Dissertação PPG.EGPSA
URI permanente para esta coleçãohttps://rigalileo.itegam.org.br/handle/123456789/3
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Resultados da Pesquisa
Item Aplicação do DEMATEL para avaliação das barreiras à robotização no abastecimento de materiais em indústria de componentes eletrônicos no Polo Industrial de Manaus(Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) COSTA, Julianny Oliveira da; RODRIGUEZ, Carlos Manoel TaboadaThe growing need for industrial modernization has highlighted the importance of robotization in production processes, especially in the Manaus Industrial Pole (PIM), one of Brazil's main industrial complexes. Despite the potential benefits for competitiveness and operational efficiency, the implementation of robotic technologies faces obstacles in the region's electronics component industries. This technological gap, in an increasingly automated global scenario, raises questions about the factors hindering the modernization of these industrial processes. This study focuses on the barriers to robotization in material supply in production lines in the electronics component sector of the Manaus Industrial Pole, a critical process for operational efficiency. To this end, a systematic literature review was conducted, aiming to outline the current state of academic research on the subject and identify a set of barriers to the implementation of robotization. These barriers were categorized, selected, and legitimized by industry and academic experts through structured questionnaires applied via Google Forms. The responses obtained from the legitimation of the barriers by experts and the application of the Decision Making Trial and Evaluation Laboratory (DEMATEL) methodology allowed the most relevant barriers to be prioritized for further evaluation. This process was fundamental to direct the research efforts and ensure that the results were relevant to the context of the Manaus Industrial Pole. The results highlighted three main barriers as the most impactful: high initial costs, cultural resistance within organizations, and lack of adequate technological infrastructure. The DEMATEL method also revealed how these factors interrelate, influencing the adoption of robotic technologies. It is expected that the findings of this research will provide valuable subsidies to overcome these challenges, facilitating the integration of robotic technologies into the production processes of the PIM and strengthening the competitiveness of the sector in the region.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.