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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Projeto Multicritério de Filtros Harmônicos Passivos para Instalações Industriais Utilizando Técnicas de Inteligência Computacional - NSGA-II
(Instituto Nacional da Propriedade Industrial (INPI), 2013-05-29) SIQUEIRA JUNIOR, Paulo Oliveira; LEITE, Jandecy Cabral; http://lattes.cnpq.br/7279183940171317
This document describes a computer program registered under number BR512023001304-8, which implements a multi-criteria design of passive harmonic filters for industrial installations, using the NSGA-II algorithm (Non-dominated Sorting Genetic Algorithm II), a computational intelligence technique. The objective of the program is to optimize the performance of passive harmonic filters, ensuring greater efficiency in mitigating harmonic distortions in industrial electrical systems. The multi-criteria approach allows you to consider different aspects of performance simultaneously, resulting in balanced and efficient solutions. This program is relevant to engineers and technicians seeking to improve power quality in industrial environments. Program registration is valid for 50 years from January 1, 2024.
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.
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.
Gestão de estoque como vantagem competitiva em uma confeitaria na cidade de Manaus-AM
(Instituto de Tecnologia e Educação Galileo da Amazônia, 2021) BAIA, Karla Josiane de Lima; ALENCAR, David Barbosa de
This chapter addresses inventory management in a confectionery located in Manaus-AM, highlighting the importance of proper planning to minimize costs and maximize profits. The research used the BCG Matrix and Production Planning and Control (PPC) to evaluate the confectionery's product portfolio and implement management techniques that ensure efficiency in inventory management, avoiding waste and material shortages. As a result, the confectionery is expected to gain a competitive advantage, staying ahead of competitors and ensuring final customer satisfaction.
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.
A Implantação do 5S em uma Confeitaria da Cidade de Manaus-AM, Utilizando Ferramentas da Qualidade
(Instituto de Tecnologia e Educação Galileo da Amazônia, 2021) COELHO, Sarah Marjurye da Silva; ALENCAR, David Barbosa de
This chapter presents the implementation of the 5S program in a confectionery located in Manaus-AM, aiming to improve the organization, cleanliness, and efficiency of the work environment. Using quality tools such as the PDCA cycle and the 5W2H action plan, the study proposes a management model that seeks to reduce costs and increase productivity. The application of 5S proved to be effective in optimizing space and improving employee performance, resulting in a more organized and productive work environment.
Digital technologies review for manufacturing processes
(Instituto de Tecnologia e Educação Galileo da Amazônia, 2021-03-31) PARENTE, Ricardo Silva; UHLMANN, Iracyanne Retto
It is apparent the industrial processes transformations caused by industry 4.0 are in advance in some countries like China, Japan, Germany and United States. But, in return, the developing countries, as the emergent Brazil, seem like to have a long way to achieve digital era. Considering manufacturing processes as the starting point the rise of industry 4.0, this research aims to show a review about the most important technologies used in smart manufacturing, including the main challenges to implement it at Brazil. The papers were collected from Web of Science (WoS), comprising 114 articles and 2 books to underpin this study. This exploratory research resulted in the presentation of some challenges faced by Brazilian industry to join the new industrial era, such as poor technological infrastructure, besides lack of investment in technologies and training of qualified people. Even though the primary motivation of this research was to present a panorama of smart manufacturing for Brazil, this study results contributes to the most of emergent countries, bringing together general concepts and addressing practical applications developed by several researchers from the international academic community.
Computational meta-heuristics based on Machine Learning to optimize fuel consumption of vessels using diesel engines
(Instituto de Tecnologia e Educação Galileo da Amazônia, 2021-05-01) SIQUEIRA JUNIOR, Paulo Oliveira; NASCIMENTO, Manoel Henrique Reis; SILVA, Ítalo Rodrigo Soares; PARENTE, Ricardo Silva; FONSECA JUNIOR, Milton; LEITE, Jandecy Cabral;
With the expansion of means of river transportation, especially in the caseof small and medium-sized vessels that make routes of greater distances, the cost of fuel, if not taken as an analysis criterion for a larger profit margin, is considered to be a primary factor , considering that the value of fuel specifically diesel to power internal combustion machines is high. Therefore, the use of tools that assist in decision making becomes necessary, as is the case of the present research, which aims to contribute with a computational model of prediction and optimization of the best speed to decrease the fuel cost considering the characteristics of the SCANIA 315 machine. propulsion model, of a vessel from the river port of Manaus that carries out river transportation to several municipalities in Amazonas. According to the results of the simulations, the best training algorithm of the Artificial Neural Network (ANN) was the BFGS Quasi-Newton considering the characteristics of the engine for optimization with Genetic Algorithm (AG).
