PPG.EGPSA/ITEGAM
URI permanente desta comunidadehttps://rigalileo.itegam.org.br/handle/123456789/1
A comunidade dispõe da produção técnica e científica do Programa de Pós-graduação em Engenharia, Gestão de Processos, Sistema e Ambiental (PPG.EGPSA) do Instituto de Tecnologia e Educação Galileo da Amazônia (ITEGAM), fruto da atividade de pesquisa e desenvolvimento (P&D). É possível acessar os trabalhos de conclusão do programa de pós-graduação, artigos e livros vinculados a pesquisa, desenvolvimento, inovação e extensão.
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7 resultados
Resultados da Pesquisa
Item Smart energy: aplicação do sistema fotovoltaico utilizando algoritmos genéticos para tomada de decisão na Indústria 4.0(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) WASCHINGTON, Adriana Carneiro; SILVA, Simone daThe global energy transition and the need for energy efficiency in industrial environments are driven by the search for sustainability and the reduction of environmental impacts. This work addresses the application of genetic algorithms in the management of photovoltaic systems within the context of Industry 4.0, highlighting the concept of Smart Energy. The main objective is to investigate the benefits and impacts of this approach on energy efficiency, environmental sustainability, and the reduction of operating costs at the Manaus Industrial Estate (PIM). To achieve the objectives, methods based on computer simulation and analysis of real cases were used. The research included the modeling and development of genetic algorithms capable of optimizing variables such as energy generation, storage, and consumption in photovoltaic systems. Data was collected based on local climatic conditions, energy demand profiles, and industrial operating parameters. The results indicated that the genetic algorithms enabled significant gains in energy efficiency, with an average reduction of 20% in energy waste and 15% in operating costs. In addition, the model developed proved to be effective in adapting to climate variations and industrial demands, reducing dependence on non-renewable sources and greenhouse gas emissions. The conclusion is that integrating photovoltaic systems with genetic algorithms is a promising solution for energy management in Industry 4.0, promoting sustainability and industrial competitiveness, especially in regions with high solar incidence like the Amazon. The research highlights the relevance of technological innovation in the transition to a low-carbon economy.Item Implementation of ISO 50001 – Energy Management System in a Factory in the Electrical and Electronics Sector: Multi-Case Study(Instituto de Tecnologia e Educação Galileo da Amazônia, 2022) PIMENTEL, Ingrid Mara do Carmo Fernandes LEITE, Jandecy Cabral; LEITE, Jandecy CabralOrganizational competitiveness is increasingly linked to good environmental, social, and corporate governance practices. In this context, ISO 50001 has gained prominence for establishing guidelines for efficient energy management, contributing to reduced energy consumption and environmental impact. This case study presents the implementation of the ISO 50001 standard in a factory in the electrical and electronics sector at the Industrial Hub of Manaus. The applied methodology involved strategic planning, SWOT matrix, PDCA, and Hoshin Plan. The study demonstrates that certification provided operational improvements, increased energy efficiency, and organizational engagement, consolidating the company's sustainable management.Item Smart energy: application of the photovoltaic system using genetic algorithms for decision making in industry 4.0(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) OLIVEIRA, Adriana Waschington Carneiro de; SILVA, Simone da; ALMEIDA, Anderson Alexandre Silva de; MONTEIRO, Odilon Bentes; RIBEIRO, Paulo Francisco da Silva; NASCIMENTO FILHO, Alarico Gonçalves do; LEITE, Jandecy CabralThe growing demand for sustainable solutions and the digitalization of industrial processes have driven the adoption of photovoltaic systems and advanced decision-making technologies. In the context of Industry 4.0, where automation and artificial intelligence are fundamental, these systems stand out as a clean energy alternative, promoting savings and reducing pollutant emissions. This study aims to develop a photovoltaic energy control model that uses genetic algorithms to optimize energy efficiency in industrial