Abstract
Simulation and preparing predictive model of electrochemical impedance Nyquist plots based on radial basis function neural network (RBFNN) are presented in this paper. The RBFNN as a powerful predictive system predicts the real and imaginary parts of impedance as a function of time, temperature and inhibitor concentration. The mean R value of 0.9996 as regression coefficient and mean square error (MSE) value of 1.72 × 10 −3 as results show the validity of proposed method for simulation and prediction of electrochemical impedance spectroscopy (EIS) in different environmental situations.
Highlights
One of the serious problem in oil and gas production technology is corrosion that mainly occurred by carbon dioxide (CO2) as sweet corrosion and/or hydrogen sulfide (H2S) as sour corrosion in water injection system [1]
The radial basis function neural network (RBFNN) was trained with the train part, the provided RBFNN was tested with the test part and the results of the prediction are shown in Fig. 2, Fig. 4 and Fig. 6
Experimental condition of inhibitor concentration of 10−3 at temperature 25°C, inhibitor concentration of 3 × 10−5 at temperature 45°C and inhibitor concentration of 5 × 10−4 at temperature 65°C are selected as samples and the prediction results are shown in Fig. 2, Fig. 4 and Fig. 6 respectively
Summary
One of the serious problem in oil and gas production technology is corrosion that mainly occurred by carbon dioxide (CO2) as sweet corrosion and/or hydrogen sulfide (H2S) as sour corrosion in water injection system [1]. The usage of inhibitors in oil industry is currently prevalent because of the effectiveness and low cost such as nitrogen based inhibitors [2]. The adsorption ability depends on the sort of corrosion environment, the kind and surface of metal and inhibitor chemical structure [3]. Many studies are performed recently to model the experimental corrosion data. Artificial neural network (ANN) is used to predict metal corrosion manner in [4]. The issue of whitening environment on pitting corrosion is analyzed in [5]
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