Constitutive modeling for prediction of optimal process parameters in corrosion inhibition of austenitic stainless steel (Type 316)/acidic medium

Abstract

Corrosion is a major concern in the industrial application of ferrous alloys due to enormous cost involved in damages, maintenance and corrosion control. Material scientists increasingly use statistical methods to speed up material design, due to the need for several process parameters in corrosion inhibition method. This study focuses on optimization of three main contributing parameters; inhibitor concentration, exposure time and temperature on the austenitic stainless steel (SS) Type 316 corrosion inhibited through a novel eco-friendly waste material. Response surface method (RSM) was used for evaluation of experimental process variables influencing SS corrosion. The effect of changes in the level of these variables on stainless steel corrosion was studied using Box Behnken design. The optimum levels of process parameters were studied using quadratic regression model coupled with desirability approach. The relationship between predicted and experimental values shows the accuracy of the developed model. Morphologies of the corroded surfaces are examined via scanning electron microscope equipped with energy dispersive x-ray spectroscopy (SEM/EDX). The study showed that RSM is an effective statistical method to predict optimum operating parameters of the inhibitor studied in acid solution required to reduce corrosion rate of stainless steel.