environments, reducing costs and dependence on non-renewable sources. The methodology included the computational modeling of a photovoltaic system and the application of genetic algorithms to optimize parameters such as panel angle and operating hours, adapting the system in real time to variable consumption and generation conditions. The results showed that the use of genetic algorithms increased the system's efficiency by up to 20% compared to traditional methods, as well as minimizing consumption from the electricity grid at peak times. This study reinforces the importance of artificial intelligence in optimizing renewable resources, contributing to energy efficiency and sustainability in Industry 4.0.Item LoRa Network and ESP32 Microprocessor Applied to a Prototype Electronic Energy Meter(Instituto de Tecnologia e Educação Galileo da Amazônia, 2022) NOGUEIRA, Caio Luiz Jodas Coautores: LEITE, Jandecy Cabral; NASCIMENTO, Marcelo Maia do; GOMES, Marivan Silva; RAMOS JUNIOR, Juarez da Silva; PAULA, Railma Lima de; CARALHO, Michael da Silva; CORREA, Henrique Mark dos Santos; BASTOS, Laís Freitas; MIRANDA, Luis Gabryel dos Santos; LEITE, Jandecy CabralThis article discusses the application of LoRa network and ESP32 microprocessor in a prototype of a polyphase electronic electric energy meter. The study was developed for a company in the Manaus Industrial Complex using an exploratory, applied, and qualitative approach, combining bibliographic research and case study. Data were collected through meetings, technical visits, and research on the subject. The results demonstrated the capability of long-distance remote communication and efficiency in energy consumption data recording, highlighting the potential innovation in smart energy metering.Item Aplicação de Inteligência Artificial para Previsões e Ajustes de Fator de Potência (FP) por Fase(Instituto de Tecnologia e Educação Galileo da Amazônia, 2025) ALMEIDA, Anderson Alexandre Silva de; LEITE, Jandecy CabralThis document certifies the registration of the software entitled "Application of Artificial Intelligence for Predictions and Adjustments of Power Factor (PF) by Phase." Developed in Python, the program leverages artificial intelligence techniques to accurately predict and adjust the power factor of electrical systems. The solution aims to optimize the energy performance of three-phase systems, reducing losses and promoting greater operational efficiency.Item Implementação de um Sistema de Medição para Melhoria do Sistema de Eficiência Energética em Empresa de Eletroeletrônico Buscando a Certificação na Norma ISO 50001: Estudo Multi-Caso(Instituto de Tecnologia e Educação Galileo da Amazônia, 2022-10-20) PIMENTEL, Ingrid Mara do Carmo Fernandes; LEITE, Jandecy CabralThis study aims to implement a measurement system to improve energy efficiency in an electronics company, with the goal of obtaining ISO 50001 certification. The research addresses the application of energy management techniques and the adoption of tools such as the PDCA cycle and digital twins to monitor and optimize energy consumption. The study highlights the importance of preventive maintenance and process readjustment to achieve energy efficiency and reduce operational costs. The applied methodology allowed for the identification of significant improvements in energy consumption, contributing to the company's sustainability and competitiveness.Item Modelo Híbrido com Redes Neurais Artificiais e Algoritmos Evolucionários para Otimização do Consumo de Combustível em Embarcações que Utilizam Motor de Combustão Interna a Diesel(Instituto Nacional da Propriedade Industrial (INPI), 2021-07-27) SIQUEIRA JUNIOR, Paulo Oliveira; NASCIMENTO, Manoel Henrique Reis; http://lattes.cnpq.br/0850846128967798This document describes a computer program registered under number BR512023001328-5, which presents a hybrid model combining artificial neural networks and evolutionary algorithms. The objective of the program is to optimize fuel consumption on vessels that use diesel internal combustion engines. The hybrid approach allows to improve energy efficiency and reduce operational costs, being especially relevant for the naval sector. The program uses advanced artificial intelligence and optimization techniques, providing a powerful tool for marine engineers and operators seeking sustainable and efficient solutions. Program registration is valid for 50 years from January 1, 2022.