Compliance application process as a strategic tool in the management of a third sector organization
(Instituto de Tecnologia e Educação Galileo da Amazônia, 2021-05-01) MELO, Maria Lidelmar Carvalho de; LEITE, Jandecy Cabral; SILVA, Ítalo Rodrigo Soares; SIQUEIRA junior, Paulo Oliveira
Adapting to integrity models that respect the current legislation has become fundamental in the management of organizations as a strategic form, mainly in entities with public and non-profit purposes, in a non-governmental scope. Therefore, the objective is to implement a model for the application of the compliance program in a Third Sector institution. In this way, the present article can be considered in an exploratory, applied and qualitative way, in two aspects, bibliographic research and case study, the data collection was through a meeting and interview with the company's professionals, reporting the importance of the theme. The results showed the main tools and compliance mechanism, proposing actions that can be used in practice with the purpose of providing a broad view of the functioning of the proposed model, with transparency and ethics, thus increasing the competitiveness of the business.
Sistemas embarcados para eficiência energética de ambientes climatizados
(Instituto de Tecnologia e Educação Galileo da Amazônia, 2021-06-20) CASTRO, Hilton Barros de; LEITE, Jandecy Cabral
This study presents the development and implementation of embedded systems for energy efficiency in air-conditioned environments. The proposal aims to create an automated system capable of controlling climate in environments using sensors and algorithms to adjust temperature efficiently, reducing electricity consumption. The research was conducted at a campus of the Federal Institute of Amazonas (IFAM) and involves the use of technologies such as Arduino and temperature and motion sensors, demonstrating the feasibility of intelligent systems in managing energy consumption in institutional environments.
Algoritmo de Previsão de Velocidade do Vento - HW+NARX
(Instituto Nacional da Propriedade Industrial (INPI), 2021-06-27) PARENTE, Ricardo Silva; ALENCAR, David Barbosa de; http://lattes.cnpq.br/4890967546423188
This document describes a computer program registered under number BR512023001329-3, which presents an algorithm for forecasting wind speed using a combination of exponential smoothing methods (Holt-Winters, HW) and autoregressive neural networks with exogenous inputs (NARX) . The goal of the program is to improve the accuracy of wind speed predictions, which is crucial for diverse applications such as wind energy and climate monitoring. The combination of HW+NARX methods provides a robust approach, capable of dealing with the variability and complexity of meteorological data. Program registration is valid for 50 years from January 1, 2022.
Modelo de previsão do KPI confiabilidade em um grupo de máquinas de combustão interna utilizando técnicas de redes neurais artificiais em usinas termoelétricas
(Instituto de Tecnologia e Educação Galileo da Amazônia, 2021-07) SILVA, Ítalo Rodrigo Soares; NASCIMENTO, Manoel Henrique Reis
The unavailability of equipment in thermoelectric plants for any reason becomes a risk of the entrepreneur, who as a consequence bears even greater losses with a high cost of machines stopped, in addition to the penalties sanctioned and provided by law, based on this assumption the maintenance programs are methodologies that aim to contribute with techniques and tools to mitigate this problem, however, However, only the use of maintenance programs are not enough, thus, this research aims to develop an Engine Reliability Prediction Algorithm, capable of predicting the Reliability Key Performance Indicator, with the purpose of indicating the probability of the equipment to operate in a pre-defined space of time, as the object of study has a group of internal combustion machines of Thermoelectric Power Plants. In view of this, the research meets the objectives of cataloging the significant variables for the prediction model; analyze twelve ANN training algorithms, considering the supervised learning approach, where the number of neurons, hidden layers, and activation functions are performance requirements of the network; To develop the prediction model for the reliability of the motor group, where the training algorithms are validated using the best model stopping criterion; to find the best network performance based on Mean Squared Error (MSE), Root Mean Square Error (RMSE), Linear Regression, and Best Model stopping criterion; and finally, to simulate the cataloged failure data in order to analyze the technical state of the motor group with the best model. The innovation of the research is characterized by the computational methods of data processing by using: optimization methods, iterative and heuristic, characterizing the use of artificial intelligence techniques to predict the reliability in days and months, in addition, it is used predictive maintenance indicators as: Mean Time Between Failures (MTBF), Mean Time To Repair (MTTR), Availability and Reliability. To analyze the results of this research, a set of twenty load generation units was used as parameters for investigating the frequency of failures, the twelve training algorithms were applied, with a combination between the activation functions: Sigmoid, Linear and Hyperbolic Tangent, the research results show that the techniques of Levenberg-Marquardt and Bayesian Regularization showed 100% correlation between the output and simulated variables, characterizing the efficiency in predicting in days and months.
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/0850846128967798
This 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.
