Multidimensional insight into the corrosion inhibition of salbutamol drug molecule on mild steel in oilfield acidizing fluid: Experimental and computer aided modeling approach

The study aims to establish detailed surface adsorption of expired salbutamol drug molecule as an emerging anticorrosion additive for mild steel corrosion in oilfield acidizing fluid. To achieve this, wide experimental data from weight loss, potentiodynamic polarization, and DFT studies were considered, statistically analyzed, and modeled. Artificial neural network-genetic algorithm (ANN-GA) and adaptive neural fuzzy inference system-genetic algorithm (ANFIS-GA) were utilized as optimization tools considering multiple inputs and a single output variable (MISO). Maximum inhibition efficiencies of 80 %, 89 % and 84 % were obtained from weight loss, potentiodynamics study and elcectrochemical impedance spectroscopy at concentration of 0.4 g/L-1SB and temperature of 323 K respectively. The results obtained from the polarization test confirmed SB as a mixed-type inhibitor and the kinetic studies fit well in the Langmuir isotherm for surface adsorption. The surface analysis via FTIR, SEM/EDAX, and AFM strongly complements the DFT results. In the soft computing method without (GA) via artificial neural network (ANN) and adaptive neural fuzzy inference system (ANFIS), optimal prediction of 79.74 and 81.24 % was attained at varying operating conditions. The predictive capabilities of the two methodologies were compared in terms of coefficient of determination (R-2), chi square (chi(2)), root mean square error (RMSE) and model predictive error (MPE). Based on the statistical indices obtained, much credence was giving to ANFIS as the best predictive model over ANN. Effect of time and inhibitor concentration are the most significant parameters for the prediction of salbutamol drug as a potential anticorrosion additive for mild steel in oilfield acidizing conditions. (C) 2022 Elsevier B.V. All rights reserved.

Automated summary

This study aims to investigate the detailed surface adsorption of the expired salbutamol drug molecule as an emerging anticorrosion additive for mild steel corrosion in oilfield acidizing fluid. Through experimental data analysis and modeling using weight loss, potentiodynamic polarization, and DFT studies, the maximum inhibition efficiencies and surface adsorption characteristics were obtained. Artificial neural network-genetic algorithm and adaptive neural fuzzy inference system-genetic algorithm were utilized for optimization, and their predictive capabilities were compared.