Livros e capítulo

URI permanente para esta coleçãohttps://rigalileo.itegam.org.br/handle/123456789/6

Navegar

Agora exibindo 1 - 7 de 7

Different Deterministic Optimization Methods for Economic Load Dispatch, Switching Off Less Efficient Generators
(Instituto de Tecnologia e Educação Galileo da Amazônia, 2023) NASCIMENTO, Manoel Henrique Reis; ALENCAR, David Barbosa de
This chapter discusses different deterministic optimization methods applied to economic load dispatch, focusing on switching off less efficient generators. The study investigates the efficiency of methods such as linear programming, quadratic programming, and other mathematical approaches to reduce operational costs and improve energy efficiency in power generation systems. The research highlights how optimization can contribute to more cost-effective and environmentally friendly decision-making, proposing strategies to integrate these methodologies into power generation systems.
Maintenance Management with Application of Computational Intelligence Generating a Decision Support System for the Load Dispatch in Power Plants
(IntechOpen, 2019) LEITE, Jandecy Cabral; NASCIMENTO, Manoel Henrique Reis
This chapter proposes the development of a computational tool to support load dispatch decisions based on the operational conditions of motors and generators in thermal power plants. The tool uses a fuzzy system to classify failure probabilities, based on indicators such as lubricating oil analysis, vibration analysis, and thermography of power generation equipment. The goal is not only to monitor the equipment's condition but also to take corrective actions to maintain service reliability and quality, considering the operating conditions of the equipment.
Multi-Objective Optimization Techniques to Solve the Economic Emission Load Dispatch Problem Using Various Heuristic and Metaheuristic Algorithms
(IntechOpen, 2018) NASCIMENTO, Manoel Henrique Reis; LEITE, Jandecy Cabral
The chapter addresses the economic emission load dispatch problem, focusing on minimizing emission levels and total generation cost in thermal power plants. Various multi-objective optimization techniques, including heuristic and metaheuristic algorithms such as Simulated Annealing, Ant Lion, Dragonfly, NSGA II, and Differential Evolution, are analyzed. The chapter also compares the effectiveness of these approaches through a case study applied to a thermal generation plant.
Optimization of Economic and Environmental Dispatch Using Bio-inspired Computer Metaheuristics
(Instituto de Tecnologia e Educação Galileo da Amazônia, 2023) NASCIMENTO, Manoel Henrique Reis; CAMPOS, Paola Souto
This chapter addresses the optimization of economic and environmental dispatch in electric power systems using bio-inspired computational metaheuristics. Techniques such as Genetic Algorithms, Particle Swarm Optimization, and Ant Colony-Based Algorithms are analyzed. The study demonstrates how these approaches can reduce operational costs and minimize environmental impacts, ensuring energy efficiency and sustainability.
Probabilidade e Estatística Aplicada a Software
(Instituto de Tecnologia e Educação Galileo da Amazônia, 2022) LEITE, Jandecy Cabral; NASCIMENTO, Manoel Henrique Reis
This study proposes to analyze the use of didactic software by teachers and students as a pedagogical support tool in teaching and learning, aiming to alleviate the difficulties in learning descriptive statistics. The research was conducted at the Instituto Federal Ifam, in Manaus-AM, employing a quantitative method, questionnaires, direct observation, and the Minitab software. The results show that teachers in the mathematical sciences field often do not continuously use didactic software, which affects students' learning. The study highlights the importance of using computer labs and didactic tools to enhance statistics education.
Transformações Digitais e Indústria 4.0 no Polo Industrial de Manaus: Desafios e Inovações
(Instituto de Tecnologia e Educação Galileo da Amazônia, 2025) GUIMARÃES, Gil Eduardo; MARINELLI FILHO, Nelson; LEITE, Jandecy Cabral; NASCIMENTO, Manoel Henrique Reis
Industry 4.0 introduces a significant transformation in the production sector, integrating technologies such as IoT, artificial intelligence, and big data to create intelligent and flexible operations. This study investigates the challenges and opportunities of implementing these technologies in the Manaus Industrial Hub (PIM), considering factors such as infrastructure, organizational competencies, and cultural resistance. The research proposes a strategic roadmap divided into three phases for the adoption of these technologies, addressing training, automation, and artificial intelligence implementation. The study concludes that transitioning to Industry 4.0 requires integrated strategies that consider technical and cultural aspects, contributing to global competitiveness and the region's sustainable development.
Uma Proposta Inovadora Utilizando Blockchain para a Gestão Financeira em Obras Públicas, Tendo como Base o Sistema Brasileiro
(Instituto de Tecnologia e Educação Galileo da Amazônia, 2021) PARENTE, Ricardo Silva; NASCIMENTO, Manoel Henrique Reis
This chapter presents an innovative proposal for financial management in public works in Brazil using blockchain technology. The objective is to increase transparency and efficiency in the use of public resources, reducing the possibility of corruption and financial deviations. The proposal includes the creation of a blockchain-based system that securely and immutably records and monitors all financial transactions, allowing real-time audits. The study discusses the practical implications of implementing this technology in the public sector and proposes a model adapted to the Brazilian reality.