Machine learning approaches to predict adsorption capacity of Azolla pinnata in the removal of methylene blue

Abstract Background In this study, the adsorption of methylene blue (MB) dye using an aquatic plant, Azolla pinnata (AP) was modelled using several various supervised machine learning (ML) algorithms, aiming to accurately predict the adsorption capacity under various experimental conditions. Methods The ML algorithms used in this study are the artificial neural network (ANN), random forests (RF), support vector regression (SVR), and instance-based learner (IbK). The SVR algorithm was trained using three kernels: radial basis function (RBF), Pearson VII universal kernel (PUK), and polynomial kernel (PolyK). The experimental data (adsorbent dosage, pH, ionic strength, initial dye concentration, and contact time) served as input for training the algorithms and with the adsorption capacity as the output. The performance of the algorithms was optimised based on the values of correlation coefficient (R) and fine-tuned using several error functions (e.g. mean absolute error, root mean square error, and non-linear chi-squared). Findings The best performing ML algorithm in this study is SVR-RBF which achieves the highest value in R (0.994) and has the lowest error.