CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 13403 > Article
Paper
18 November 2024 Research on PID control method for predictive modeling of oil pipeline infrastructure under Industrial Internet of Things driving
Hao Liu, Hongbing Dong, Bo Wang, Yang Xu, Zhongwei Cai, Xiang Li
Author Affiliations +
Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 1340338 (2024) https://doi.org/10.1117/12.3051848
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China
Abstract
The oil pipeline system faces numerous safety hazards and operational management challenges, such as leaks, blockages, and pressure fluctuations, which may lead to environmental pollution, economic losses, and even casualties. To address these issues, this study proposes a predictive PID control method for oil pipeline infrastructure based on cloud servers and Industrial Internet of Things (IIoT) technology, coupled with advanced data analysis and control algorithms, enabling comprehensive monitoring and management of oil pipeline systems. A risk assessment and control framework based on online model fusion is developed, integrating diverse data sources and optimization model algorithms to achieve intelligent monitoring and control of pipeline systems. Additionally, PID control algorithms are employed for real-time adjustment of flow rates and pressures in the pipeline system to ensure stable operation. Experimental results demonstrate that the proposed risk assessment and control framework effectively identifies anomalies in the pipeline system, while the PID control algorithm enables real-time adjustment of flow rates and pressures, enhancing the safety and stability of the pipeline system. Comparative analysis shows that the proposed control method offers a 28.96% improvement in feedback control effectiveness and can reduce the risk rate by up to 4.2 times through the predictive modeling method.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Hao Liu, Hongbing Dong, Bo Wang, Yang Xu, Zhongwei Cai, and Xiang Li "Research on PID control method for predictive modeling of oil pipeline infrastructure under Industrial Internet of Things driving", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 1340338 (18 November 2024); https://doi.org/10.1117/12.3051848
ACCESS THE FULL ARTICLE
ORGANIZATIONAL
Sign in with credentials provided by your organization.
INSTITUTIONAL
Select your institution to access the SPIE Digital Library.
SELECT YOUR INSTITUTION
PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
PERSONAL SIGN IN
No SPIE Account? Create one
PURCHASE THIS CONTENT
SUBSCRIBE TO DIGITAL LIBRARY
50 downloads per 1-year subscription
Members: $195
Non-members: $335 ADD TO CART
25 downloads per 1 - year subscription
Members: $145
Non-members: $250 ADD TO CART
PURCHASE SINGLE ARTICLE
Includes PDF, HTML & Video, when available
Members: $17.00
Non-members: $21.00 ADD TO CART
PROCEEDINGS
 11 PAGES
DOWNLOAD PAPER SAVE TO MY LIBRARY
GET CITATION
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Data modeling
Control systems
Clouds
Performance modeling
Sensors
Systems modeling
Transportation
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top