Modelagem e previsão da adsorção do CO2 em diferentes adsorventes utilizando equações fenomenológicas e redes neurais: uma abordagem combinada
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Biblioteca Digital de Teses e Dissertações do UNIOESTE |
Texto Completo: | https://tede.unioeste.br/handle/tede/7194 |
Resumo: | The high emission of carbon dioxide (CO2) is one of the main causes of global warming. Adsorption is one of the potential options for capture, since it is easy to apply and adapt in industrial processes, in addition to being low cost. However, it is still necessary to study the optimal conditions to increase capture efficiency. Among the materials used for adsorption, zeolites, metal-organic frameworks and activated carbon stand out, since their characteristics such as pore volume and distribution, as well as surface area are variables of significant importance for the efficiency of the process. However, in classical equilibrium modeling these properties do not appear in the mathematical formulation. Phenomenological models based on equations of state are alternatives to describe adsorption phenomena, however, the phenomenological route presents some limitations when the number of variables involved in the process increases. Thus, the main objective of this work is to perform the mathematical modeling of equilibrium data of CO2 adsorption on different types of adsorbents using phenomenological models (2D Equations of State and classical Adsorption Isotherms) and artificial neural networks. Thus, adsorption data collection was initially performed for the following adsorbents: Cu-BTC, Zeolite 13X, IRMOF-1, ZIF-8, Mg-MOF-74, activated carbon and Zeolite 5A, with different conditions of temperature, pressure, besides the textural properties of the adsorbents: surface area and pore volume, thus totaling 2991 data of temperature, pressure and adsorbed amount. After data collection, mathematical modeling was performed with the classical equations (Langmuir, Freundlich, Toth and Sips) and with the two-dimensional equations of state (Van der Waals, Redlich-Kwong and Pegn-Robinson). The models that best represented the investigated systems were Toth (IRMOF-1 and activated carbon), Sips (ZIF-8 and Zeolite 5A), Van der Waals (Zeolite 13X and Mg-MOF-74) and Langmuir (Cu-BTC). In a second step the best model for each type of adsorbent was used to generate a standardized database with the adsorption equilibrium conditions together with the textural properties to perform the training with neural networks. For the training, the K-Fold cross-validation technique was used, with 4 subsets, with 15% separation of the data to perform the final validation, 6 different conditions were tested and optimized, with 1, 2 and 3 internal layers of neurons, testing perceptron and recurrent neurons. The criterion used to separate the data was the combination of the input variables: area, temperature and pore volume for Cu-BTC, Zeolite 13X and IRMOF-1, and area and temperature for the rest of the adsorbents, in order to avoid statistical analysis with previously trained data and bring greater robustness in the models. The number of neurons and the activation function were chosen using genetic algorithms. With the best configuration chosen, the optimal number of epochs was then determined, comparing with the test data. Finally, with the best configuration, a statistical comparison was made to choose the best model obtained among the 6 configurations. In general, the configuration with perceptron neurons stood out in relation to the recurrent networks, and only for activated carbon that the modeling was not satisfactory. Through the parametric analysis it was observed that the area has a negative correlation with adsorption, except for Zeolite 13X and the pore volume showed a positive correlation for Cu-BTC, negative for Zeolite 13X and inconclusive for IRMOF-1. Furthermore, the use of neural networks combined with phenomenological equations was satisfactory for generating generic models with predictive capacity under different operating conditions and textural properties of the adsorbents. |
