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Prediction of Carbon Steel Corrosion Rate Based on an Alternating Conditional Expectation (Ace) Algorithm

  • ECOLOGY
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Chemistry and Technology of Fuels and Oils Aims and scope

Based on dynamic corrosion experiments, we propose a new model for predicting corrosion rate that is based on an alternating conditional expectation (ACE) algorithm. This model lets us more accurately predict the corrosion rate for a broad range of temperatures, pH, and concentrations of Ca2+, HCO − 3 , Mg2+, Cl–, SO 2 − 4 ions. Based on tests performed on a testing sample group, we have confirmed the reliability of the model and have also demonstrated its high accuracy. Sensitivity analysis based on a rank correlation coefficient revealed that the major factor influencing the corrosion rate of N80 steel is the pH value. We have also carried out a comparison analysis of the results obtained when using the ACE algorithm and the results obtained when using a backpropagation neural network (BPNN) and the support vector regression (SVR) method. As a result, we found that the model based on the ACE algorithm is more accurate than other currently used models.

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Authors and Affiliations

  1. Oil and gas engineering Institute of Southwest Petroleum University, Chengdu, 610500, China

    Xing-yi Chen, Zong-ming Yuan & Yun-ping Zheng

  2. School of Energy Resource of Chengdu University of Technology, Chengdu, 610059, China

    Wei Liu

  3. Post-Doctoral Research Center of Tarim Oilfield, Korla, 841000, China

    Wei Liu

Authors
  1. Xing-yi Chen
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  2. Zong-ming Yuan
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  3. Yun-ping Zheng
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  4. Wei Liu
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Correspondence to Xing-yi Chen.

Additional information

Translated from Khimiya i Tekhnologiya Topliv i Masel, No. 6, pp. 107 – 112, November – December, 2015.

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Chen, Xy., Yuan, Zm., Zheng, Yp. et al. Prediction of Carbon Steel Corrosion Rate Based on an Alternating Conditional Expectation (Ace) Algorithm. Chem Technol Fuels Oils 51, 728–739 (2016). https://doi.org/10.1007/s10553-016-0664-7

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