IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v263y2023ipas036054422202480x.html
   My bibliography  Save this article

Prediction method of key corrosion state parameters in refining process based on multi-source data

Author

Listed:
  • Yang, Jianfeng
  • Suo, Guanyu
  • Chen, Liangchao
  • Dou, Zhan
  • Hu, Yuanhao

Abstract

Corrosion problems have threatened long-term safe and stable operation of refining units. At present, refining enterprises mainly use corrosion monitoring and detection to identify equipment corrosion states, which has the shortcomings of narrow identification scope and high cost. The data-driven method avoids these problems, and has the advantage of efficiently predicting corrosion states to support corrosion management decisions. This paper, based on multi-source data, proposes a method focusing on the prediction about key corrosion parameters and establishes prediction models on critical parts of refining unit. Firstly, the application of demand-oriented corrosion prediction method is proposed. Then, according to the process operation parameters and medium analysis data of atmospheric tower overhead circuit, the regression prediction models based on RF are established. Meanwhile, after outlier detection by iForest, the model's parameters are optimized by SOS. In the limited real data, the optimized model achieves the best prediction with RMSE of 0.00611, MAE of 0.00513, and R2 of 0.918, and realizes the mining of corrosion parameter sensitivity. Finally, a variety of models are compared. The prediction method proposes in this paper have generalization performance, which can serve as an instruction for equipment safety management and hidden dangers identification.