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Palu, Fernandohttp://lattes.cnpq.br/5272104493905559Silva, Edson Antonio dahttp://lattes.cnpq.br/9304493875700070Borba, Carlos Eduardohttp://lattes.cnpq.br/075004872022910Johann, Graciellehttp://lattes.cnpq.br/0042481312764501Palu, Fernandohttp://lattes.cnpq.br/5272104493905559http://lattes.cnpq.br/0341339358177992Colombo, William Luis Reginatto2024-05-08T20:02:39Z2023-08-18COLOMBO, William Luis Reginatto. Modelagem e previsão da adsorção do CO2 em diferentes adsorventes utilizando equações fenomenológicas e redes neurais: uma abordagem combinada. 2023. 140 f. Dissertação (Mestrado em Engenharia Química) - Universidade Estadual do Oeste do Paraná, Toledo, 2023.https://tede.unioeste.br/handle/tede/7194The high emission of carbon dioxide (CO2) is one of the main causes of global warming. Adsorption is one of the potential options for capture, since it is easy to apply and adapt in industrial processes, in addition to being low cost. However, it is still necessary to study the optimal conditions to increase capture efficiency. Among the materials used for adsorption, zeolites, metal-organic frameworks and activated carbon stand out, since their characteristics such as pore volume and distribution, as well as surface area are variables of significant importance for the efficiency of the process. However, in classical equilibrium modeling these properties do not appear in the mathematical formulation. Phenomenological models based on equations of state are alternatives to describe adsorption phenomena, however, the phenomenological route presents some limitations when the number of variables involved in the process increases. Thus, the main objective of this work is to perform the mathematical modeling of equilibrium data of CO2 adsorption on different types of adsorbents using phenomenological models (2D Equations of State and classical Adsorption Isotherms) and artificial neural networks. Thus, adsorption data collection was initially performed for the following adsorbents: Cu-BTC, Zeolite 13X, IRMOF-1, ZIF-8, Mg-MOF-74, activated carbon and Zeolite 5A, with different conditions of temperature, pressure, besides the textural properties of the adsorbents: surface area and pore volume, thus totaling 2991 data of temperature, pressure and adsorbed amount. After data collection, mathematical modeling was performed with the classical equations (Langmuir, Freundlich, Toth and Sips) and with the two-dimensional equations of state (Van der Waals, Redlich-Kwong and Pegn-Robinson). The models that best represented the investigated systems were Toth (IRMOF-1 and activated carbon), Sips (ZIF-8 and Zeolite 5A), Van der Waals (Zeolite 13X and Mg-MOF-74) and Langmuir (Cu-BTC). In a second step the best model for each type of adsorbent was used to generate a standardized database with the adsorption equilibrium conditions together with the textural properties to perform the training with neural networks. For the training, the K-Fold cross-validation technique was used, with 4 subsets, with 15% separation of the data to perform the final validation, 6 different conditions were tested and optimized, with 1, 2 and 3 internal layers of neurons, testing perceptron and recurrent neurons. The criterion used to separate the data was the combination of the input variables: area, temperature and pore volume for Cu-BTC, Zeolite 13X and IRMOF-1, and area and temperature for the rest of the adsorbents, in order to avoid statistical analysis with previously trained data and bring greater robustness in the models. The number of neurons and the activation function were chosen using genetic algorithms. With the best configuration chosen, the optimal number of epochs was then determined, comparing with the test data. Finally, with the best configuration, a statistical comparison was made to choose the best model obtained among the 6 configurations. In general, the configuration with perceptron neurons stood out in relation to the recurrent networks, and only for activated carbon