A hybrid intelligent model combining ANN and imperialist competitive algorithm for prediction of corrosion rate in 3C steel under seawater environment

Abstract

There are species of carbon steel in the industry suffering from corrosion phenomena under seawater environment. In this paper, for purposes of the prediction of 3C steel corrosion rate, the proposed methodology here adopts a hybrid model based on neural network (NN) and imperialist competitive algorithm (ICA). Additionally, to validate the suggested method, we have managed to apply a procedure, namely leaving-one-out cross-validation (LOOCV). Thus, the model in this paper is abbreviated as NN–ICA_LOOCV. In the case study, the model is implemented on 46 experimental samples. This dataset is included within five parameters, namely temperature, dissolved oxygen, salinity, PH value and oxidation–reduction potential as inputs and corrosion rate(s) as an output parameter. The dataset was divided into two parts: one for training and the other for testing with 42 and 4 data number, respectively. For an evaluation purpose, the performance of NN–ICA_LOOCV is compared with other models on the basis of indicators such as the coefficient of determination (R 2), root-mean-square error (RMSE) and mean absolute error (MAE). The model was successfully tested, yielding a prediction of corrosion rate with a RMSE of around 0.01, MAE of 0.011 and a correlation factor of 0.99 to the test data. The results demonstrate that the carefully designed hybrid model further succeeded to denote lower modeling error and higher accuracy. Hence, this model is an applicable and reliable offer to engineers in order to online and safe prediction of corrosion rate in 3C steel under seawater environment.