Optimization and Predictive Modelling of Gravimetric Corrosion Characteristics of Irvingia Gabonensis Leaves Extracts as Anti Corrosion Inhibitor of Mild Steel in HCI Solution

Abstract

In this research, the optimization and predictive modeling of gravimetric corrosion characteristics of Irvingia gabonensis leaf extracts (IGLE) as an anti-corrosion inhibitor of mild steel in hydrochloric acid were investigated. Design expert software version 11 were used to analyze the corrosion inhibition-related process characteristics, such as inhibition efficiency, corrosion rate, and weight loss, and their relationships. An effort were made to obtain the optimal conditions for these corrosion inhibition-related process characteristics. Weight loss measurement and design methodology were used for the evaluation of the inhibition efficiency of IGLE for mild steel in HCl. The corrosion inhibition process variables were optimized and predictive regression models were developed using Box-Behnken tool of the Response Surface Methodology (RSM). The findings showed that there were a good fit between the model predictions and the experimental results. The quadratic models developed were significant with P value less than 0.05. The research established an inhibition efficiency of 88.9%, a corrosion rate of 0.143 mm/yr, and a weight loss of 0.02 g, which were obtained at the optimum conditions of an extract concentration of 0.6 g/L, an immersion time of 16 hrs, and a temperature of 298 K. Therefore, the models were considered ideal for prediction with a confidence level of 95%, and the optimal combination is suitable for the corrosion inhibition process design. Hence these models can be recommended for applications such as oil well acidizing and pickling pipelines.