarulhos implantou o primeiro Ponto de Entrega Voluntária (PEV) de resíduos em 2003 e, de forma ininterrupta, vem aumentando a disponibilidade desses pontos a população, possuindo 17 pontos em 2014. Inicialmente planejados para receber apenas resíduos provenientes de construção civil, tornaram-se pontos onde a população destina inúmeros resíduos sólidos não orgânicos. A empresa de pública responsável por obras na cidade, PROGUARU, possuiu uma Usina de Reciclagem de Resíduos de Construção Civil (URE) que produz agregados reciclados para uso em manutenções e pavimentação, além de fornecer material para utilização na Fábrica de Pré-Moldados da Prefeitura, fechando um ciclo de captação, processamento e reutilização dos Resíduos de Construção Civil no município. Foram desenvolvidos dois modelos que incluem no cálculo econômico do processo de reciclagem de Resíduos de Construção Civil de um município, a economia obtida com a diminuição do descarte irregular e com a substituição de agregados naturais pelos agregados reciclados produzidos pela PROGUARU. No período de 2003 a 2014, os Pontos de Entrega Voluntária receberam 296.210,11 . 10³ kg de resíduos totais, e enviou 214.910, 57 . 10³ kg de Resíduos de Construção Civil para a URE. O lucro anual do sistema incluindo o custo dos terrenos (PEV e URE) foi de R$ 3,50 por habitante, e desprezando o valor dos terrenos foi de R$ 5,02 por habitante. O trabalho de pesquisa comprova a viabilidade econômica da reciclagem, além dos ganhos ambientais pela diminuição dos descartes irregulares e economia de recursos naturais. / Tese (Doutorado em Tecnologia Nuclear) / IPEN/T / Instituto de Pesquisas Energeticas e Nucleares - IPEN-CNEN/SP

Submitted by Claudinei Pracidelli (cpracide@ipen.br) on 2016-11-11T16:56:25Z No. of bitstreams: 0 / Made available in DSpace on 2016-11-11T16:56:25Z (GMT). No. of bitstreams: 0 / As evidências da contaminação das águas por resíduos de medicamentos e seus subprodutos levou esse grupo de resíduos a compor a lista de poluentes orgânicos emergentes, como consequência da expansão do uso de medicamentos, como o antidepressivo cloridrato de fluoxetina e o anti-inflamatório diclofenaco. Diversos Processos Oxidativos Avançados vêm sendo aplicados para a degradação destes compostos. Dentre eles, o processo de irradiação com feixe elétrons obteve bons resultados na remoção de toxicidade e degradação de fármacos. O presente estudo consistiu em aplicar radiação ionizante como uma possível tecnologia para degradar os fármacos em águas. A irradiação de solução aquosa contendo os fármacos foi aplicada usando acelerador de elétrons, cuja eficiência foi discutida mediante análises químicas (Cromatografia Líquida Ultra Rápida e Carbono Orgânico Total (COT)), ecotoxicológicas (ensaios de toxicidade com Vibrio fischeri e Daphnia similis) e biológicas (Ensaios Respirométricos). Os resultados de COT indicaram mineralização não significativa dos compostos, mesmo sendo observada degradação máxima de 99,9% para o diclofenaco e 55% para o cloridrato de fluoxetina na mistura (1:1) em 5.0 kGy. Foi observada toxicidade aguda dos fármacos, sendo mais acentuada para a fluoxetina, seguido do diclofenaco e, finalmente, da mistura para V. fischeri. Quando D. similis foram empregadas nessa avaliação, a ordem de toxicidade foi de fluoxetina, a mistura de ambos os medicamentos e do diclofenaco. Além disso, foi observada remoção de toxicidade nas amostras irradiadas em todas as doses aplicadas para a bactéria V. fischeri, com maior eficiência de remoção de toxicidade de 55%, em 5 kGy, na mistura dos dois fármacos. Para a D. similis, foi observada remoção significativa de toxicidade da mistura apenas na dose 2,5 kGy. Os ensaios respiroétricos não indicaram biodegradabilidade após o tratamento. / Dissertação (Mestrado em Tecnologia Nuclear) / IPEN/D / Instituto de Pesquisas Energeticas e Nucleares - IPEN-CNEN/SP