that the modeling was not satisfactory. Through the parametric analysis it was observed that the area has a negative correlation with adsorption, except for Zeolite 13X and the pore volume showed a positive correlation for Cu-BTC, negative for Zeolite 13X and inconclusive for IRMOF-1. Furthermore, the use of neural networks combined with phenomenological equations was satisfactory for generating generic models with predictive capacity under different operating conditions and textural properties of the adsorbents.A alta emissão de dióxido de carbono (CO2) é uma das principais causas do aquecimento global. A adsorção é uma das opções em potencial para a captura, uma vez que é de fácil aplicação e adaptação em processos industriais, além de ser de baixo custo. Entretanto, ainda é necessário estudar as condições ótimas para aumentar a eficiência de captura. Destacam-se, dentre os materiais utilizados para adsorção, as zeólitas, as estruturas metal-orgânicas e o carvão ativado, tendo em vista que, suas características como volume e distribuição de poro, bem como área superficial são variáveis de expressiva importância para a eficiência do processo. No entanto, na modelagem clássica do equilíbrio estas propriedades não aparecem na formulação matemática. Os modelos fenomenológicos baseados em equações de estado são alternativas para descrever fenômenos de adsorção, contudo, a via fenomenológica apresenta algumas limitações quando o número de variáveis envolvidas no processo aumenta. Assim este trabalho tem como principal objetivo realizar a modelagem matemática dos dados de equilíbrio da adsorção do CO2 em diferentes tipos de adsorventes empregando modelos fenomenológicos (Equações de Estado 2D e Isotermas clássicas de Adsorção) e redes neurais artificiais. Assim, inicialmente foi realizado a coleta de dados de adsorção para os seguintes adsorventes: Cu-BTC, Zeólita 13X, IRMOF-1, ZIF-8, Mg-MOF-74, carbono ativado e Zeólita 5A, com diferentes condições de temperatura, pressão, além das propriedades texturais dos adsorventes: área superficial e volume de poro, totalizando assim 2991 dados de temperatura, pressão e quantidade adsorvida. Após a coleta dos dados, realizou-se uma modelagem matemática com as equações clássicas (Langmuir, Freundlich, Toth e Sips) e com as equações de estados bidimensionais (Van der Waals, Redlich-Kwong e Pegn-Robinson). Os modelos que melhor representaram os sistemas investigados foram Toth (IRMOF-1 e carvão ativado), Sips (ZIF-8 e Zeólita 5A), Van der Waals (Zeólita 13X e Mg-MOF-74) e Langmuir (Cu-BTC). Numa segunda etapa o melhor modelo para cada tipo de adsorvente foi utilizado para gerar uma base de dados padronizada com as condições de equilíbrio de adsorção junto com as propriedades texturais para realizar o treinamento com redes neurais. Para o treinamento utilizou-se como técnica de validação cruzada o K-Fold, com 4 subconjuntos, com separação de 15% dos dados para realizar a validação final, foram testadas e otimizadas 6 diferentes condições, com 1, 2 e 3 camadas internas de neurônios, testando neurônios perceptron e recorrentes. O critério usado para separar os dados foi a combinação das variáveis de entrada: área, temperatura e volume de poro para o Cu-BTC, Zeólita 13 X e IRMOF-1, e área e temperatura para o restante dos adsorventes, com a finalidade de evitar análises estatísticas com dados já treinados previamente e trazer maior robustez nos modelos. A quantidade de neurônios e a função de ativação foi escolhida com o uso de algoritmos genéticos. Com a melhor configuração escolhida, em seguida determinou-se a quantidade ótima de épocas, comparando-se com os dados de teste. Por fim, com a melhor configuração fez-se um comparativo estatístico para escolha do melhor modelo obtido entre as 6 configurações. No geral a configuração com neurônios perceptron se sobressaiu em relação as redes recorrentes, e apenas para o carvão ativado que a modelagem não foi satisfatória. Através da análise paramétrica observou-se que a área tem uma correlação negativa com a adsorção, com exceção da Zeólita 13X e o volume de