Soft sensor development for mixed oil interface tracking in multi-product pipelines based on knowledge-informed semi-supervised Variational Bayesian Gaussian mixture regression
Author
Abstract
Suggested Citation
DOI: 10.1016/j.energy.2024.131516
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Yin, Xiong & Wen, Kai & Huang, Weihe & Luo, Yinwei & Ding, Yi & Gong, Jing & Gao, Jianfeng & Hong, Bingyuan, 2023. "A high-accuracy online transient simulation framework of natural gas pipeline network by integrating physics-based and data-driven methods," Applied Energy, Elsevier, vol. 333(C).
- 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).
- Wang, Shuhui & Wang, Zhenpo & Cheng, Ximing & Zhang, Zhaosheng, 2023. "A double-layer fault diagnosis strategy for electric vehicle batteries based on Gaussian mixture model," Energy, Elsevier, vol. 281(C).
- Wu, Shengyang & Ding, Zhaohao & Wang, Jingyu & Shi, Dongyuan, 2023. "Unveiling bidding uncertainties in electricity markets: A Bayesian deep learning framework based on accurate variational inference," Energy, Elsevier, vol. 276(C).
- Yuan, Ziyun & Chen, Lei & Liu, Gang & Zhang, Yuhan, 2023. "Knowledge-informed Variational Bayesian Gaussian mixture regression model for predicting mixed oil length," Energy, Elsevier, vol. 285(C).
- 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).
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- 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).
- Koo, Bonchan & Chang, Seungjoon & Kwon, Hweeung, 2023. "Digital twin for natural gas infrastructure operation and management via streaming dynamic mode decomposition with control," Energy, Elsevier, vol. 274(C).
- Yuan, Ziyun & Chen, Lei & Liu, Gang & Zhang, Yuhan, 2023. "Knowledge-informed Variational Bayesian Gaussian mixture regression model for predicting mixed oil length," Energy, Elsevier, vol. 285(C).
- Liu, Qiquan & Ma, Jian & Zhao, Xuan & Zhang, Kai & Xiangli, Kang & Meng, Dean, 2024. "A novel method for fault diagnosis and type identification of cell voltage inconsistency in electric vehicles using weighted Euclidean distance evaluation and statistical analysis," Energy, Elsevier, vol. 293(C).
- 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).
More about this item
Keywords
Multi-product pipeline; Mixed oil; Gaussian mixture regression; Soft sensor; Knowledge-data;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:300:y:2024:i:c:s0360544224012891. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.