Deeppipe: A hybrid model for multi-product pipeline condition recognition based on process and data coupling

Author:

Wang Chang,Zheng JianqinORCID,Liang Yongtu,Li Miao,Chen Wencai,Liao Qi,Zhang Haoran

Funder

China University of Petroleum Beijing

Publisher

Elsevier BV

Subject

Computer Science Applications,General Chemical Engineering

Reference60 articles.

Cited by 9 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Maximum pitting corrosion depth prediction of buried pipeline based on theory-guided machine learning;International Journal of Pressure Vessels and Piping;2024-08

2. How government policies promote transportation utilization in the national-level hydrogen supply chain: A case of China;Energy for Sustainable Development;2024-06

3. Intelligent Methods for Pipeline Operation and Integrity;Journal of Pipeline Systems Engineering and Practice;2024-02

4. Leveraging Machine Learning for Pipeline Condition Assessment;Journal of Pipeline Systems Engineering and Practice;2023-08

5. Deeppipe: A hybrid intelligent framework for real-time batch tracking of multi-product pipelines;Chemical Engineering Research and Design;2023-03

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