poro apresentou correlação positiva para o Cu-BTC, negativa para a Zeólita 13X e inconclusiva para o IRMOF-1. Ademais, o uso de redes neurais combinados com as equações fenomenológicas foram satisfatórios para geração de modelos genéricos com capacidade preditiva em diferentes condições operacionais e propriedades texturais dos adsorventes.Submitted by Marilene Donadel (marilene.donadel@unioeste.br) on 2024-05-08T20:02:39Z No. of bitstreams: 1 William_Colombo_2023.pdf: 6521960 bytes, checksum: 85d4795e8058b601a696270c4e8f00c6 (MD5)Made available in DSpace on 2024-05-08T20:02:39Z (GMT). No. of bitstreams: 1 William_Colombo_2023.pdf: 6521960 bytes, checksum: 85d4795e8058b601a696270c4e8f00c6 (MD5) Previous issue date: 2023-08-18Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESapplication/pdfpor-2624803687637593200500Universidade Estadual do Oeste do ParanáToledoPrograma de Pós-Graduação em Engenharia QuímicaUNIOESTEBrasilCentro de Engenharias e Ciências Exatashttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAdsorçãoRedes neuraisAlgoritimos genéticosModelagem matemática.AdsorptionNeural networksGenetic algorithmsMathematical modelingENGENHARIA QUIMICA::PROCESSOS INDUSTRIAIS DE ENGENHARIA QUIMICAModelagem e previsão da adsorção do CO2 em diferentes adsorventes utilizando equações fenomenológicas e redes neurais: uma abordagem combinadaModeling and prediction of CO2 adsorption on different adsorbents using phenomenological equations and neural networks: a combined approachinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesis1582274381427649589600600600600-773440212408214692288981387697583185912075167498588264571reponame:Biblioteca Digital de Teses e Dissertações do UNIOESTEinstname:Universidade Estadual do Oeste do Paraná (UNIOESTE)instacron:UNIOESTEORIGINALWilliam_Colombo_2023.pdfWilliam_Colombo_2023.pdfapplication/pdf6521960http://tede.unioeste.br:8080/tede/bitstream/tede/7194/2/William_Colombo_2023.pdf85d4795e8058b601a696270c4e8f00c6MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-82165http://tede.unioeste.br:8080/tede/bitstream/tede/7194/1/license.txtbd3efa91386c1718a7f26a329fdcb468MD51tede/71942024-05-08 17:02:40.018oai:tede.unioeste.br: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Biblioteca Digital de Teses e Dissertaçõeshttp://tede.unioeste.br/PUBhttp://tede.unioeste.br/oai/requestbiblioteca.repositorio@unioeste.bropendoar:2024-05-08T20:02:40Biblioteca Digital de Teses e Dissertações do UNIOESTE - Universidade Estadual do Oeste do Paraná (UNIOESTE)false |
dc.title.por.fl_str_mv |
Modelagem e previsão da adsorção do CO2 em diferentes adsorventes utilizando equações fenomenológicas e redes neurais: uma abordagem combinada |
dc.title.alternative.eng.fl_str_mv |
Modeling and prediction of CO2 adsorption on different adsorbents using phenomenological equations and neural networks: a combined approach |
title |
Modelagem e previsão da adsorção do CO2 em diferentes adsorventes utilizando equações fenomenológicas e redes neurais: uma abordagem combinada |
spellingShingle |
Modelagem e previsão da adsorção do CO2 em diferentes adsorventes utilizando equações fenomenológicas e redes neurais: uma abordagem combinada Colombo, William Luis Reginatto Adsorção Redes neurais Algoritimos genéticos Modelagem matemática. Adsorption Neural networks Genetic algorithms Mathematical modeling ENGENHARIA QUIMICA::PROCESSOS INDUSTRIAIS DE ENGENHARIA QUIMICA |
title_short |
Modelagem e previsão da adsorção do CO2 em diferentes adsorventes utilizando equações fenomenológicas e redes neurais: uma abordagem combinada |
title_full |
Modelagem e previsão da adsorção do CO2 em diferentes adsorventes utilizando equações fenomenológicas e redes neurais: uma abordagem combinada |
title_fullStr |
Modelagem e previsão da adsorção do CO2 em diferentes adsorventes utilizando equações fenomenológicas e redes neurais: uma abordagem combinada |
title_full_unstemmed |
Modelagem e previsão da adsorção do CO2 em diferentes adsorventes utilizando equações fenomenológicas e redes neurais: uma abordagem combinada |
title_sort |
