Deeppipe: Theory-guided neural network method for predicting burst pressure of corroded pipelines

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F22%3APU144649" target="_blank" >RIV/00216305:26210/22:PU144649 - isvavai.cz</a>

Výsledek na webu

<a href="https://www-sciencedirect-com.ezproxy.lib.vutbr.cz/science/article/pii/S0957582022003536" target="_blank" >https://www-sciencedirect-com.ezproxy.lib.vutbr.cz/science/article/pii/S0957582022003536</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.psep.2022.04.036" target="_blank" >10.1016/j.psep.2022.04.036</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Deeppipe: Theory-guided neural network method for predicting burst pressure of corroded pipelines

Popis výsledku v původním jazyce

Crude oil and natural gas are the primary energy sources, mainly transported by pipelines. Pipeline safety has to be seriously considered to ensure the continuous and stable transportation of these two types of energy sources. The burst pressure is an important indicator of pipeline safety. Accurate prediction of the burst pressure is of great significance to the design, construction, daily operation, and maintenance of the pipeline. This paper proposes a theory-guided neural network model-based method to predict burst pressure prediction of corroded pipelines, which can incorporate physical principles into the deep learning framework. First, higher-order features with physical meaning are constructed and coupled with the original features to form a new feature space. Then the traditional burst pressure prediction formula Pipeline Corrosion Criterion (PCORRC) is integrated into the model to make full use of the prior knowledge contained in the empirical formula. The designed loss function enables the network to have different weights for different samples and focuses on learning the PCORRC formula to predict samples with large deviations. Finally, the model was verified using a public dataset based on experiments and finite element simulations. The results show that the theory-guided neural network model proposed in this paper has the highest accuracy compared with other models. The correlation coefficient is 0.9945, the root mean square error is 0.562, and the mean absolute percentage error is 2.65%. Further tests have shown that the model is very robust and has good adaptability to different data. This work presented that integrating domain knowledge into the traditional neural network model can effectively improve the performance of burst pressure prediction of the corroded pipeline.

Název v anglickém jazyce

Deeppipe: Theory-guided neural network method for predicting burst pressure of corroded pipelines

Popis výsledku anglicky

Crude oil and natural gas are the primary energy sources, mainly transported by pipelines. Pipeline safety has to be seriously considered to ensure the continuous and stable transportation of these two types of energy sources. The burst pressure is an important indicator of pipeline safety. Accurate prediction of the burst pressure is of great significance to the design, construction, daily operation, and maintenance of the pipeline. This paper proposes a theory-guided neural network model-based method to predict burst pressure prediction of corroded pipelines, which can incorporate physical principles into the deep learning framework. First, higher-order features with physical meaning are constructed and coupled with the original features to form a new feature space. Then the traditional burst pressure prediction formula Pipeline Corrosion Criterion (PCORRC) is integrated into the model to make full use of the prior knowledge contained in the empirical formula. The designed loss function enables the network to have different weights for different samples and focuses on learning the PCORRC formula to predict samples with large deviations. Finally, the model was verified using a public dataset based on experiments and finite element simulations. The results show that the theory-guided neural network model proposed in this paper has the highest accuracy compared with other models. The correlation coefficient is 0.9945, the root mean square error is 0.562, and the mean absolute percentage error is 2.65%. Further tests have shown that the model is very robust and has good adaptability to different data. This work presented that integrating domain knowledge into the traditional neural network model can effectively improve the performance of burst pressure prediction of the corroded pipeline.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20704 - Energy and fuels

Projekt

<a href="/cs/project/EF15_003%2F0000456" target="_blank" >EF15_003/0000456: Laboratoř integrace procesů pro trvalou udržitelnost</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Process Safety and Environmental Protection

ISSN

0957-5820

e-ISSN

1744-3598

Svazek periodika

neuveden

Číslo periodika v rámci svazku

162

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

15

Strana od-do

595-609

Kód UT WoS článku

000803861600004

EID výsledku v databázi Scopus

2-s2.0-85130098436