Elucidating the efficacy of vanillin-tryptophan Schiff base ligand for mitigating carbon steel corrosion: weight loss, electrochemical, soft-computing, DFT, and MD-simulation

Abstract

Metal corrosion poses a threat to the structural integrity of industrial equipment, resulting in severe financial losses. To counteract this imminent danger, vanillin-tryptophan Schiff base ligand (VTSBL) was utilised to prevent carbon steel (CS) corrosion. The corrosion mitigation of CS in 1.5 M H₂SO₄ was modelled leveraging the predictive propensity of response surface methodology (RSM), artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS), as well as weight loss, electrochemical, adsorption isotherms, and computational methods. It was discovered that as the VTSBL concentration increased, the percentage inhibition efficiency (%IE) increased significantly by 91.65 % at the optimum conditions of 5.00 mM, 12.00 h, and 37.50 °C. The predictability of RSM, ANN, and ANFIS was evaluated using error function analysis, which incorporated correlation regression (R²), absolute average relative error (AARE), root mean square error (RMSE), and standard deviation (SD). Consequently, the ANFIS simulation yielded the highest R² (0.9824) and the lowest magnitude of AARE values (0.0093) compared to other algorithms, thereby showcasing its superiority over the RSM and ANN algorithms.