Submitted by Marco Antonio Oliveira da Silva (maosilva@ipen.br) on 2017-10-27T12:25:12Z No. of bitstreams: 0 / Made available in DSpace on 2017-10-27T12:25:12Z (GMT). No. of bitstreams: 0 / Nas ultimas décadas o processo de biossorção tem alcançado grande relevância no tratamento de efluentes contendo metais potencialmente tóxicos. O uso de bactérias nesse processo tem obtido destaque, uma vez que possuem inúmeras vantagens. O presente estudo pretendeu avaliar o mecanismo envolvido no processo de biossorção dos íons Pb(II) e Hg(II) por meio das linhagens Cupriavidus metallidurans (CH34) e Cupriavidus metallidurans (CH34/pCM3). Dentre os modelos estudados a isoterma de Langmuir foi a que melhor se ajusta ao processo de adsorção, apresentando uma capacidade máxima de adsorção (qmax) de 0,98 mg.g-1 para o Hg(II) e 86,2 mg.g-1 para o Pb(II), para a linhagem selvagem. Já para a linhagem recombinante o qmax obtido foi 3,4 mg.g-1 para o mercúrio e 172,4mg g-1 para o chumbo. Baseado nos valores referentes à energia livre de Gibbs (ΔG) o processo de retenção ocorreu de forma química e espontânea. A influencia do pH foi avaliada por meio de estudo competitivo entre os íons metálicos, em níveis equimolares. O valor que melhor contemplou a adsorção para ambos os íons foi o pH 7,0, tendo o Pb(II) demonstrado maior capacidade de retenção. Em pH 2,0 houve maior retenção do Hg (II), já em pH 10,0 o Pb(II) obteve maior retenção. Indicando que o meio influencia diretamente na competição dos íons metálicos pelos sítios ativos. Constatou-se que a retenção do metal é robusta e estável ao longo de 6 meses. Os resultados indicam que a Cupriavidus metallidurans (CH34/pCM3) pode ser uma boa opção para biossorção de íons metálicos por meio de biorreator. / Dissertação (Mestrado em Tecnologia Nuclear) / IPEN/D / Instituto de Pesquisas Energéticas e Nucleares - IPEN-CNEN/SP

<p>This dissertation addresses a key 21st Century Grand Challenge – "Combatting Nuclear Terrorism”. A principal component associated with addressing this challenge pertains to timely and near real-time detection and tracking of small quantities of special nuclear materials (SNMs); the isotopes of uranium (U-235), and Plutonium (Pu) which constitute the key components of nuclear warheads. Such detection and tracking, especially for shielded U-235 using passive means is virtually impossible due to the extremely faint neutron-photon emission signals from radioactive decay which can be readily masked. Active photon and/or neutron interrogation methods are the only viable means for HEU detection but the field suffers from detector saturation in extreme 10⁴ R h^-1 radiation fields. Pu isotopes in multi-kg levels emanate neutrons from spontaneous fission that offer a means for passive interrogation with directionality, even at low levels assuming novel, high-efficiency detectors are available. Both U-235 and Pu isotopes also emit Rn gas (an alpha radiation emitter) at trace levels, during decay - which offers a possible novel means for identifying the presence of SNMs – from the faint multi Bq m^-3(pCi L^-1) alpha emitting gas and progeny in air - if only a real time sensitive enough detector were available. </p> <p> </p> <p>This thesis work was aimed at filling critical technology gaps, via researching and advancing the field of metastable fluid detector (TMFD) technology pertaining to novel/transformational passive and active (photoneutron) interrogation of SNMs. The results of R&D from this dissertation provide evidence for rapidly and conclusively monitoring for the presence of Rn-222 and progeny in air at ultra-trace (pCi L^-1) levels – even below the action levels mandated by the U.S. EPA by the development of protocols for sampling and detection using centrifugally tensioned metastable fluid detectors (CTMFD). </p> <p> </p> <p>For SNM neutron emission (either spontaneous or induced) based active and passive interrogation this dissertation presents evidence for advancing into novel designs, and schemes resulting in 100-1000x enhancements in detection efficiency for the acoustically driven ATMFD architecture in single and array forms. Novel drive modes: a direct (fixed and sweep) resonance mode, and radically novel indirect traveling wave mode were used to expand ATMFD capabilities and efficiencies beyond previous iterations of ATMFD technology. The experimentation work has been coupled with multi-physics theoretical modeling and simulations benchmarked against experimental data. ATMFDs in single and array-based architectures are being investigated for offering a novel, high-efficiency means for passive interrogation of SNMs. Coupled together with the Rn-alpha sensing approach, the ATMFD sensors for neutron monitoring enable a first-of-a-kind transformational dual mode architecture for monitoring both HEU (U-235) and Pu based SNMs.</p> <p> </p> <p>Successful results were also demonstrated for rapid and convincing 9 MeV (end point x-ray) photoneutron based active interrogation of 4.5 kg of depleted uranium in ultra-high gamma background of ~10⁴ R h^-1 using a single CTMFD or ATMFD sensor. Under such intense gamma backgrounds, conventional detectors are known to get saturated and have presented a major challenge. The research from this thesis offers a novel solution for both passive and active SNM interrogation. </p>

