Publication detail

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

Ma, Y., Zheng, J., Liang, Y., Klemeš, J.J., Du, J., Liao, Q., Lu, H., Wang, B.

Original Title

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

Type

journal article in Web of Science

Language

English

Original Abstract

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.

Keywords

Burst pressure prediction; Corroded pipeline; Neural network; Oil and gas; Theory-guided

Authors

Ma, Y., Zheng, J., Liang, Y., Klemeš, J.J., Du, J., Liao, Q., Lu, H., Wang, B.

Released

1. 6. 2022

Publisher

Institution of Chemical Engineers

ISBN

0957-5820

Periodical

Process Safety and Environmental Protection

Number

162

State

United Kingdom of Great Britain and Northern Ireland

Pages from

595

Pages to

609

Pages count

15

URL

https://www-sciencedirect-com.ezproxy.lib.vutbr.cz/science/article/pii/S0957582022003536

BibTex

@article{BUT178076,
  author="Jiří {Klemeš} and Bohong {Wang}",
  title="Deeppipe: Theory-guided neural network method for predicting burst pressure of corroded pipelines",
  journal="Process Safety and Environmental Protection",
  year="2022",
  number="162",
  pages="595--609",
  doi="10.1016/j.psep.2022.04.036",
  issn="0957-5820",
  url="https://www-sciencedirect-com.ezproxy.lib.vutbr.cz/science/article/pii/S0957582022003536"
}