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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10 resultados
Resultados da Pesquisa
Item Reliability Analysis of a Group of Internal Combustion Engines (ICM) in Thermoelectric Power Plants Using Optimization Methods for Artificial Neural Networks (ANN)(Instituto de Tecnologia e Educação Galileo da Amazônia, 2023) SILVA, Ítalo Rodrigo Soares; CAMPOS, Paola SoutoThis chapter presents a study on the reliability of internal combustion engines in thermoelectric power plants using artificial neural networks (ANN) and optimization methods. The research models engine performance based on operational and environmental variables, aiming to predict failures and optimize predictive maintenance. The results show that applying ANN reduces operational costs and improves energy generation efficiency.Item Rain Prediction Model Using Artificial Neural Networks(Instituto de Tecnologia e Educação Galileo da Amazônia, 2022) Gustavo Galdino Rodrigues; LEITE, Jandecy CabralPrecipitation is crucial for environmental sustainability and life. Accurate forecasts can mitigate floods, environmental disasters, and agricultural losses. This study presents an hourly rain prediction model using Artificial Neural Networks (ANNs) based on meteorological data from the National Institute of Meteorology (INMET) in Manaus/AM. Various network architectures were tested to determine the optimal configuration. The results demonstrated that the model effectively reproduced precipitation behavior, particularly on rainy days. However, for convective events, the model failed to capture intensity accurately, suggesting the need for additional atmospheric variables.Item Artificial Neural Networks for Predicting the Generation of Acetaldehyde in PET Resin in the Process of Injection of Plastic Packages(Instituto de Tecnologia e Educação Galileo da Amazônia, 2021) NASCIMENTO, Mauro Reis ALENCAR, David Barbosa de NASCIMENTO, Manoel Henrique Reis MONTEIRO, Carlos Alberto; LEITE, Jandecy CabralThe industrial production of preforms for PET bottles requires strict control of the PET resin drying temperature to minimize the generation of acetaldehyde (ACH), a compound that can alter the taste of beverages. This study proposes the use of Artificial Neural Networks (ANN) of the Backpropagation type (Cascadeforwardnet) to support decision-making in controlling the ideal drying temperature. The methodology included data collection from the preform injection process and the application of Computational Intelligence techniques to predict ACH levels. The results demonstrate that ANN can optimize the management of production parameters, reducing waste and improving the quality of the final product.Item Application of the NARX Model for Forecasting Wind Speed for Wind Energy Generation(Instituto de Tecnologia e Educação Galileo da Amazônia, 2021) PARENTE, Ricardo Silva ALENCAR, David Barbosa de SIQUEIRA JUNIOR, Paulo Oliveira SILVA, Ítalo Rodrigo Soares LEITE, Jandecy Cabral; LEITE, Jandecy CabralThe wind energy matrix has been gradually increasing in recent years and its importance for the renewable energy industry is increasingly linked to environmental benefits. This study applies the NARX model to forecast wind speed in the short term and, consequently, wind energy generation. The research used data from the SONDA project (System of National Organization of Environmental Data), organized by INPE, specifically from the Brasília anemometric station, covering the period from February 2005 to March 2019. The results indicate that the NARX model achieved better performance for short-term forecasts of 10 minutes up to 10 steps ahead, providing greater reliability in wind energy delivery for the energy sector.Item Estimation Of The Unitary Cost Of The Square Meter Popular Housing In The City Of Manaus Based On The Most Important Inputs, Using Artificial Neural Networks(Instituto de Tecnologia e Educação Galileo da Amazônia, 2023) FROTA, Arlindo Rubens de Oliveira; LEITE, Jandecy CabralCivil construction is one of the most expressive sectors in the economy, development, and employability in Brazil. One of the main challenges in the construction of low-income housing is the lack of predictability of costs during project execution. This study developed a tool based on Artificial Neural Networks (ANN), using data from the CUB and INCC construction price databases to predict the cost per square meter of social housing in Manaus. The tool showed a high correlation between the databases, validating its reliability and applicability in cost estimation.Item Redes Neurais Artificiais para Comparação entre Propriedades Mecânicas Materiais(Instituto de Tecnologia e Educação Galileo da Amazônia, 2025) DIAS, Jonathan Oliveira; LEITE, Jandecy CabralRegistration of a computer program entitled "Artificial Neural Networks for Comparison of Mechanical Properties of Materials," developed in Python. The program is valid for 50 years from January 1st following the publication date (12/03/2024). Its applications cover engineering, materials, and artificial intelligence areas as specified by the National Institute of Industrial Property (INPI).