https://repositorio.ufpb.br/jspui/handle/tede/4457 PPGEE Tue, 01 Sep 2026 09:27:48 GMT 2026-09-01T09:27:48Z http://repositorio.ufpb.br:443/jspui/retrieve/26509/ https://repositorio.ufpb.br/jspui/handle/tede/4457 https://repositorio.ufpb.br/jspui/handle/123456789/38655 Título: Análise e modelagem das emissões instantâneas de co2 em veículos de combustão interna por meio de dados OBD-II e redes neurais artificiais Autor(es): Matos, Vinício de Araújo Orientador: Villanueva, Juan Moises Maurício Abstract: This study develops a methodology to evaluate and model vehicle carbon dioxide (CO2) emissions using data collected via the OBD-II interface, integrating detailed automotive analysis with artificial neural network techniques. The overall goal is to compare the emission behavior of internal combustion engine vehicles with electric vehicles under real operating conditions. Relevant operational data (speed, throttle position, air flow, etc.) were gathered directly from vehicles through OBD-II and then explored to identify patterns and correlations (such as the strong relationship between engine load and CO2 generation). Predictive models using Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM) neural networks were trained to estimate instantaneous CO2 emissions. The LSTM model achieved the best performance with a mean absolute percentage error (MAPE) of around 5%, significantly outperforming the MLP (MAPE 16%). The comparative analysis indicates that electric vehicles eliminate direct emissions during use, but their total CO2 impact depends on the electrical grid mix and manufacturing emissions. In conclusion, the proposed approach - combining OBD-II data and neural networks - proved effective for real-time emission estimation and can complement standardized lab-based estimates. Editor: Universidade Federal da Paraíba Tipo: Dissertação Mon, 08 Dec 2025 00:00:00 GMT https://repositorio.ufpb.br/jspui/handle/123456789/38655 2025-12-08T00:00:00Z https://repositorio.ufpb.br/jspui/handle/123456789/38049 Título: Desenvolvimento de um sensor virtual de vazão utilizando redes neurais para macromedição de uma estação de distribuição de água Autor(es): Gonçalves, Luiz Felipe Rodrigues Orientador: Villanueva, Juan Moises Maurício Abstract: Law No. 14,026/2020, which established the new legal framework for sanitation in Brazil, introduced significant changes for water and sewage distribution companies by imposing stricter regulations and increasing competition in the sector. One of its key objectives is to reduce water losses and improve macromeasurement rates. According to the National Sanitation Information System (SNIS), the average water loss in Brazil is around 40%, while only 70% of the distributed water is properly measured. However, the high cost of sensors, especially flow macrometers, limits their widespread deployment, leading many companies to focus monitoring efforts on metropolitan areas. To address this issue, indirect measurement methods allow flow estimation based on network pressure and reservoir level data, reducing the need for physical sensors across the entire distribution system. In this context, artificial intelligence (AI) has been increasingly applied to improve the efficiency of water supply systems, enabling the reconstruction of missing data, fault detection, and forecasting of operational variables. Soft sensors based on neural networks allow continuous system monitoring and anomaly detection, enhancing operational decision-making and minimizing resource waste. This study presents the development and implementation of a soft sensor based on Long Short-Term Memory (LSTM) neural networks to estimate flow rates in a water supply system. The model was trained using flow, pressure, and reservoir level data collected by the Companhia de Água e Esgotos da Paraíba (CAGEPA) in the municipality of Salgado de São Félix, through an MQTT broker and cloud-based monitoring. The model validation was performed by comparing the estimated values with real measurements from physical sensors, resulting in a mean absolute percentage error lower than 5%, demonstrating the reliability of the proposed approach. Editor: Universidade Federal da Paraíba Tipo: Dissertação Mon, 15 Dec 2025 00:00:00 GMT https://repositorio.ufpb.br/jspui/handle/123456789/38049 2025-12-15T00:00:00Z https://repositorio.ufpb.br/jspui/handle/123456789/37641 Título: Modelagem e acionamento de uma máquina de indução dodecafásica com injeção harmônica para ganho de conjugado eletromagnético Autor(es): Quirino, Marcos Lázaro de Andrade Orientador: Freitas, Isaac Soares de Abstract: The electromagnetic torque can be increased, with significant gain, through the harmonic injection technique. With this procedure, it is possible we use a induction machine with a number of stator phases more than, reducing their dimensions when compared with the three- phase machines, widely used today and easily found on the market. By using multiphase machines we were able to obtain larger conjugates to meet heavier loads, improving the efficiency of certain electromechanical systems, thus taking up less space and increasing the reliability. The present work proposes a mathematical modeling for a twelve-phase induction machine with cage rotor, considering the spatial flow harmonics and the harmonic injection analysis in time. It is presented that using from the fundamental component up to the eleventh harmonic it is possible to produce dc torque, improving the mechanical torque density of a drive system. It is presented the dynamic model of the machine, the steady state analysis and the set of experimental results. Editor: Universidade Federal da Paraíba Tipo: Dissertação Thu, 29 Aug 2019 00:00:00 GMT https://repositorio.ufpb.br/jspui/handle/123456789/37641 2019-08-29T00:00:00Z https://repositorio.ufpb.br/jspui/handle/123456789/37132 Título: Sistema integrado com comunicação LoRa para monitoramento e transmissão de dados em redes e reservatórios de água da CAGEPA Autor(es): Córdula, Márcio Miranda Orientador: Souto, Cícero da Rocha Abstract: Efficient water resource management is one of the greatest challenges faced by sanitation companies in Brazil. This study presents the development and implementation of an integrated system for monitoring and transmitting data in water distribution networks and reservoirs, utilizing Internet of Things (IoT)-based technologies. The research was conducted at the Companhia de Água e Esgotos da Paraíba (CAGEPA) with the objective of reducing water losses, improving operational efficiency, and meeting the targets established by the Legal Framework for Basic Sanitation (Law No. 14,026/2020). The proposed system employs LoRa (Long Range) modules in conjunction with pressure, flow, and level sensors, which are integrated into a SCADA (Supervisory Control and Data Acquisition) platform. This combination enables real-time data transmission over long distances with low power consumption and reduced costs, allowing continuous monitoring even in remote and hardto- reach areas. This project presents a replicable model for other sanitation companies facing similar challenges, standing out for its scalability, energy efficiency, and economic viability. The findings indicate that the integration of IoT, LoRa, and supervisory systems represents a significant advancement in Brazil’s sanitation sector, fostering sustainability, natural resource preservation, and improvements in service quality for the population. Editor: Universidade Federal da Paraíba Tipo: Dissertação Fri, 25 Jul 2025 00:00:00 GMT https://repositorio.ufpb.br/jspui/handle/123456789/37132 2025-07-25T00:00:00Z