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Deeppipe: An intelligent framework for predicting mixed oil concentration in multi-product pipeline

Author

Listed:
  • Du, Jian
  • Zheng, Jianqin
  • Liang, Yongtu
  • Xia, Yuheng
  • Wang, Bohong
  • Shao, Qi
  • Liao, Qi
  • Tu, Renfu
  • Xu, Bin
  • Xu, Ning

Abstract

Accurately predicting mixed oil concentration distribution exerts a core effect on the optimization of pipelines and the quality of oil. Due to the neglect of mechanism features, high-dimensional complex feature correlations, and insufficient feature information on small batch data, the current methods cannot predict mixed oil concentration accurately. This work proposes a hybrid intelligent framework to provide an accurate and effective monitoring tool for mixed oil concentration of multi-product pipelines. In the proposed framework, the development mechanism of mixed oil is analyzed to select and reconstruct holistic features to explore the influencing mechanism of mixed oil concentration. Then, a parameterization and nonlinear transformation module is designed to acquire the accurate and concise representation of mixed oil concentration, thus decreasing the complexity of feature space and promoting the approximating ability of the prediction model. Eventually, a novel virtual samples generation module is established to obtain high-quality samples of mixed oil concentration, aiming to extract more comprehensive correlations of feature variables and improve the prediction performance. Cases from real-world multi-product pipelines suggest more accurate prediction results of mixed oil concentration compared to other advanced methods, with RMSE and R2 being 0.0500 and 0.9688. Furthermore, it is also proved that acquiring more holistic and accurate feature variables of mixed oil development and fully exploring comprehensive correlations between feature variables are crucial for the performance enhancement of the prediction model.

Suggested Citation

  • Du, Jian & Zheng, Jianqin & Liang, Yongtu & Xia, Yuheng & Wang, Bohong & Shao, Qi & Liao, Qi & Tu, Renfu & Xu, Bin & Xu, Ning, 2023. "Deeppipe: An intelligent framework for predicting mixed oil concentration in multi-product pipeline," Energy, Elsevier, vol. 282(C).
    • Handle: RePEc:eee:energy:v:282:y:2023:i:c:s0360544223022041
    DOI: 10.1016/j.energy.2023.128810
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    References listed on IDEAS

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    1. Daming Li & Zhu Zhen & Hongqiang Zhang & Yanqing Li & Xingchen Tang, 2019. "Numerical Model of Oil Film Diffusion in Water Based on SPH Method," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-14, November.
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    4. Du, Jian & Zheng, Jianqin & Liang, Yongtu & Lu, Xinyi & Klemeš, Jiří Jaromír & Varbanov, Petar Sabev & Shahzad, Khurram & Rashid, Muhammad Imtiaz & Ali, Arshid Mahmood & Liao, Qi & Wang, Bohong, 2022. "A hybrid deep learning framework for predicting daily natural gas consumption," Energy, Elsevier, vol. 257(C).
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    8. Du, Jian & Zheng, Jianqin & Liang, Yongtu & Xu, Ning & Klemeš, Jiří Jaromír & Wang, Bohong & Liao, Qi & Varbanov, Petar Sabev & Shahzad, Khurram & Ali, Arshid Mahmood, 2023. "Deeppipe: A two-stage physics-informed neural network for predicting mixed oil concentration distribution," Energy, Elsevier, vol. 276(C).
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    Cited by:

    1. Miao, Xingyuan & Zhao, Hong, 2024. "Corroded submarine pipeline degradation prediction based on theory-guided IMOSOA-EL model," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    2. Du, Jian & Zheng, Jianqin & Liang, Yongtu & Ma, Yunlu & Wang, Bohong & Liao, Qi & Xu, Ning & Ali, Arshid Mahmood & Rashid, Muhammad Imtiaz & Shahzad, Khurram, 2024. "A deep learning-based approach for predicting oil production: A case study in the United States," Energy, Elsevier, vol. 288(C).
    3. Li, Zhuochao & Guo, Yi & Wang, Bohong & Yan, Yamin & Liang, Yongtu & Mikulčić, Hrvoje, 2024. "Two-stage optimization model for scheduling multiproduct pipeline network with multi-source and multi-terminal," Energy, Elsevier, vol. 306(C).

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