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 de Tecnologia e Educação Galileo da Amazônia, 2021-07-27) SIQUEIRA JUNIOR, Paulo Oliveira; NASCIMENTO, Manoel Henrique Reis
The waterway transport is the main means of locomotion in the North Region, feeding cities through boats, speedboats, ships and ferries with the transport of goods and/or passengers. However, one of the problems of this modality of transport is the cost of supply, considering that the lack of technologies/resources that allow or facilitate a strategic vision of the business is a reality. The flow of passenger transport concentrates an average turnover of 9 million people, while the cargo transport with approximately 3 million, both distributed throughout the northern region. This fact characterizes a considerable demand in the water transportation sector, bringing to light the perspective of this research to study methods of analysis and support for decision making on the basis of fuel consumption. This dissertation aims to present the results about the development of an optimization model of fuel consumption considering the optimal speed for small vessels that operate on a regular basis in the Manaus River Port. In view of this, the research meets the objectives of mapping the variables related to the specifications of the vessel and engine to be analyzed, present the methods and results about the development of the computational hybrid model for optimization of fuel consumption when considering as a parameter of regulation the optimal speed for minimizing the predictor variable and the distance of the projected path for 3 scenarios: Manaus to Itacoatiara, Manaus to Barcelos and Manaus to Parintins, determine by statistical error analysis the best model of Artificial Neural Network (ANN) when considering number of neurons, hidden layers, activation functions (hyperbolic Tangent, Sigmoid and Linear) and training algorithm being the latter 12 possibilities each with different objectives and convergence strategies, To test the hybrid model analyzing the performance of 3 optimization algorithms (Particle Swarm, Genetic Algorithm and Simulated Annealing) as a function of computational cost and error rate in each generation of elites, and finally, to present the simulation results of the 3 scenarios mentioned above when using the hybrid model with the winning algorithm as a function of the requirements mentioned above. To analyze the results provided by the simulations, scenarios and tests for the acquisition of thebest models, the statistical techniques of Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Normalized Root Mean Squared Error (NRMSE) and Mean Absolute Percentage Error (MAPE) were used, the data regarding the vessel were acquired through meetings and interviews with experts (vessel owner) to formalize a specific database for the study delimited in this dissertation, 12 training algorithms were used to choose the best ANN, according to the results the Levenberg-Marquardt presented 100% correlation between the output variables and the Particle Swarm obtained the lowest computational cost in relation to the others proving the effectiveness of the computational hybrid model.
Modelo híbrido utilizando holt-winters e rede neural não linear autoregressiva com entradas exógenas (narx) para previsão da velocidade do vento
(Instituto de Tecnologia e Educação Galileo da Amazônia, 2021-07-27) PARENTE, Ricardo Silva; ALENCAR, David Barbosa de; http://lattes.cnpq.br/4890967546423188
The wind energy matrix is gradually increasing in recent years and its importance for the renewable energy industry is increasingly linked to the benefits in relation to the environment, with this growing energy matrix the research around the wind power generation is also increasing, and one of the strands is the wind speed prediction, because with this it is possible to predict the wind power generation and decrease the error rate in decision making in the industry of electricity generation through wind power matrix. Considering the problem of decision making and the unpredictability of wind speed, this paper aims to develop a hybrid model for wind speed prediction and consequently wind power generation, based on HoltWinters Exponential Smoothing and Nonlinear Auto-Regressive Neural Network with Exogenous Inputs (NARX). In the materials and methods, the database of the SONDA project (System of National Organization of Environmental Data) organized by INPE (National Institute for Space Research) was used, in which it was chosen to use the anemometric data from the station of Brasilia - BRB and Petrolina - PTR, where data from the years February 2005 to March 2019 of the BRB station were used for training, validation and testing, and from January 2006 to December 2015 of the PTR station for simulations of the HW, NARX and proposed model. The results obtained with the proposed hybrid HW-NARX model were compared with the HW and NARX seasonal time series forecasting algorithms, in which the proposed model was able to achieve better performance and predictability results than HW and NARX for the ultra-short-term, medium-term, and long-term time horizons.
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 de
The 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.
Composting model with the reuse of organic waste in rural schools
(Instituto de Tecnologia e Educação Galileo da Amazônia, 2021-11-01) SIMÕES, Natália Cristina Bezerra de Alencar; ALENCAR, David Barbosa de
School institutions become producers of organic waste as a result of the daily consumption of school meals offered to students during the school year. An alternative, so that this material is not incorrectly released into the environment, is the implementation of a composting model that will reuse organic waste, generating humus, which will serve as fertilizer for the implementation of a vegetable garden in the school in the rural area. This work aims to propose a composter model in a School in the Rural Area of Manaus for the reuse of organic waste, to develop a prototype of composter for the production of humic substances and mineral nutrients for the creation of gardens, to prepare the manual with guidelines for the correct and sustainable management of the composting plant and the school garden and implementing the garden system through the composting process using school organic waste. The work is a case study that proposes to implement a prototype of compost for the production of humic substances and mineral nutrients for the construction of a school garden. It is intended that the proposal raises the awareness of the school community for the correct management and reuse of solid waste generated by the school and arouse in students and teachers the interest in environmental education and behavior change for the preservation of the environment in which they live.
