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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Filtros

2021 - 20232

Configurações

Autor: search.filters.author.NASCIMENTO, Mauro Reis

Resultados da Pesquisa

Agora exibindo 1 - 2 de 2
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    Redes neurais artificiais para predição da geração de acetaldeido na resina pet no processo de injeção de pre-formas de embalagens plásticas
    (Instituto Nacional da Propriedade Industrial (INPI), 2023-05-16) NASCIMENTO, Mauro Reis; ALENCAR, David Barbosa de; http://lattes.cnpq.br/4890967546423188
    Registration of a computer program registered with the National Institute of Industrial Property (INPI), work derived from the dissertation "Artificial neural networks for predicting the generation of acetaldehyde in pet resin in the injection process of plastic packaging preforms".
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    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.