Abstract

Response surface methodology (RSM) and artificial neural network (ANN) on modeling and optimization of corrosion inhibition efficiencies of mild steel using water hyacinth as an inhibitor was carried out in this work. The optimization of the process was done using generic algorithm (GA) and RSM which were subsequently compared. The optimum inhibition efficiency predicted were 87.675924 and 82.89% by ANN and RSM, respectively. The value of R2 obtained were 0.9695 and 0.85118 for ANN and RSM models, respectively while RMSE values of 3.90 and 4.3089 were gotten for RSM and ANN models, respectively. The model regression indicated that RSM best fit the experimental data thus perform better on mild steel corrosion inhibition.