Speed prediction of a pipeline inspection gauge (PIG) based on differential pressure and acceleration with artificial neural networks

A device known as pipeline inspection gauge (PIG) runs through oil and gas pipelines performing various maintenance operations in the oil and gas industry. The PIG’s velocity, which plays a role in the efficiency of these operations, is usually obtained indirectly from odometers installed in it. A...

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Bibliografski detalji
Glavni autor: Freitas, Victor Carvalho Galvão de
Daljnji autori: Salazar, Andrés Ortiz
Format: doctoralThesis
Jezik:pt_BR
Izdano: Universidade Federal do Rio Grande do Norte
Teme:
Pipeline Inspection Gauge (PIG)
Artificial neural networks
Embedded systems
Raspberry Pi
CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
Online pristup:https://repositorio.ufrn.br/handle/123456789/49553
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id ri-123456789-49553
record_format dspace
institution Repositório Institucional
collection RI - UFRN
language pt_BR
topic Pipeline Inspection Gauge (PIG)
Artificial neural networks
Embedded systems
Raspberry Pi
CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
spellingShingle Pipeline Inspection Gauge (PIG)
Artificial neural networks
Embedded systems
Raspberry Pi
CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
Freitas, Victor Carvalho Galvão de
Speed prediction of a pipeline inspection gauge (PIG) based on differential pressure and acceleration with artificial neural networks
description A device known as pipeline inspection gauge (PIG) runs through oil and gas pipelines performing various maintenance operations in the oil and gas industry. The PIG’s velocity, which plays a role in the efficiency of these operations, is usually obtained indirectly from odometers installed in it. Although this is a relatively simple technique, the loss of contact between the odometer wheel and the pipeline results in measurement errors. To help reduce these errors, this work employed neural networks to estimate the speed of a prototype PIG, using the pressure difference that acts on the device inside the pipeline and its acceleration instead of using odometers; we built static networks (e.g., multilayer perceptron) and recurrent networks (e.g., long short-term memory). We developed the prototype PIG with an embedded system based on Raspberry Pi 3 to collect speed, acceleration, and pressure data for the model training. The Python library TensorFlow was used to implement supervised neural networks. To train and evaluate the models, we used the PIG testing pipeline available at the Petroleum Evaluation and Measurement Laboratory of the Federal University of Rio Grande do Norte (LAMP/UFRN). Our results show that the models were able to learn the relationship between the differential pressure, acceleration and speed of the PIG. The proposed approach can complement the odometer-based systems, thereby increasing the reliability of speed measurement.
author2 Salazar, Andrés Ortiz
author_facet Salazar, Andrés Ortiz
Freitas, Victor Carvalho Galvão de
format doctoralThesis
author Freitas, Victor Carvalho Galvão de
author_sort Freitas, Victor Carvalho Galvão de
title Speed prediction of a pipeline inspection gauge (PIG) based on differential pressure and acceleration with artificial neural networks
title_short Speed prediction of a pipeline inspection gauge (PIG) based on differential pressure and acceleration with artificial neural networks
title_full Speed prediction of a pipeline inspection gauge (PIG) based on differential pressure and acceleration with artificial neural networks
title_fullStr Speed prediction of a pipeline inspection gauge (PIG) based on differential pressure and acceleration with artificial neural networks
title_full_unstemmed Speed prediction of a pipeline inspection gauge (PIG) based on differential pressure and acceleration with artificial neural networks
title_sort speed prediction of a pipeline inspection gauge (pig) based on differential pressure and acceleration with artificial neural networks
publisher Universidade Federal do Rio Grande do Norte
publishDate 2022
url https://repositorio.ufrn.br/handle/123456789/49553
work_keys_str_mv AT freitasvictorcarvalhogalvaode speedpredictionofapipelineinspectiongaugepigbasedondifferentialpressureandaccelerationwithartificialneuralnetworks
