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H2Calculator: uma ferramente computacional para análise de sensibilidade e viabilidade econômica da produção de hidrogênio renovável conectada à rede elétrica

Wed, 01 Jan 2025 00:00:00 GMT

Título: H2Calculator: uma ferramente computacional para análise de sensibilidade e viabilidade econômica da produção de hidrogênio renovável conectada à rede elétrica Autor(es): Sousa, Walter Viana de Abstract: Climate change and the pursuit of decarbonization have received increasing attention, as economic growth can generate significant environmental impacts. In this context, renewable hydrogen emerges as a strategic energy vector, fundamental for the decarbonization of several productive sectors. Renewable hydrogen presents broad applicability, and Brazil, particularly the state of Ceará, has high potential for projects in this area due to its vast availability of renewable resources. However, the feasibility of these ventures critically depends on techno-economic aspects rather than solely on natural factors. Nevertheless, there is a lack of tools capable of providing parameterized economic analyses. In this context, this study aims to propose the H2Calculator, a computational tool developed in Python, designed for preliminary financial analysis in the prospecting of hydrogen production projects connected to the power grid. The tool incorporates electrolysis costs (PEM and alkaline technologies), water supply options (CAGECE, COGERH, and desalinated water), and electricity acquisition in the Free Contracting Environment (ACL), allowing the determination of indicators such as the Levelized Cost of Hydrogen (LCOH), Net Present Value (NPV), Internal Rate of Return (IRR), and discounted payback for the evaluated project. Ten distinct scenarios were simulated using the H2Calculator, four of which were analyzed in detail. In the reference scenario, with an annual production of 1.000.000 kg of H2, PEM technology, water supplied by CAGECE, and an electricity purchase tariff of R$ 163,43/MWh, an LCOH of R$ 23,94/kg was obtained, with a payback period of 23,8 years, as well as positive NPV and IRR values. Comparative analysis among the remaining scenarios revealed that electricity costs are the most influential factor in the LCOH composition, followed by the electrolyzer CAPEX, while water costs had a marginal impact. The model’s consistency was validated through comparison with reference studies. Thus, the H2Calculator is established as a valuable instrument to support decision-making and promote investment in renewable hydrogen. Tipo: Dissertação

Microwave brain stimulation plarform: neural network-based parameter optimization for non-invasive applications

Wed, 01 Jan 2025 00:00:00 GMT

Título: Microwave brain stimulation plarform: neural network-based parameter optimization for non-invasive applications Autor(es): Pereira, Francisco Estevão Simão Abstract: Non-invasive brain stimulation has numerous fundamental challenges in balancing penetration depth and spatial focus. Established methods, such as Transcranial Magnetic Stimulation (TMS) and Transcranial Direct Current Stimulation (tDCS), are effective only on superficial cortical targets. Microwave Brain Stimulation (MBS) emerges as a promising alternative to reach subcortical targets, but its clinical viability critically depends on the complex and multiobjective optimization of the antenna arrangement. This modeling problem, which involves the simultaneous adjustment of physical (hardware) and electrical (beam control) parameters, remains a gap in the literature. This thesis proposes and validates a new computational platform, the Hybrid Coevolutionary Method (HCM-TBS), specifically designed to solve this problem. The platform implements a multi-stage modular architecture that integrates global search algorithms (PSO), diversity and robustness (GA, SA), and a coevolutionary optimization core.This core decomposes the problem into subpopulations of physical (hardware) and electrical (software) parameters, optimizing them cooperatively through joint fitness assessments in an FDTD (Meep) simulator. The performance of the HCM-TBS platform was rigorously validated against baseline methods thru 30 independent runs for each group. The results demonstrate that HCM-TBS achieves statistically superior beamforming accuracy (Directivity) (p < 0.001). Moreover, the analysis reveals that the proposed platform produces solutions with significantly lower variability, demonstrating high robustness and reliability, in contrast to the extreme performance instability observed in the baseline methods. In light of the findings, this thesis contributes a robust and statistically validated computational framework for the modeling of non-invasive neurostimulation systems, prioritizing reliability and precision of focus, essential steps for future clinical translation. Tipo: Tese

Physics and machine learning in photovoltaic systems modeling: exploring hybrid approaches

Wed, 01 Jan 2025 00:00:00 GMT

Título: Physics and machine learning in photovoltaic systems modeling: exploring hybrid approaches Autor(es): Santos, Leticia de Oliveira Abstract: Hybrid modeling approaches that combine physical and data-driven techniques have shown potential for photovoltaic (PV) power estimation by leveraging the strengths of both individual techniques. Despite their increasing popularity, different hybridization approaches for PV applications and the impact of physical model selection on hybrid model performance remain underexplored. This thesis presents a comprehensive study on hybrid models for PV power prediction, introducing a novel classification into three categories: Physics-Informed Machine Learning (ML) models, Optimized Physical Models, and Physics-Guided Models. As a key contribution, an Optimized Physical model based on the energy balance of PV systems is proposed, with Bayesian inference applied to mitigate parameter uncertainties. The model is integrated into a Physical Model Chain (PMC) and combined with ML techniques using two hybridization approaches: Physics-Informed ML, which relies on ML models, and Physics-Guided models, which prioritize physical modeling. Combinations of four ML models (Multiple Linear Regression – MLR, K-Nearest Neighbors – KNN, Extreme Gradient Boosting – XGB, and Random Forests – RF) are tested with three PMC compositions: standard (PMC1), optimized model choice (PMC2), and the proposed energy balance-based (PMC3). The results show that ML model choice has the greatest impact on performance, since ensemble methods (RF and XGB) consistently present the lowest MAE values (0.030–0.032 kW/kWp), while MLR reaches 0.036–0.037 kW/kWp MAE. In comparison, PMC choice has a smaller effect, with differences across PMC1–PMC3 of 0.001 kW/kWp in MAE for most cases. Nevertheless, the proposed PMC3 surpasses classical models in both hybrid approaches, providing the most accurate foundation for hybrid frameworks. Overall, the Physics-Informed ML models combining PMC3 and XGB or RF yield the best accuracy (0.030 kW/kWp). This result demonstrates the potential of ML models to capture the underlying physics of PV conversion through feature engineering and accurately describe PV power conversion compared to primarily physical hybrid models. In conclusion, this work demonstrates the impact of physical model selection on hybrid model performance, compares hybridization strategies, and offers guidance on balancing accuracy, complexity, and generalizability in PV power modeling. Tipo: Tese

Sistema de monitoramentopara acapella choice baseado em sensores de frequência oscilatória e níveis de pressão

Mon, 01 Jan 2024 00:00:00 GMT

Título: Sistema de monitoramentopara acapella choice baseado em sensores de frequência oscilatória e níveis de pressão Autor(es): Nascimento, Andreza Costa Abstract: This work presents the development of a respiratory physiotherapy device to monitor the fre- quency of oscillations and the pressure generated during the Oscillatory Positive Expiratory Pressure (OPEP) technique in treating patients with pulmonary pathologies. The Acapella Choice device was used to generate oscillations according to the pressure exerted by the individual during expiration. A signal conditioning circuit was designed to filter and adapt the continuous analog information. The ESP32 microcontroller series was selected for analog-to-digital conversion and signal processing, performing both frequency calculation using the Fast Fourier Transform and pressure calculation. The obtained parameters are displayed to the user, enabling effective and accurate monitoring of the therapy, aiming to optimize treatment outcomes. Tipo: Dissertação