<p>Recent developments in Artificial Intelligence (AI) and Machine Learning (ML) have not only revolutionized engineering but also the way humanity foresees the future with machines. From self-driving cars to large language models and ChatGPT, AI and ML will continue to redefine the boundaries of innovations and reshape the way we interact with the world. The anticipated benefits are transformative, enabling enhanced productivity, improved decision-making, and the potential for significant cost savings. These developments in AI/ML and the promise for improved reliability, anomaly detection, efficient operation, etc., have unavoidably caught the attention of nuclear engineers. Advancing nuclear predictive models and providing real-time support with regard to operation and maintenance are just a few of the potential tasks AI/ML could provide assistance. Microreactors is just one example of future nuclear systems where semi-autonomous operation and fully digital instrumentation and control with AI/ML-based decision support would be required for cost-effective deployment in remote areas.</p><p>However, the world of nuclear engineering is skeptical of the direct application of AI/ML at nuclear facilities mostly due to limited past experience, potential high risk for false negatives, and limited amount of available data to demonstrate widespread applicability with high confidence. In order to curb these worries and take advantage of recent public interest in AI/ML, publicly available, real-time datasets need to be created. In this thesis, a universal AI/ML dataset is developed takes advantage of the recent digitization of Purdue University Reactor One (PUR-1) and using real-time data directly from PUR-1. The expectation is to follow the paradigm of the AI/ML community where open datasets (e.g., Kaggle, ImageNet, etc.) were the stepping stone towards new algorithms, facilitating collaborative problem-solving, and driving breakthroughs in the field of AI/ML through open competitions and knowledge sharing.</p><p>PUR-1 is capable of providing real-time research data to the second for over 2000 different parameters ranging from physical components such as neutron flux and control rod positions to calculated signals such as the system change rate. The proposed Purdue Reactor Integrated Machine Learning dataset (PRIMaL), as described in the thesis herein, includes ten signals handpicked to create simple and of various degree of complexity AI/ML benchmarks related directly to the nuclear field, with the goal of kickstarting both a new-founded interest in the nuclear field by AI/ML professionals and building faith in AI/ML amongst nuclear engineers. To the best of our knowledge, PRIMaL is the first curated AI/ML benchmark based on real reactor data and focused on nuclear applications, aiming to advance safety, efficiency, and innovation in the nuclear industry while promoting the responsible and secure use of AI/ML technologies.</p><p>To confirm the validity of the dataset and provide a simple example on how to use the dataset for AI/ML benchmarking, an example problem of classifying shutdown data as gang lowers or SCRAM was performed using three ML algorithms: support vector machine, random forest, and logistic regression. This binary classification problem was repeated 288 times for each algorithm, varying the balance ratio of the SCRAMs to gang lowers, the time prior to the shutdown, and the time after the shutdown the algorithms have access to. The sample problem was a success, as the algorithms were able to distinguish SCRAMs and gang lowers with reasonable accuracy in all cases. Future work would include gathering more data from PUR-1 for the database, as further testing with different sized balanced datasets lead to unusually high accuracy due to the smaller sample size.</p>

