ARTIFICIAL NEURAL NETWORKS FOR PREDICTING THE GENETATION OF ACETALDEHYDE IN PET RESIN IN THE PROCESS OF INJECTION OF PLASTIC PACKAGES
Item
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Tipo do ITEM
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Artigo Ciêntifico
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Título do Artigo
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ARTIFICIAL NEURAL NETWORKS FOR PREDICTING THE GENETATION OF ACETALDEHYDE IN PET RESIN IN THE PROCESS OF INJECTION OF PLASTIC PACKAGES
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Descrição
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The industrial production of preforms for the manufacture of PET bottles, during the plastic injection
process, is essential to regulate the drying temperature of the PET resin, to control the generation of
Acetaldehyde (ACH), which alters the flavor of carbonated or non-carbonated drinks, giving the drink a
citrus flavor and putting in doubt the quality of packaged products. In this work, an Artificial Neural
Network (ANN) of the Backpropagation type (Cascadeforwardnet) is specified to support the decisionmaking process in controlling the ideal drying temperature of the PET resin, allowing specialists to make
the necessary temperature regulation decisions for the best performance by decreasing ACH levels. The
materials and methods were applied according to the manufacturer's characteristics on the moisture in
the PET resin grain, which may contain between 50 ppm and 100 ppm of ACH. Data were collected for the
method analysis, according to temperatures and residence times used in the blow injection process in the
manufacture of the bottle preform, the generation of ACH from the PET bottle after solid postcondensation stage reached residual ACH levels below (3-4) ppm, according to the desired specification,
reaching levels below 1 ppm. The results found through the Computational Intelligence (IC) techniques
applied by the ANNs, where they allowed the prediction of the ACH levels generated in the plastic injection
process of the bottle packaging preform, allowing an effective management of the parameters of
production, assisting in strategic decision making regarding the use of temperature control during the
drying process of PET resin.
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Abstract
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The industrial production of preforms for the manufacture of PET bottles, during the plastic injection
process, is essential to regulate the drying temperature of the PET resin, to control the generation of
Acetaldehyde (ACH), which alters the flavor of carbonated or non-carbonated drinks, giving the drink a
citrus flavor and putting in doubt the quality of packaged products. In this work, an Artificial Neural
Network (ANN) of the Backpropagation type (Cascadeforwardnet) is specified to support the decisionmaking process in controlling the ideal drying temperature of the PET resin, allowing specialists to make
the necessary temperature regulation decisions for the best performance by decreasing ACH levels. The
materials and methods were applied according to the manufacturer's characteristics on the moisture in
the PET resin grain, which may contain between 50 ppm and 100 ppm of ACH. Data were collected for the
method analysis, according to temperatures and residence times used in the blow injection process in the
manufacture of the bottle preform, the generation of ACH from the PET bottle after solid postcondensation stage reached residual ACH levels below (3-4) ppm, according to the desired specification,
reaching levels below 1 ppm. The results found through the Computational Intelligence (IC) techniques
applied by the ANNs, where they allowed the prediction of the ACH levels generated in the plastic injection
process of the bottle packaging preform, allowing an effective management of the parameters of
production, assisting in strategic decision making regarding the use of temperature control during the
drying process of PET resin.
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Língua do arquivo
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inglês
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Data da Publicação
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Ano 2020
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Palavra-chave
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Drying Temperature
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Artificial Neural Networks
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Computational Intelligence
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PET
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Acetaldehyde
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Autores
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Mauro Reis Nascimento
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David Barbosa de Alencar
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Manoel Henrique Reis Nascimento
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Local
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ITEGAM - MANAUS, 2021
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Áreas de Conhecimento
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Otimização de Processos Industriais
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Turma
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Turma 1
