Publication detail

Deeppipe: An intelligent monitoring framework for operating condition of multi-product pipelines

Wang, C., Zheng, J., Liang, Y., Wang, B., Klemeš, J.J., Zhu, Z., Liao, Q.

Original Title

Deeppipe: An intelligent monitoring framework for operating condition of multi-product pipelines

Type

journal article in Web of Science

Language

English

Original Abstract

The operation monitoring of multi-product pipelines helps to grasp the operation dynamics, detect abnormal situations in time, and assist on-site operation management. However, due to the complexity of the scheduling plan, the operating conditions of pipelines change frequently, which makes it difficult to accurately recognise condition types. To solve the above problem, an intelligent monitoring framework for operating conditions is proposed to simultaneously achieve the system recognition of steady, unsteady, and abnormal conditions. (i) The proposed monitoring framework extracts temporal and spatial characteristics of condition samples through four modules: Modules 1 and 2 form an unsupervised model for monitoring state changes and capturing temporal characteristics of condition samples; Module 3 is utilised to capture the spatial characteristics; the fusion layer based on Module 4 is applied to nonlinearly fit the spatiotemporal characteristics, and while monitoring the status changes of condition, it can also accurately recognise whether the condition is normal operation adjustment or abnormal condition. (ii) Taking a simulated pipeline and a real pipeline as examples, the effectiveness of the proposed monitoring framework is verified, and the accuracy, precision, recall, and F1 score of the recognition results reach 98.56%, 98.56%, 97.68%, and 98.12%. (iii) Through the sensitivity analysis of each module, accuracy, precision, recall, and F1 score are reduced to 96.10%, 96.10%, 95.83%, and 96.83% (i.e., only 2.46%, 2.46%, 1.85%, 1.29% differences) without Module I, which proves that the framework has strong robustness and generalisation. (iv) Finally, an intelligent analysis and control system of multi-product pipelines is designed for future applications. Consequently, the proposed intelligent monitoring framework can guide the safe operation and management of multi-product pipelines on-site.

Keywords

Condition recognition; Condition status change; Intelligent monitoring framework; Pipeline system safety

Authors

Wang, C., Zheng, J., Liang, Y., Wang, B., Klemeš, J.J., Zhu, Z., Liao, Q.

Released

15. 12. 2022

Publisher

Elsevier Ltd

ISBN

0360-5442

Periodical

Energy

Number

261

State

United Kingdom of Great Britain and Northern Ireland

Pages count

11

URL

https://www-sciencedirect-com.ezproxy.lib.vutbr.cz/science/article/pii/S0360544222022095

BibTex

@article{BUT179239,
  author="Bohong {Wang} and Jiří {Klemeš}",
  title="Deeppipe: An intelligent monitoring framework for operating condition of multi-product pipelines",
  journal="Energy",
  year="2022",
  number="261",
  pages="11",
  doi="10.1016/j.energy.2022.125325",
  issn="0360-5442",
  url="https://www-sciencedirect-com.ezproxy.lib.vutbr.cz/science/article/pii/S0360544222022095"
}