<p>The two primarily methodologies discussed in this thesis are the nonparametric entropy-based synthetic timeseries (NEST) and Directed infusion of data (DIOD) algorithms. </p> <p><br></p> <p>The former presents a novel synthetic data algorithm that is shown to outperform sismilar state-of-the-art, including generative networks, in terms of utility and data consistency. Majority of data used are open-source, and are cited where appropriate.</p> <p><br></p> <p>DIOD presents a novel data masking paradigm that presevres the utility, privacy, and efficiency required by the current industrial paradigm, and presents a cheaper alternative to many state-of-the-art. Data used include simulation data (source code cited), equations-based data, and open-source images (cited as needed). </p>

<p> </p> <ol> <li>This dissertation investigates the thermodynamics and transport of vacancies and interstitials of oxygen (O) and thorium (Th) in thorium dioxide (ThO₂) with varying charge states from neutral to maximum, with respect to temperature and oxygen pressure. The study also explores the impact of varying fractions of uranium (U) as a cation (<em>y</em>) on the defect disorder in mixed oxide fuels (Th_1-<em>y</em>U_<em>y</em>O₂). Understanding the properties of point defects in these oxides lays a strong foundation, as defects influence the properties of bulk materials, such as thermal transport. To accomplish the stated objectives of this dissertation, the research is structured into three sections that employ first principles density functional theory (DFT) and phonon calculations. The first section focuses on the structure, internal energy of formation, and vibrational entropy of point defects in ThO₂. The results demonstrate that defect energetics increase with an increase in defect charge for O interstitials and Th vacancies, while the opposite is true for O vacancies and Th interstitials. The lowest internal energy of formation shifts from O vacancies of charge 2+ to O interstitials and Th vacancies at various temperature ranges of 0 to 600 K, 600 to 1300 K, and 1300 to 2000 K. The second section develops a model to calculate the defect disorder and off-stoichiometry in ThO_{2±<em>x</em>} and Th_1-<em>y</em>U_<em>y</em>O_{2±<em>x</em>}. The model shows that ThO₂ exists mainly as a hypo-stoichiometric oxide between 1200 K to 2900 K for oxygen pressures ranging from 10^-30 to 10 atm, with O defects dominating this off-stoichiometric regime. The addition of U increases the thermodynamic window over which Th_1-<em>y</em>U_<em>y</em>O2 is hyper-stoichiometric, with O vacancies dominating in the hypo-stoichiometric regime, and cation vacancies and O interstitials dominating at low and high temperatures, respectively. Specifically, at low U content and low temperatures, U vacancies dominate hyper-stoichiometry, while at high U content and low temperatures, Th vacancies are dominant. This research facilitates the comprehension of the intricate changes in structural and defect equilibria that take place during nuclear fuel irradiation, where the fuel is not in a stoichiometric condition. The third section of the dissertation investigates migration barriers and diffusivities of defects and of O and Th in ThO₂. Results indicate that the migration energy of a point defect is dependent on its charge state. The average diffusivity of O vacancies exceeds that of O interstitials, while the similar is true for Th vacancies and Th interstitials above 1650 K. The self-diffusion coefficient of O and Th increases with temperature and is influenced by oxygen pressure, showing a close agreement with experimental and molecular-dynamics-based computational data. At 1500 K, the self-diffusivity of O and Th in ThO2 is 7.47 x 10^-16 m²s^-1 and 4.48 x 10^-23 m²s^-1 , respectively, while at 2500 K, the values increase to 1.06 x 10^-12 m²s^-1  and 2.28 x 10^-17 m²s^-1 , respectively. The chemical diffusion coefficients of defects decrease initially and then plateau as the hypo-stoichiometry in the oxide increases. These findings serve as a fundamental framework for understanding the diffusion-controlled processes of defects, which affect the radiation tolerance and microstructural evolution of ThO₂ as a nuclear fuel.  </li> </ol>

<p dir="ltr">The research focuses on improving core simulation procedures in Boiling Water Reactors (BWRs) by leveraging machine learning techniques. Aimed at better fuel planning and enhanced safety, a machine learning model has been developed to predict errors in existing low-fidelity, diffusion-based core simulators. The machine learning models have demonstrated the capability to accurately and efficiently predict errors in core eigenvalue and power distribution in BWR Operations. This results in a significant improvement over conventional simulation methods in nuclear reactors without increasing computational complexity.</p>

