IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i7p1972-d529429.html
   My bibliography  Save this article

The Evaluation of the Corrosion Rates of Alloys Applied to the Heating Tower Heat Pump (HTHP) by Machine Learning

Author

Listed:
  • Qingqing Liu

    (College of Civil Engineering, Hunan University, Changsha 410081, China)

  • Nianping Li

    (College of Civil Engineering, Hunan University, Changsha 410081, China)

  • Yongga A

    (College of Civil Engineering, Hunan University, Changsha 410081, China)

  • Jiaojiao Duan

    (College of Civil Engineering, Hunan University, Changsha 410081, China)

  • Wenyun Yan

    (College of Civil Engineering, Hunan University, Changsha 410081, China)

Abstract

The corrosion rate is an important indicator describing the degree of metal corrosion, and quantitative analysis of the corrosion rate is of great significance. In the present work, the support vector machine (SVM) and the artificial neural network (ANN) integrating the k-fold split method and the root-mean-square prop (RMSProp) optimizer are used to evaluate the corrosion rates of alloys, i.e., copper H65, aluminum 3003, and 20# steel, applied to the heating tower heat pump (HTHP) in various anti-freezing solutions at different corrosion times, flow velocities, and temperatures. The mean-square error (MSE) versus the epoch of the ANN model shows that the result breaks the local minimum and is at or close to the global minimum. Comparisons of the SVM-/ANN-evaluated corrosion rates and the measured ones show good agreements, demonstrating the good reliability of the obtained SVM and ANN models. Moreover, the ANN model is recommended since it performs better than the SVM model according to the obtained R 2 value. The present work can be further applied to predicting the corrosion rate without any prior experiment for improving the service life of the HTHP.

