Impact of Environmental Parameters on the Corrosion Inhibition of ۷- Hydroxyphenoxazone: an Experimental and Artificial Neural Network Study

Artificial neural network model is a high precision predictive tool for unknown values without performing the related experiments. It can be developed and utilized for prediction of nonlinear corrosion processes.  In this way, owning a numer of input values, the output can predicted, exactely. In present work, first, the effect of holding time, hydrodynamic conditions and temperature were investigated on the inhibiting efficiency of ۷Hydroxyphenoxazone on steel corrosion in ۱.۰M HCl solution by electrochemical impedance spectroscopy (EIS). Then, the experimental variables such as concentration, immersion time, and hydrodynamic condition were taken as the input values and the corrosion inhibiting efficiency as the output value of artificial neural network model. Results showed  that by increasing immersion time up to ۸ hours, with ۱۰۰ppm of ۷-Hydroxyphenoxazone, the polarization resitance increases from ۱۶۶۰ to ۲۲۶۰ Ωcm۲ and inhibition efficiency reaches to ۹۱%. Also, by increasing rotational speed up to ۵۰۰ rpm and temperature up to ۵۵۰C, the inhibition efficiency decreases from ۸۶.۶% to ۲۴% and ۶۰%, respectively. The prediction results of artificial neural network indicated the good agreement with experimental data and the trained values of artificial neural network predicted the inhibiting efficiency values with average error less than ۱%.

کلیدواژه ها:

Artificial Neural Network ، Inhibitive Behavior ، ۷-Hydroxyphenoxazone ، electrochemical impedance spectroscopy ، شبکه عصبی مصنوعی ، بازدارندگی ، ۷- هیدروکسی فنوکسازون ، طیف سنجی امپدانس الکتروشیمیایی