<p dir="ltr">Nuclear energy, renowned for its clean, carbon-free attributes and cost-effectiveness, stands as a pivotal pillar in the global quest for sustainable energy sources. Additionally, nuclear power, being a spatially high-concentrated industry, offers an unparalleled energy density compared to other sources of energy. Despite its numerous advantages, if a nuclear power plant (NPP) is not operated safely, it can lead to long-term shutdowns, radiation exposure to workers, radiation contamination of surrounding areas, or even a national-scale disaster, as witnessed in the Chernobyl incident of 1986. Therefore, ensuring the safe operation of nuclear reactors is considered the most important factor in their operation. Recognizing the intricate tradeoff between safety and economy, economic considerations are often sacrificed in favor of safety.</p><p dir="ltr">Given this context, it becomes crucial to develop technologies that ensure NPPs’ safety while optimizing their operational efficiency, thereby minimizing the sacrifice of economic benefits. In response to this critical need, scientists introduced the term “digital twin (DT)”, derived from the concept of product lifecycle management. As the first instance of the term, the DT model comprises the physical product, its digital representation, data flowing from the physical to the DT, and information flowing from the digital to the physical twin. In this regard, various nuclear stakeholders such as reactor designers, researchers, operators, and regulators in the nuclear sector, are pursuing the DT technologies which are expected to enable NPPs to be monitored and operated/controlled in an automated and reliable manner. DT is now being actively sought given its wide potential, including increased operational effectiveness, enhanced safety and reliability, uncertainty reduction, etc.</p><p dir="ltr">While a number of technical challenges must be overcome to successfully implement DT technology, this Ph.D. work limits its focus on one of the DT’s top challenges, i.e., model validation, which ensures that model predictions can be trusted for a given application, e.g., the domain envisaged for code usage. Model validation is also a key regulatory requirement in support of the various developmental stages starting from conceptual design to deployment, licensing, operation, and safety. To ensure a given model to be validated, the regulatory process requires the consolidation of two independent sources of knowledge, one from measurements collected from experimental conditions, and the other from code predictions that model the same experimental conditions.</p><p dir="ltr">and computational domains in an optimal manner, considering the characteristics of predictor and target responses. Successful model validation necessitates a complete data analytics pipeline, generally including data preprocessing, data analysis (model training), and result interpretation. Therefore, this Ph.D. work begins by revisiting fundamental concepts such as uncertainty classification, sensitivity analysis (SA), similarity/representativity metrics, and outlier rejection techniques, which serve as robust cornerstones of validation analysis.</p><p dir="ltr">The ultimate goal of this Ph.D. work is to develop an intelligent inference framework that infers/predicts given responses, adaptively handling various levels of data complexities, i.e., residual shape, nonlinearity, heteroscedasticity, etc. These Ph.D. studies are expected to significantly advance DT technology, enabling support for various levels of operational autonomy in both existing and first-of-a-kind reactor designs. This extends to critical aspects such as nuclear criticality safety, nuclear fuel depletion dynamics, spent nuclear fuel (SNF) analysis, and the introduction of new fuel designs, such as high burnup fuel and high-assay low-enriched uranium fuel (HALEU). These advancements are crucial in scenarios where constructing new experiments is costly, time-consuming, or infeasible with new reactor systems or high-consequence events like criticality accidents.</p>