Quantifying Uncertainty of Green Inhibition Efficiency of Luffa Cylindrica Leaf Extract on Mild Steel in Acidic Medium

Abstract

Green corrosion inhibitors, such as Luffa Cylindrica leaf extract, have demonstrated outstanding inhibitory efficiency on mild steel in acidic environments. However, their effective design and optimization are limited and time-consuming owing to the associated uncertainties. Quantifying these uncertainties remains a challenge due to the requirement of many model realisations to capture and represent the true distribution of uncertainty. This study built a Response Surface Model (RSM) approximation of corrosion inhibition efficiency (IE) for effective optimization and uncertainty propagation. To quantify the uncertainties, we explored two stochastic methods: Monte Carlo Simulation (MCS) and Markowitz classical theory with the Genetic Algorithm (GA). The two approaches differ in propagation, sampling, and the number of realizations. MCS uses the approximation RSM with 10,000 randomly generated realizations, whereas the Markowitz technique uses the mean-variance objective function with just 100 realizations. Markowitz's classical theory revealed a 50 and 99.9% chance that the IE of Luffa Cylindrica leaf extract is 79.7 and 76.5%, respectively while MCS indicates at least 10 and 90% probabilities that the IE of Luffa Cylindrica leaf extract is 85.16 and 74.14%, respectively. When compared to the 88.4% efficiency previously reported for the same extract, the two techniques indicate less than 10% chances for IE. As a result, for the actual implementation of green inhibitors, their assessment must include uncertainty analysis.

Keywords

• green inhibitor
• inhibition efficiency
• luffa cylindrical
• monte carlo simulation
• uncertainty analysis