A hybrid framework for forecasting power generation of multiple renewable energy sources

J Zheng, J Du, B Wang, JJ Klemeš, Q Liao… - … and Sustainable Energy …, 2023 - Elsevier
The accurate power generation forecast of multiple renewable energy sources is significant
for the power scheduling of renewable energy systems. However, previous studies focused …

Review on automated condition assessment of pipelines with machine learning

Y Liu, Y Bao - Advanced Engineering Informatics, 2022 - Elsevier
Pipelines carrying energy products play vital roles in economic wealth and public safety, but
incidents continue occurring. Condition assessment of pipelines is essential to identify …

A hybrid deep learning framework for predicting daily natural gas consumption

J Du, J Zheng, Y Liang, X Lu, JJ Klemeš, PS Varbanov… - Energy, 2022 - Elsevier
Conventional time-series prediction methods for natural gas consumption mainly focus on
capturing the temporal feature, neglecting static and dynamic information extraction. The …

Detection of faults in subsea pipelines by flow monitoring with regression supervised machine learning

D Eastvedt, G Naterer, X Duan - Process Safety and Environmental …, 2022 - Elsevier
This study investigates the relationship between pressure change, velocity change, and
temperature of crude oil through a pipeline and presents a method of using a regression …

[HTML][HTML] Review on the transport capacity management of oil and gas pipeline network: Challenges and opportunities of future pipeline transport

G Wang, Q Cheng, W Zhao, Q Liao, H Zhang - Energy Strategy Reviews, 2022 - Elsevier
Pipelines are the essential transportation infrastructure of the energy system, undertaking
most transportation tasks of oil and gas on land. In a critical period of the energy transition, it …

[HTML][HTML] Innovations of carbon-neutral petroleum pipeline: A review

Q Liao, Y Liang, R Tu, L Huang, J Zheng, G Wang… - Energy Reports, 2022 - Elsevier
As one of the major energy-intensive industries, petroleum pipelines are facing with huge
pressure from carbon-neutral policies. Conforming to green development, the traditional …

Deeppipe: A deep-learning method for anomaly detection of multi-product pipelines

J Zheng, C Wang, Y Liang, Q Liao, Z Li, B Wang - Energy, 2022 - Elsevier
The multi-product pipeline is the main way of refined oil transportation to ensure the safety of
the energy supply. Considering that abnormal conditions of the pipeline will cause huge …

A knowledge-enhanced graph-based temporal-spatial network for natural gas consumption prediction

J Du, J Zheng, Y Liang, B Wang, JJ Klemeš, X Lu, R Tu… - Energy, 2023 - Elsevier
The accurate prediction of natural gas consumption plays a central role in long-distance
pipeline system production and transportation planning, and it becomes even more …

Deep learning identifies leak in water pipeline system using transient frequency response

Z Liao, H Yan, Z Tang, X Chu, T Tao - Process Safety and Environmental …, 2021 - Elsevier
Pipeline leak identification method using transient frequency response (TFR) has been
researched in the past two decades. To extend this method to a more general water pipeline …

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

J Du, J Zheng, Y Liang, Y Xia, B Wang, Q Shao, Q Liao… - Energy, 2023 - Elsevier
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 …