Application of machine learning models to investigate the performance of stainless steel type 904 with agricultural waste

Abstract

In this study, machine learning algorithm was used to model measurements of corrosion rates of stainless steel Type 904 in 0.5 M H2SO4 media as a function of exposure time, and temperature, when corrosion inhibitors are added in different dosage. Hyper-parameter optimization of the adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) was carried out to select the best model that accurately predicts the corrosion rate. A range of 1–10 neurons in a single hidden layer neural network with varying prominent training algorithms and activation functions was tested. The FCM-clustered ANFIS model tested a range of 2–20 cluster numbers. The models’ performance was evaluated using correlation determination (R2), root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute deviation (MAD). The experiments show that the FCM-clustered ANFIS model with 20 clusters outperformed all other models with MAPE, MAD, RMSE, and R2 values of 1.9222, 0.5543, 0.7317, and 0.9308 at the testing phase respectively, and 0.5457, 0.3943, 2.9258 and 0.9993 at the training phase respectively. The entire FCM-ANFIS model produced the optimum prediction with a 96.40% level of robustness and accuracy. Therefore, ANFIS and ANN can be used to predict corrosion inhibition using agricultural waste in acidic media with great degree of success. These models are reliable, cheap, fast, efficient, and effective tools for corrosion inhibition prediction.