Prediction Model for Optimal Efficiency of the Green Corrosion Inhibitor Oleoylsarcosine: Optimization by Statistical Testing of the Relevant Influencing Factors

Abstract

Optimization and statistical methods are used to minimize the number of experiments required to complete a study, especially in corrosion testing. Here, a statistical Box–Behnken design (BBD) was implemented to investigate the effects of four independent variables (inhibitor concentration [I], immersion time t, temperature ϑ, and NaCl content [NaCl]) based on the variation of three levels (lower, middle, and upper) on the corrosion protection efficiency of the green inhibitor oleoylsarcosine for low-carbon steel type CR4 in salt water. The effects of the selected variables were optimized using the response surface methodology (RSM) supported by the Minitab17 program. Depending on the BBD analytical tools, the largest effects were found for ϑ, followed by [I]. The effect of interactions between these variables was in the following order: [I] and ϑ > t and ϑ > [I] and [NaCl]. The second-order model used here for optimization showed that the upper level (+1) with 75 mmol/L for [I], 30 min for t, and 0.2 mol/L [NaCl] provided an optimal protective effect for each of these factors, while the lower level (−1) was 25 °C for ϑ. The theoretical efficiency predicted by the RSM model was 99.4%, while the efficiency during the experimental test procedure with the best-evaluated variables was 97.2%.