Modelagem e previsão da adsorção do CO2 em diferentes adsorventes utilizando equações fenomenológicas e redes neurais: uma abordagem combinada |
author |
Colombo, William Luis Reginatto |
author_facet |
Colombo, William Luis Reginatto |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Palu, Fernando |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/5272104493905559 |
dc.contributor.advisor-co1.fl_str_mv |
Silva, Edson Antonio da |
dc.contributor.advisor-co1Lattes.fl_str_mv |
http://lattes.cnpq.br/9304493875700070 |
dc.contributor.referee1.fl_str_mv |
Borba, Carlos Eduardo |
dc.contributor.referee1Lattes.fl_str_mv |
http://lattes.cnpq.br/075004872022910 |
dc.contributor.referee2.fl_str_mv |
Johann, Gracielle |
dc.contributor.referee2Lattes.fl_str_mv |
http://lattes.cnpq.br/0042481312764501 |
dc.contributor.referee3.fl_str_mv |
Palu, Fernando |
dc.contributor.referee3Lattes.fl_str_mv |
http://lattes.cnpq.br/5272104493905559 |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/0341339358177992 |
dc.contributor.author.fl_str_mv |
Colombo, William Luis Reginatto |
contributor_str_mv |
Palu, Fernando Silva, Edson Antonio da Borba, Carlos Eduardo Johann, Gracielle Palu, Fernando |
dc.subject.por.fl_str_mv |
Adsorção Redes neurais Algoritimos genéticos Modelagem matemática. |
topic |
Adsorção Redes neurais Algoritimos genéticos Modelagem matemática. Adsorption Neural networks Genetic algorithms Mathematical modeling ENGENHARIA QUIMICA::PROCESSOS INDUSTRIAIS DE ENGENHARIA QUIMICA |
dc.subject.eng.fl_str_mv |
Adsorption Neural networks Genetic algorithms Mathematical modeling |
dc.subject.cnpq.fl_str_mv |
ENGENHARIA QUIMICA::PROCESSOS INDUSTRIAIS DE ENGENHARIA QUIMICA |
description |
The high emission of carbon dioxide (CO2) is one of the main causes of global warming. Adsorption is one of the potential options for capture, since it is easy to apply and adapt in industrial processes, in addition to being low cost. However, it is still necessary to study the optimal conditions to increase capture efficiency. Among the materials used for adsorption, zeolites, metal-organic frameworks and activated carbon stand out, since their characteristics such as pore volume and distribution, as well as surface area are variables of significant importance for the efficiency of the process. However, in classical equilibrium modeling these properties do not appear in the mathematical formulation. Phenomenological models based on equations of state are alternatives to describe adsorption phenomena, however, the phenomenological route presents some limitations when the number of variables involved in the process increases. Thus, the main objective of this work is to perform the mathematical modeling of equilibrium data of CO2 adsorption on different types of adsorbents using phenomenological models (2D Equations of State and classical Adsorption Isotherms) and artificial neural networks. Thus, adsorption data collection was initially performed for the following adsorbents: Cu-BTC, Zeolite 13X, IRMOF-1, ZIF-8, Mg-MOF-74, activated carbon and Zeolite 5A, with different conditions of temperature, pressure, besides the textural properties of the adsorbents: surface area and pore volume, thus totaling 2991 data of temperature, pressure and adsorbed amount. After data collection, mathematical modeling was performed with the classical equations (Langmuir, Freundlich, Toth and Sips) and with the two-dimensional equations of state (Van der Waals, Redlich-Kwong and Pegn-Robinson). The models that best represented the investigated systems were Toth (IRMOF-1 and activated carbon), Sips (ZIF-8 and Zeolite 5A), Van der Waals (Zeolite 13X and Mg-MOF-74) and Langmuir (Cu-BTC). In a second step the best model for each type of adsorbent was used to generate a standardized database with the adsorption equilibrium conditions together with the textural properties to perform the training with neural networks. For the training, the K-Fold cross-validation technique was used, with 4 subsets, with 15% separation of the data to perform the final validation, 6 different conditions were tested and