IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v284y2023ics0360544223027664.html
   My bibliography  Save this article

Exergy-related process monitoring for hot strip mill process based on improved support tensor data description

Author

Listed:
  • Zhang, Chuanfang
  • Peng, Kaixiang
  • Dong, Jie
  • Zhang, Xueyi
  • Yang, Kaixuan

Abstract

Process monitoring is important for ensuring industrial production safety. If faults are detected in time, maintenance plan will be made to avoid economic losses. Traditional process monitoring methods pay more attention to the utilization of process data and ignore process mechanism. It is necessary to consider the energy flow and the spatial information of different production equipments. As the unity of quality and quantity of energy, exergy contains the performance change information of the process and can be used as another way of achieving the required dimensionality reduction. Moreover, the introduction of spatial information will lead to the increase of data dimension. Support vector data description (SVDD) are oriented to vector data and cannot deal with tensor data directly. To handle above issues, a novel exergy-related process monitoring method based on improved support tensor data description (ISTDD) is proposed in this paper. First, exergy efficiency are calculated and exergy-related process variables are obtained by the minimal redundancy maximal relevance (mRMR). Second, a third-order tensor is constructed with spatial information. Then, the exergy-related monitoring model and its robust version are developed. Finally, case study on a hot strip mill process (HSMP) is given to illustrate the effectiveness of proposed method.

Suggested Citation

  • Zhang, Chuanfang & Peng, Kaixiang & Dong, Jie & Zhang, Xueyi & Yang, Kaixuan, 2023. "Exergy-related process monitoring for hot strip mill process based on improved support tensor data description," Energy, Elsevier, vol. 284(C).
  • Handle: RePEc:eee:energy:v:284:y:2023:i:c:s0360544223027664
    DOI: 10.1016/j.energy.2023.129372
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223027664
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2023.129372?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Osuolale, Funmilayo N. & Zhang, Jie, 2016. "Energy efficiency optimisation for distillation column using artificial neural network models," Energy, Elsevier, vol. 106(C), pages 562-578.
    2. Park, Yeseul & Choi, Minsung & Choi, Gyungmin, 2022. "Fault detection of industrial large-scale gas turbine for fuel distribution characteristics in start-up procedure using artificial neural network method," Energy, Elsevier, vol. 251(C).
    Full references (including those not matched with items on IDEAS)

    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.
    1. Vlad Mureșan & Mihaela-Ligia Ungureșan & Mihail Abrudean & Honoriu Vălean & Iulia Clitan & Roxana Motorga & Emilian Ceuca & Marius Fișcă, 2021. "AI versus Classic Methods in Modelling Isotopic Separation Processes: Efficiency Comparison," Mathematics, MDPI, vol. 9(23), pages 1-31, November.
    2. Kazemi, Abolghasem & Mehrabani-Zeinabad, Arjomand & Beheshti, Masoud, 2018. "Recently developed heat pump assisted distillation configurations: A comparative study," Applied Energy, Elsevier, vol. 211(C), pages 1261-1281.
    3. Li, Jiangkuan & Lin, Meng & Wang, Bo & Tian, Ruifeng & Tan, Sichao & Li, Yankai & Chen, Junjie, 2024. "Open set recognition fault diagnosis framework based on convolutional prototype learning network for nuclear power plants," Energy, Elsevier, vol. 290(C).
    4. Đozić, Damir J. & Gvozdenac Urošević, Branka D., 2019. "Application of artificial neural networks for testing long-term energy policy targets," Energy, Elsevier, vol. 174(C), pages 488-496.
    5. Wenxiang Zhou & Sangwei Lu & Wenjie Kai & Jichang Wu & Chenyang Zhang & Feng Lu, 2023. "A Novel Adaptive Generation Method for Initial Guess Values of Component-Level Aero-Engine Start-Up Models," Sustainability, MDPI, vol. 15(4), pages 1-25, February.
    6. Gong, Linjuan & Hou, Guolian & Li, Jun & Gao, Haidong & Gao, Lin & Wang, Lin & Gao, Yaokui & Zhou, Junbo & Wang, Mingkun, 2023. "Intelligent fuzzy modeling of heavy-duty gas turbine for smart power generation," Energy, Elsevier, vol. 277(C).
    7. Mochen Liao & Kai Lan & Yuan Yao, 2022. "Sustainability implications of artificial intelligence in the chemical industry: A conceptual framework," Journal of Industrial Ecology, Yale University, vol. 26(1), pages 164-182, February.
    8. Máša, Vítězslav & Stehlík, Petr & Touš, Michal & Vondra, Marek, 2018. "Key pillars of successful energy saving projects in small and medium industrial enterprises," Energy, Elsevier, vol. 158(C), pages 293-304.
    9. Kazemi, Abolghasem & Mehrabani-Zeinabad, Arjomand & Beheshti, Masoud, 2017. "Development of a novel processing system for efficient sour water stripping," Energy, Elsevier, vol. 125(C), pages 449-458.
    10. Chen, Yu-Zhi & Tsoutsanis, Elias & Wang, Chen & Gou, Lin-Feng, 2023. "A time-series turbofan engine successive fault diagnosis under both steady-state and dynamic conditions," Energy, Elsevier, vol. 263(PD).
    11. Lin, Meng & Li, Jiangkuan & Li, Yankai & Wang, Xu & Jin, Chengyi & Chen, Junjie, 2023. "Generalization analysis and improvement of CNN-based nuclear power plant fault diagnosis model under varying power levels," Energy, Elsevier, vol. 282(C).

    Corrections

    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:284:y:2023:i:c:s0360544223027664. 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.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.