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.Item Modelo de Previsão de Chuva Usando Redes Neurais Artificiais(Instituto Nacional da Propriedade Industrial (INPI), 2022-09-20) BERNHARD, Gustavo Galdino Rodrigues; LIMA, Alexandra Amaro de; NASCIMENTO, Manoel Henrique Reis; ALENCAR, David Barbosa de; http://lattes.cnpq.br/4890967546423188; http://lattes.cnpq.br/6915958689972413This document describes a computer program registered under number BR512023002419-8, whose objective is to predict the occurrence of rain using artificial neural networks. The model developed applies artificial intelligence techniques to analyze meteorological data, allowing for more accurate and efficient forecasts. The tool is useful for meteorologists, researchers and institutions that need reliable climate forecasts for planning and decision making. The program was created to improve the accuracy of rain forecasts, contributing to the mitigation of risks associated with extreme weather events. Program registration is valid for 50 years from January 1, 2023.Item Estimativa do Custo Unitário de Metro Quadrado Habitacional Popular na Cidade de Manaus Baseado nos Principais Insumos, Usando Redes Neurais Artificiais(Instituto de Tecnologia e Educação Galileo da Amazônia, 2023-06-12) FROTA, Arlindo Rubens de Oliveira; NASCIMENTO, Manoel Henrique Reis; http://lattes.cnpq.br/0850846128967798This document describes a computer program registered under number BR512023002194-6, whose purpose is to calculate the unit cost per square meter for popular housing in the city of Manaus. The estimation is carried out using artificial neural networks, taking into account the main inputs needed for construction. The program was developed to support analysis and decision-making in housing projects, providing accurate, data-driven estimates for cost planning. Registration is valid for 50 years from January 1, 2023.Item Redes neurais artificiais para predição da geração de acetaldeído na resina pet no processo de injeção de pré-formas de embalagens plásticas(Instituto de Tecnologia e Educação Galileo da Amazônia, 2021-09-21) NASCIMENTO, Mauro Reis; ALENCAR, David Barbosa deThe industrial production of preforms for the manufacture of PET bottles, during the plastic injection process, is essential to regulate the temperature of the PET resin drying Silo, to control the generation of Acetaldehyde (ACH), which in high concentrations alters the flavor of carbonated or non-carbonated beverages, giving a citrus flavor to the beverage and casting doubt on the quality of packaged products. In this work, several configurations of Artificial Neural Networks (ANN's) for the Feedforward type are simulated in the specification of an ANN model to predict the formation of Acetaldehyde from the evaluation of the parameters of the PET packaging preform manufacturing process, generating information to support decision-making on optimal silo temperature control in PET resin drying, enabling specialists to make the necessary regulation decisions to lower ACH levels. The materials and methods were applied according to the manufacturer's characteristics on the moisture in the grain of the PET resin, which may contain between 50 ppm and 100 ppm of ACH, and for the analysis of the methods, data were collected, according to the temperatures and times of residence used in the blow injection process in the manufacture of the bottle preform, the generation of ACH from the PET bottle after the solid post-condensation step reached residual ACH levels lower than (3-4) ppm, as per the desired specification, reaching levels below 1 ppm. The results were found through simulations of Computational Intelligence (CI) techniques applied by ANNs, where they enabled the prediction of ACH levels generated in the plastic injection process of the preform of bottle packaging, allowing an effective management of the production parameters, helping in strategic decision making regarding the use of temperature control during the drying process of PET resin.