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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
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    References listed on IDEAS

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    5. 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.
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