Prediction of Carbon Steel Corrosion Rate Based on an Alternating Conditional Expectation (Ace) Algorithm
作者： Xing-yi Chen , Zong-ming Yuan , Yun-ping Zheng , Wei Liu
DOI: 10.1007/S10553-016-0664-7
关键词:
摘要: Based on dynamic corrosion experiments, we propose a new model for predicting rate that is based an alternating conditional expectation (ACE) algorithm. This lets us more accurately predict the broad range of temperatures, pH, and concentrations Ca2+, HCO − , Mg2+, Cl–, SO 2 − ions. tests performed testing sample group, have confirmed reliability also demonstrated its high accuracy. Sensitivity analysis rank correlation coefficient revealed major factor influencing N80 steel pH value. We carried out comparison results obtained when using ACE algorithm backpropagation neural network (BPNN) support vector regression (SVR) method. As result, found accurate than other currently used models.
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