Detail publikace

Deeppipe: A two-stage physics-informed neural network for predicting mixed oil concentration distribution

Du, Jian Zheng, Jianqin Liang, Yongtu Xu, Ning Klemes, Jiri Jaromir Wang, Bohong Liao, Qi Varbanov, Petar Sabev Shahzad, Khurram Ali, Arshid Mahmood

Originální název

Deeppipe: A two-stage physics-informed neural network for predicting mixed oil concentration distribution

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

Owing to the oil diffusion, a mixed oil segment would inevitably form between two adjacent oil products, leading to economic loss and a reduction of oil product quality. Current works have inherent drawbacks, including computational inapplicability for long-distance pipelines by using numerical methods and unreasonable physical results by using conventional machine learning models. This work proposes a two-stage physics-informed neural network (TS-PINN) method, aiming to provide a highly efficient and precise predictive tool for the mixed oil concentration distribution of multi-product pipelines. In the TS-PINN, the scientific theory and engineering control knowledge of mixed oil diffusion are incorporated into the neural network, which allows the developed neural network model to be capable of exploring the potential physical information of mixed oil and constraining the training process. Subsequently, a two-stage modelling approach is proposed to improve the convergence effect and prediction accuracy of the proposed TS-PINN model. Results from numerical case studies suggest the higher accuracy and robustness achieved by the proposed model compared to the deep neural network, while the root mean square error and mean absolute percentage error gotten by TS-PINN are reduced by 79.5% and 80.5%. Further, the test results on sparse data prove that the TS-PINN achieves a reduction in dependency on available data when training the neural network. Compared with the numerical methods, the TS-PINN reduces the calculation time from several days to hundreds of seconds, it is practicable to predict the mixed oil migration in long-distance pipelines rapidly and accurately using the proposed model.

Klíčová slova

Mixed oil concentration prediction; Multi-product pipeline; Physics-informed neural network; Sequential transportation; Two-stage modelling approach

Autoři

Du, Jian; Zheng, Jianqin; Liang, Yongtu; Xu, Ning; Klemes, Jiri Jaromir; Wang, Bohong; Liao, Qi; Varbanov, Petar Sabev; Shahzad, Khurram; Ali, Arshid Mahmood

Vydáno

1. 8. 2023

Nakladatel

PERGAMON-ELSEVIER SCIENCE LTDTHE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND

Místo

PERGAMON-ELSEVIER SCIENCE LTDTHE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND

ISSN

0360-5442

Periodikum

Energy

Ročník

276

Číslo

1

Stát

Spojené království Velké Británie a Severního Irska

Strany počet

35

URL

https://www.sciencedirect.com/science/article/pii/S0360544223008460?via%3Dihub

BibTex

@article{BUT187469,
  author="Du, Jian and Zheng, Jianqin and Liang, Yongtu and Xu, Ning and Klemes, Jiri Jaromir and Wang, Bohong and Liao, Qi and Varbanov, Petar Sabev and Shahzad, Khurram and Ali, Arshid Mahmood",
  title="Deeppipe: A two-stage physics-informed neural network for predicting mixed oil concentration distribution",
  journal="Energy",
  year="2023",
  volume="276",
  number="1",
  pages="35",
  doi="10.1016/j.energy.2023.127452",
  issn="0360-5442",
  url="https://www.sciencedirect.com/science/article/pii/S0360544223008460?via%3Dihub"
}