Modelo de compostagem com a reutilização de resíduos orgânicos em escola rural
(Instituto de Tecnologia e Educação Galileo da Amazônia, 2021-11-10) SIMÕES, Natália Cristina Bezerra de Alencar; ALENCAR, David Barbosa de
The booklet "Composting model with the reuse of organic waste in rural schools" is an educational guide on composting and the implementation of school gardens, aiming at the sustainable reuse of organic waste. The introduction addresses the increase in the production of organic waste and the importance of its correct management to avoid sanitary problems. He explains that half of the daily waste produced by Brazilians is of organic origin, which can be transformed into fertilizer. The booklet describes three types of composters: natural aeration (waste arranged in windrows), forced aeration (use of perforated tubes and mechanical pumping) and biological reactors (closed systems). It also highlights the importance of school gardens to improve student nutrition and generate resources for schools. Types of vegetable gardens include traditional, domestic, mini garden, organic and hanging. Humus, resulting from the decomposition of organic matter, is presented as an excellent fertilizer. The booklet details the composting process in three phases: mesophilic (rapid initial phase), thermophilic (temperature reduction and elimination of pathogens) and maturation (humification and mineralization). Includes a word search activity to reinforce learning of terms related to composting. In short, the booklet promotes sustainability and environmental education through composting and school gardens.
Modelo de compostagem com a reutilização de resíduos orgânicos em escola rural
(Instituto de Tecnologia e Educação Galileo da Amazônia, 2021-11-10) SIMÕES, Natália Cristina Bezerra de Alencar; ALENCAR, David Barbosa de
School institutions are producers of organic waste as a result of the daily consumption of school meals and inadequate disposal has become a problem for the Environment. Given this scenario, the project proposed the reuse of organic substances, avoiding their incorrect disposal. Therefore, the feasibility of the low-cost composting model, reusing material from school meals, generating humus, became viable for the production of fertilizer, enabling the implementation of vegetable gardens in rural schools. The research aimed to develop a composting model for schools through the reuse of organic waste, contributing to the correct disposal of waste from schools and fulfilled the other objectives by conducting a low-cost production of substances humic and mineral nutrients in the creation of gardens; prepare a Manual with guidelines for the correct and sustainable management of composting and the school garden and implement the garden system through the composting process using organic waste from the school. The work was a case study with on-site visits, having as a method the manufacture of composters, the preparation of the school garden, socioeducational lectures and the creation of a manual with instructions for the correct handling of food leftovers. The research results demonstrated the effectiveness and importance of composting for the reuse of organic food and fertilizer in school gardens. The proposed project raised the interest of students and teachers in environmental education at school and in changing their behavior in view of the need to preserve the environment.
O gerenciamento de resíduos sólidos como instrumento de sustentabilidade em denominações protestantes
(Instituto de Tecnologia e Educação Galileo da Amazônia, 2021-12-15) SILVA, Marcelo Guedes da; SILVA, Simone da; http://lattes.cnpq.br/7260488247107062
Nowadays, there is a worldwide concern about issues related to the environment and one of the biggest environmental problems caused are generated by inadequate solid waste management of such waste and the lack of society's involvement. The present work deals with the management in Protestant Christian religious institutions, which are also producers of solid waste from them and, therefore, deserve to be studied scientifically, in order to answer the following guiding question: Solid waste management in Protestant Christian institutions in the city of Manaus asserts itself as an effective instrument for the concept of sustainability? The main objective of this work is to evaluate how Protestant Christian religious institutions act in the management of solid waste, identifying if these institutions do any kind of management of this generated waste, in order to monitor how it is done using quality management tools, methodology and analysis of the results achieved. The applied methodology will be descriptive, through observation, registration, analysis and correlation of the object or facts under study, however, without manipulating them. With the information collected from selected churches in all areas of the city, in equal quantity, through a practical form and checklist based on the National Solid Waste Policy, it will be possible to build a DMAIC framework and sequentially apply the GUT tool, to notes of the most urgent problems. The results of these notes will be put into the PDCA cycle for improvement planning and, finally, the 5W2H will be used to propose improvements, which will facilitate observation, data collection and analysis of the information obtained. The survey results showed a great possibility of these Christian communities to serve as environmental schools, and that if carried out, could bring a real mass awareness of society, thanks to its enormous scope.