Development of Predictive Mathematical Model using Scent Leaf, Cassava Leaf and Neem Leaf Extracts as Corrosion Inhibitors on Mild Steel in Seawater and Sodium Chloride Environments

Mathematical Model is a statistical tool used to evaluate the rate of corrosion on oil and gas pipeline and proffer corrosion inhibitor to mitigate corrosion on the material. Experimental values were utilized to achieve predictive values. The tool enables us to deduce values on the pipeline where experimental values are not available. Maple Software was the basic tool used to perform the analysis. Weight loss method was utilized to obtain the experimental values which were further applied to determine the predictive values by using Mean Square Error (MSE), Evaluation of Accuracy on Prediction, Multiple Absolute Errors (MAE), Multiple Regression Analysis (MRA) and Polynomial Equation. Green plant extracts like Scent Leaf (SL), Cassava Leaf (CL) and Neem Leaf (NL) extracts were served as corrosion inhibitors which were added to these corrosive environments, Seawater, SW and Sodium Chloride, NaCl. The Mild Steel C-1026 coupons were immersed in the various solutions for 100 days. The results obtained from the study on weight loss of Seawater and Sodium Chloride was 0.271 and 0.193. Then the Weight loss on MSE analysis show that Seawater had both experimental and predictive values as 0.48 and 0.048008474 with an error of 0.0008474, while Sodium Chloride had 0.32 and 0.32007343 with error of 0.0007343 respectively. The evaluation of accuracy on prediction, EAP of weight loss in Seawater had 0.48 and 0.48008474 with error of 0.0008474, and Sodium Chloride has 0.32 and 0.32007343 with error of 0.00007343. Deducing from the analysis, the errors obtained from the MSE and EAP were very minimal. Therefore, Mathematical model is ideal for the prediction of pipeline corrosion and maintenance.