optimized, with 1, 2 and 3 internal layers of neurons, testing perceptron and recurrent neurons. The criterion used to separate the data was the combination of the input variables: area, temperature and pore volume for Cu-BTC, Zeolite 13X and IRMOF-1, and area and temperature for the rest of the adsorbents, in order to avoid statistical analysis with previously trained data and bring greater robustness in the models. The number of neurons and the activation function were chosen using genetic algorithms. With the best configuration chosen, the optimal number of epochs was then determined, comparing with the test data. Finally, with the best configuration, a statistical comparison was made to choose the best model obtained among the 6 configurations. In general, the configuration with perceptron neurons stood out in relation to the recurrent networks, and only for activated carbon that the modeling was not satisfactory. Through the parametric analysis it was observed that the area has a negative correlation with adsorption, except for Zeolite 13X and the pore volume showed a positive correlation for Cu-BTC, negative for Zeolite 13X and inconclusive for IRMOF-1. Furthermore, the use of neural networks combined with phenomenological equations was satisfactory for generating generic models with predictive capacity under different operating conditions and textural properties of the adsorbents. |
publishDate |
2023 |
dc.date.issued.fl_str_mv |
2023-08-18 |
dc.date.accessioned.fl_str_mv |
2024-05-08T20:02:39Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
COLOMBO, William Luis Reginatto. Modelagem e previsão da adsorção do CO2 em diferentes adsorventes utilizando equações fenomenológicas e redes neurais: uma abordagem combinada. 2023. 140 f. Dissertação (Mestrado em Engenharia Química) - Universidade Estadual do Oeste do Paraná, Toledo, 2023. |
dc.identifier.uri.fl_str_mv |
https://tede.unioeste.br/handle/tede/7194 |
identifier_str_mv |
COLOMBO, William Luis Reginatto. Modelagem e previsão da adsorção do CO2 em diferentes adsorventes utilizando equações fenomenológicas e redes neurais: uma abordagem combinada. 2023. 140 f. Dissertação (Mestrado em Engenharia Química) - Universidade Estadual do Oeste do Paraná, Toledo, 2023. |
url |
https://tede.unioeste.br/handle/tede/7194 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.program.fl_str_mv |
1582274381427649589 |
dc.relation.confidence.fl_str_mv |
600 600 600 600 |
dc.relation.department.fl_str_mv |
-7734402124082146922 |
dc.relation.cnpq.fl_str_mv |
8898138769758318591 |
dc.relation.sponsorship.fl_str_mv |
2075167498588264571 |
dc.rights.driver.fl_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Estadual do Oeste do Paraná Toledo |
dc.publisher.program.fl_str_mv |
Programa de Pós-Graduação em Engenharia Química |
dc.publisher.initials.fl_str_mv |
UNIOESTE |
dc.publisher.country.fl_str_mv |
Brasil |
dc.publisher.department.fl_str_mv |
Centro de Engenharias e Ciências Exatas |
publisher.none.fl_str_mv |
Universidade Estadual do Oeste do Paraná Toledo |
dc.source.none.fl_str_mv |
reponame:Biblioteca Digital de Teses e Dissertações do UNIOESTE instname:Universidade Estadual do Oeste do Paraná (UNIOESTE) instacron:UNIOESTE |
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Universidade Estadual do Oeste do Paraná (UNIOESTE) |
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UNIOESTE |
institution |
UNIOESTE |
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Biblioteca Digital de Teses e Dissertações do UNIOESTE |
collection |
Biblioteca Digital de Teses e Dissertações do UNIOESTE |
bitstream.url.fl_str_mv |
http://tede.unioeste.br:8080/tede/bitstream/tede/7194/2/William_Colombo_2023.pdf http://tede.unioeste.br:8080/tede/bitstream/tede/7194/1/license.txt |
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repository.name.fl_str_mv |
Biblioteca Digital de Teses e Dissertações do UNIOESTE - Universidade Estadual do Oeste do Paraná (UNIOESTE) |
repository.mail.fl_str_mv |
biblioteca.repositorio@unioeste.br |
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1811723484965896192 |