Suggested Citation

  • Qingqing Liu & Nianping Li & Yongga A & Jiaojiao Duan & Wenyun Yan, 2021. "The Evaluation of the Corrosion Rates of Alloys Applied to the Heating Tower Heat Pump (HTHP) by Machine Learning," Energies, MDPI, vol. 14(7), pages 1-13, April.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:7:p:1972-:d:529429
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/7/1972/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/7/1972/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cui, Haijiao & Li, Nianping & Wang, Xinlei & Peng, Jinqing & Li, Yuan & Wu, Zhibin, 2017. "Optimization of reversibly used cooling tower with downward spraying," Energy, Elsevier, vol. 127(C), pages 30-43.
    2. Niksiar, Arezou & Rahimi, Amir, 2009. "Energy and exergy analysis for cocurrent gas spray cooling systems based on the results of mathematical modeling and simulation," Energy, Elsevier, vol. 34(1), pages 14-21.
    3. Cui, Haijiao & Li, Nianping & Peng, Jinqing & Cheng, Jianlin & Li, Shengbing, 2016. "Study on the dynamic and thermal performances of a reversibly used cooling tower with upward spraying," Energy, Elsevier, vol. 96(C), pages 268-277.
    4. Małgorzata Smuga-Kogut & Tomasz Kogut & Roksana Markiewicz & Adam Słowik, 2021. "Use of Machine Learning Methods for Predicting Amount of Bioethanol Obtained from Lignocellulosic Biomass with the Use of Ionic Liquids for Pretreatment," Energies, MDPI, vol. 14(1), pages 1-16, January.
    5. Wiens, Trevor S. & Dale, Brenda C. & Boyce, Mark S. & Kershaw, G. Peter, 2008. "Three way k-fold cross-validation of resource selection functions," Ecological Modelling, Elsevier, vol. 212(3), pages 244-255.
    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. Cui, Haijiao & Li, Nianping & Peng, Jinqing & Yin, Rongxin & Li, Jingming & Wu, Zhibin, 2018. "Investigation on the thermal performance of a novel spray tower with upward spraying and downward gas flow," Applied Energy, Elsevier, vol. 231(C), pages 12-21.
    2. Men, Yiyu & Liu, Xiaohua & Zhang, Tao, 2020. "Analytical solutions of heat and mass transfer process in combined gas-water heat exchanger applied for waste heat recovery," Energy, Elsevier, vol. 206(C).
    3. Li, Hailong & Wang, Bin & Yan, Jinying & Salman, Chaudhary Awais & Thorin, Eva & Schwede, Sebastian, 2019. "Performance of flue gas quench and its influence on biomass fueled CHP," Energy, Elsevier, vol. 180(C), pages 934-945.
    4. Cui, Haijiao & Li, Nianping & Wang, Xinlei & Peng, Jinqing & Li, Yuan & Wu, Zhibin, 2017. "Optimization of reversibly used cooling tower with downward spraying," Energy, Elsevier, vol. 127(C), pages 30-43.
    5. Yifei Lv & Jun Lu & Yongcai Li & Ling Xie & Lulu Yang & Linlin Yuan, 2020. "Comparative Study of the Heat and Mass Transfer Characteristics between Counter-Flow and Cross-Flow Heat Source Towers," Energies, MDPI, vol. 13(11), pages 1-29, May.
    6. Ayoub, Ali & Gjorgiev, Blaže & Sansavini, Giovanni, 2018. "Cooling towers performance in a changing climate: Techno-economic modeling and design optimization," Energy, Elsevier, vol. 160(C), pages 1133-1143.
    7. Redha, Adel Mohammed & Dincer, Ibrahim & Gadalla, Mohamed, 2011. "Thermodynamic performance assessment of wind energy systems: An application," Energy, Elsevier, vol. 36(7), pages 4002-4010.
    8. Su, Wei & Lu, Zhifei & She, Xiaohui & Zhou, Junming & Wang, Feng & Sun, Bo & Zhang, Xiaosong, 2022. "Liquid desiccant regeneration for advanced air conditioning: A comprehensive review on desiccant materials, regenerators, systems and improvement technologies," Applied Energy, Elsevier, vol. 308(C).
    9. Naveed, Muhammad Hamza & Khan, Muhammad Nouman Aslam & Mukarram, Muhammad & Naqvi, Salman Raza & Abdullah, Abdullah & Haq, Zeeshan Ul & Ullah, Hafeez & Mohamadi, Hamad Al, 2024. "Cellulosic biomass fermentation for biofuel production: Review of artificial intelligence approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    10. Sławomir Francik & Bogusława Łapczyńska-Kordon & Norbert Pedryc & Wojciech Szewczyk & Renata Francik & Zbigniew Ślipek, 2022. "The Use of Artificial Neural Networks for Determining Values of Selected Strength Parameters of Miscanthus × Giganteus," Sustainability, MDPI, vol. 14(5), pages 1-26, March.
    11. Jian Qin & Yipeng Wang & Jialuo Ding & Stewart Williams, 2022. "Optimal droplet transfer mode maintenance for wire + arc additive manufacturing (WAAM) based on deep learning," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 2179-2191, October.
    12. Yan, Bo & Wieberdink, Jacob & Shirazi, Farzad & Li, Perry Y. & Simon, Terrence W. & Van de Ven, James D., 2015. "Experimental study of heat transfer enhancement in a liquid piston compressor/expander using porous media inserts," Applied Energy, Elsevier, vol. 154(C), pages 40-50.
    13. Willcock, Simon & Martínez-López, Javier & Hooftman, Danny A.P. & Bagstad, Kenneth J. & Balbi, Stefano & Marzo, Alessia & Prato, Carlo & Sciandrello, Saverio & Signorello, Giovanni & Voigt, Brian & Vi, 2018. "Machine learning for ecosystem services," Ecosystem Services, Elsevier, vol. 33(PB), pages 165-174.
    14. Moises Ramos-Martinez & Carlos Alberto Torres-Cantero & Gerardo Ortiz-Torres & Felipe D. J. Sorcia-Vázquez & Himer Avila-George & Ricardo Eliú Lozoya-Ponce & Rodolfo A. Vargas-Méndez & Erasmo M. Rente, 2023. "Control for Bioethanol Production in a Pressure Swing Adsorption Process Using an Artificial Neural Network," Mathematics, MDPI, vol. 11(18), pages 1-26, September.
    15. Voyant, Cyril & Motte, Fabrice & Fouilloy, Alexis & Notton, Gilles & Paoli, Christophe & Nivet, Marie-Laure, 2017. "Forecasting method for global radiation time series without training phase: Comparison with other well-known prediction methodologies," Energy, Elsevier, vol. 120(C), pages 199-208.
    16. Chen, Qun & Wang, Moran & Pan, Ning & Guo, Zeng-Yuan, 2009. "Optimization principles for convective heat transfer," Energy, Elsevier, vol. 34(9), pages 1199-1206.
    17. Hong-Sen Yan & Yu-Fang Wang, 2019. "Matching decision method for knowledgeable manufacturing system and its production environment," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 771-782, February.
    18. Voyant, Cyril & Notton, Gilles & Darras, Christophe & Fouilloy, Alexis & Motte, Fabrice, 2017. "Uncertainties in global radiation time series forecasting using machine learning: The multilayer perceptron case," Energy, Elsevier, vol. 125(C), pages 248-257.
    19. Voyant, Cyril & Motte, Fabrice & Notton, Gilles & Fouilloy, Alexis & Nivet, Marie-Laure & Duchaud, Jean-Laurent, 2018. "Prediction intervals for global solar irradiation forecasting using regression trees methods," Renewable Energy, Elsevier, vol. 126(C), pages 332-340.
    20. Voyant, Cyril & Soubdhan, Ted & Lauret, Philippe & David, Mathieu & Muselli, Marc, 2015. "Statistical parameters as a means to a priori assess the accuracy of solar forecasting models," Energy, Elsevier, vol. 90(P1), pages 671-679.

    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:gam:jeners:v:14:y:2021:i:7:p:1972-:d:529429. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.