Application of artificial intelligence technologies in intelligent diagnosis of crude oil pipelines

Jiangqiao Lian; Lu Zhao; Zhen Li; Rui Xie

doi:10.1117/12.2639551

19 May 2022 Application of artificial intelligence technologies in intelligent diagnosis of crude oil pipelines

Jiangqiao Lian, Lu Zhao, Zhen Li, Rui Xie

Proceedings Volume 12250, International Symposium on Computer Applications and Information Systems (ISCAIS 2022); 1225004 (2022) https://doi.org/10.1117/12.2639551
Event: International Symposium on Computer Applications and Information Systems (ISCAIS2022), 2022, Shenzhen, China

Abstract

With the continuous development of diagnosis, prediction and systems management technologies for complex pipeline systems and driven by the context of Industry 4.0, digital twin technology has become a research hotspot in the field of smart manufacturing and intelligent operation and maintenance of pipeline systems. Digital twin can be defined as an adaptive model of complex physical systems. The prospect of digital twin and its impact on society is brought closer to reality with recent advances in multiphysics simulation, artificial intelligence, big data cybernetics, data processing and management tools. With a case study on Sino-Myanmar pipelines, the author uses artificial intelligence models on the basis of digital twin technology to predict the pipeline transmission demand to make sure the smooth operation of pipelines with large topographic fluctuations and geological complexity. The objective is to achieve intelligent operation such as real-time autonomous optimization of pipeline operation and control of safety warning, thus realizing data analysis and intelligent diagnosis, as well as providing a reference for the application of demand prediction of complex pipeline networks and system health management in digital twin technology.

Citation Download Citation

Jiangqiao Lian, Lu Zhao, Zhen Li, and Rui Xie "Application of artificial intelligence technologies in intelligent diagnosis of crude oil pipelines", Proc. SPIE 12250, International Symposium on Computer Applications and Information Systems (ISCAIS 2022), 1225004 (19 May 2022); https://doi.org/10.1117/12.2639551

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KEYWORDS

Data modeling

Artificial intelligence

Error analysis

Mathematical modeling

Operational intelligence

Principal component analysis

Systems modeling

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