Suggested Citation

  • Yang, Jianfeng & Suo, Guanyu & Chen, Liangchao & Dou, Zhan & Hu, Yuanhao, 2023. "Prediction method of key corrosion state parameters in refining process based on multi-source data," Energy, Elsevier, vol. 263(PA).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pa:s036054422202480x
    DOI: 10.1016/j.energy.2022.125594
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054422202480X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.125594?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. Tran, Duc-Hoc & Luong, Duc-Long & Chou, Jui-Sheng, 2020. "Nature-inspired metaheuristic ensemble model for forecasting energy consumption in residential buildings," Energy, Elsevier, vol. 191(C).
    2. Benali, L. & Notton, G. & Fouilloy, A. & Voyant, C. & Dizene, R., 2019. "Solar radiation forecasting using artificial neural network and random forest methods: Application to normal beam, horizontal diffuse and global components," Renewable Energy, Elsevier, vol. 132(C), pages 871-884.
    3. Bienvenido-Huertas, David & Rubio-Bellido, Carlos & Solís-Guzmán, Jaime & Oliveira, Miguel José, 2020. "Experimental characterisation of the periodic thermal properties of walls using artificial intelligence," Energy, Elsevier, vol. 203(C).
    4. Rocabruno-Valdés, C.I. & González-Rodriguez, J.G. & Díaz-Blanco, Y. & Juantorena, A.U. & Muñoz-Ledo, J.A. & El-Hamzaoui, Y. & Hernández, J.A., 2019. "Corrosion rate prediction for metals in biodiesel using artificial neural networks," Renewable Energy, Elsevier, vol. 140(C), pages 592-601.
    5. Lv, Ya-jun & Wang, Jun-wei & Wang, Julian & Xiong, Cheng & Zou, Liang & Li, Ly & Li, Da-wang, 2020. "Steel corrosion prediction based on support vector machines," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
    6. Xiaoxu Chen & Linyuan Wang & Zhiyu Huang, 2020. "Principal Component Analysis Based Dynamic Fuzzy Neural Network for Internal Corrosion Rate Prediction of Gas Pipelines," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-9, September.
    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. Yamashiro, Hirochika & Nonaka, Hirofumi, 2021. "Estimation of processing time using machine learning and real factory data for optimization of parallel machine scheduling problem," Operations Research Perspectives, Elsevier, vol. 8(C).
    2. Makade, Rahul G. & Chakrabarti, Siddharth & Jamil, Basharat & Sakhale, C.N., 2020. "Estimation of global solar radiation for the tropical wet climatic region of India: A theory of experimentation approach," Renewable Energy, Elsevier, vol. 146(C), pages 2044-2059.
    3. Amal A. Al-Shargabi & Abdulbasit Almhafdy & Dina M. Ibrahim & Manal Alghieth & Francisco Chiclana, 2021. "Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics," Sustainability, MDPI, vol. 13(22), pages 1-20, November.
    4. Liu, Yanfeng & Zhou, Yong & Chen, Yaowen & Wang, Dengjia & Wang, Yingying & Zhu, Ying, 2020. "Comparison of support vector machine and copula-based nonlinear quantile regression for estimating the daily diffuse solar radiation: A case study in China," Renewable Energy, Elsevier, vol. 146(C), pages 1101-1112.
    5. Kugelmeier, Cristie Luis & Monteiro, Marcos Roberto & da Silva, Rodrigo & Kuri, Sebastião Elias & Sordi, Vitor Luiz & Della Rovere, Carlos Alberto, 2021. "Corrosion behavior of carbon steel, stainless steel, aluminum and copper upon exposure to biodiesel blended with petrodiesel," Energy, Elsevier, vol. 226(C).
    6. David Bienvenido-Huertas, 2020. "Assessing the Environmental Impact of Thermal Transmittance Tests Performed in Façades of Existing Buildings: The Case of Spain," Sustainability, MDPI, vol. 12(15), pages 1-18, August.
    7. Minseok Jang & Hyun Cheol Jeong & Taegon Kim & Dong Hee Suh & Sung-Kwan Joo, 2021. "Empirical Analysis of the Impact of COVID-19 Social Distancing on Residential Electricity Consumption Based on Demographic Characteristics and Load Shape," Energies, MDPI, vol. 14(22), pages 1-15, November.
    8. Ye, Wenlian & Wang, Xiaojun & Liu, Yingwen, 2020. "Application of artificial neural network for predicting the dynamic performance of a free piston Stirling engine," Energy, Elsevier, vol. 194(C).
    9. Imani, Maryam, 2021. "Electrical load-temperature CNN for residential load forecasting," Energy, Elsevier, vol. 227(C).
    10. Ben Ammar, Rim & Ben Ammar, Mohsen & Oualha, Abdelmajid, 2020. "Photovoltaic power forecast using empirical models and artificial intelligence approaches for water pumping systems," Renewable Energy, Elsevier, vol. 153(C), pages 1016-1028.
    11. Ghimire, Sujan & Deo, Ravinesh C. & Casillas-Pérez, David & Salcedo-Sanz, Sancho, 2022. "Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Deep Residual model for short-term multi-step solar radiation prediction," Renewable Energy, Elsevier, vol. 190(C), pages 408-424.
    12. Hasna Hissou & Said Benkirane & Azidine Guezzaz & Mourade Azrour & Abderrahim Beni-Hssane, 2023. "A Novel Machine Learning Approach for Solar Radiation Estimation," Sustainability, MDPI, vol. 15(13), pages 1-21, July.
    13. Nishant Raj Kapoor & Ashok Kumar & Tabish Alam & Anuj Kumar & Kishor S. Kulkarni & Paolo Blecich, 2021. "A Review on Indoor Environment Quality of Indian School Classrooms," Sustainability, MDPI, vol. 13(21), pages 1-43, October.
    14. Gonzalo Astray & Benedicto Soto & Enrique Barreiro & Juan F. Gálvez & Juan C. Mejuto, 2021. "Machine Learning Applied to the Oxygen-18 Isotopic Composition, Salinity and Temperature/Potential Temperature in the Mediterranean Sea," Mathematics, MDPI, vol. 9(19), pages 1-15, October.
    15. Narvaez, Gabriel & Giraldo, Luis Felipe & Bressan, Michael & Pantoja, Andres, 2021. "Machine learning for site-adaptation and solar radiation forecasting," Renewable Energy, Elsevier, vol. 167(C), pages 333-342.
    16. Pachauri, Nikhil & Ahn, Chang Wook, 2023. "Weighted aggregated ensemble model for energy demand management of buildings," Energy, Elsevier, vol. 263(PC).
    17. Ochuko Kelvin Overen & Edson Leroy Meyer, 2022. "Solar Energy Resources and Photovoltaic Power Potential of an Underutilised Region: A Case of Alice, South Africa," Energies, MDPI, vol. 15(13), pages 1-29, June.
    18. Zhaoyi Cui & Xiuli Geng, 2021. "Product Service System Configuration Based on a PCA-QPSO-SVM Model," Sustainability, MDPI, vol. 13(16), pages 1-22, August.
    19. Dong, Yan & Han, Han & Wang, Fuqiang & Zhang, Yingjie & Cheng, Ziming & Shi, Xuhang & Yan, Yuying, 2022. "A low-cost sustainable coating: Improving passive daytime radiative cooling performance using the spectral band complementarity method," Renewable Energy, Elsevier, vol. 192(C), pages 606-616.
    20. Dengchang Ma & Guobing Pan & Fang Xu & Hongfei Sun, 2021. "Quantitative Analysis of the Impact of Meteorological Environment on Photovoltaic System Feasibility," Energies, MDPI, vol. 14(10), pages 1-16, May.

    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:263:y:2023:i:pa:s036054422202480x. 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.