_version_ 1773962666501472256
spelling ri-123456789-495532022-10-11T20:33:33Z Speed prediction of a pipeline inspection gauge (PIG) based on differential pressure and acceleration with artificial neural networks Freitas, Victor Carvalho Galvão de Salazar, Andrés Ortiz http://lattes.cnpq.br/7762013122037641 https://orcid.org/0000-0001-5650-3668 http://lattes.cnpq.br/7865065553087432 Doria Neto, Adrião Duarte https://orcid.org/0000-0002-5445-7327 http://lattes.cnpq.br/1987295209521433 Maitelli, André Laurindo Lima, Gustavo Fernandes de Villanueva, Juan Moisés Mauricio Pipeline Inspection Gauge (PIG) Artificial neural networks Embedded systems Raspberry Pi CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA A device known as pipeline inspection gauge (PIG) runs through oil and gas pipelines performing various maintenance operations in the oil and gas industry. The PIG’s velocity, which plays a role in the efficiency of these operations, is usually obtained indirectly from odometers installed in it. Although this is a relatively simple technique, the loss of contact between the odometer wheel and the pipeline results in measurement errors. To help reduce these errors, this work employed neural networks to estimate the speed of a prototype PIG, using the pressure difference that acts on the device inside the pipeline and its acceleration instead of using odometers; we built static networks (e.g., multilayer perceptron) and recurrent networks (e.g., long short-term memory). We developed the prototype PIG with an embedded system based on Raspberry Pi 3 to collect speed, acceleration, and pressure data for the model training. The Python library TensorFlow was used to implement supervised neural networks. To train and evaluate the models, we used the PIG testing pipeline available at the Petroleum Evaluation and Measurement Laboratory of the Federal University of Rio Grande do Norte (LAMP/UFRN). Our results show that the models were able to learn the relationship between the differential pressure, acceleration and speed of the PIG. The proposed approach can complement the odometer-based systems, thereby increasing the reliability of speed measurement. Um dispositivo conhecido como pipeline inspection gauge (PIG) percorre oleodutos e gasodutos realizando diversas operações de manutenção na indústria de petróleo e gás. A velocidade do PIG, que desempenha um papel importante na eficiência dessas operações, geralmente é obtida indiretamente a partir dos hodômetros nele instalados. Embora esta seja uma técnica relativamente simples, a perda de contato entre a roda do hodômetro e a tubulação resulta em erros de medição. Para ajudar a reduzir esses erros, este trabalho empregou redes neurais para estimar a velocidade de um PIG protótipo, utilizando a diferença de pressão que atua no dispositivo dentro dos dutos e sua aceleração ao invés de utilizar hodômetros; construímos redes neurais estáticas (por exemplo, multilayer perceptron) e redes recorrentes (por exemplo, long short-term memory). Desenvolvemos o PIG protótipo com um sistema embarcado baseado em Raspberry Pi 3 para coletar dados de velocidade, aceleração e pressão para o treinamento do modelo. A biblioteca Python TensorFlow foi usada para implementar redes neurais supervisionadas. Para treinar e avaliar os modelos, utilizamos o duto de testes de PIGs disponível no Laboratório de Avaliação e Medição em Petróleo da Universidade Federal do Rio Grande do Norte (LAMP/UFRN). Nossos resultados mostram que os modelos foram capazes de aprender a relação entre a pressão diferencial, a aceleração e a velocidade do PIG. A abordagem proposta pode complementar os sistemas baseados em hodômetros, aumentando assim a confiabilidade da medição de velocidade. 2022-10-11T20:32:56Z 2022-10-11T20:32:56Z 2022-07-28 doctoralThesis FREITAS, Victor Carvalho Galvão de. Speed prediction of a pipeline inspection gauge (PIG) based on differential pressure and acceleration with artificial neural networks. Orientador: Andrés Ortiz Salazar. 2022. 87f. Tese (Doutorado em Engenharia Elétrica e de Computação) - Centro de Tecnologia, Universidade Federal do Rio Grande do Norte, Natal, 2022. https://repositorio.ufrn.br/handle/123456789/49553 pt_BR Acesso Aberto application/pdf Universidade Federal do Rio Grande do Norte Brasil UFRN PROGRAMA DE PÓS-GRADUAÇÃO EM ENGENHARIA ELÉTRICA E DE COMPUTAÇÃO

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