An exploration of RSM, ANN, and ANFIS models for methylene blue dye adsorption using Oryza sativa straw biomass: a comparative approach.

ABSTRACT: This study focused on simulating the adsorption-based separation of Methylene Blue (MB) dye utilising Oryza sativa straw biomass (OSSB). Three distinct modelling approaches were employed: artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), and response surface methodology (RSM). To evaluate the adsorbent's potential, assessments were conducted using Fourier-transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM). The evaluation of RSM, ANN, and ANFIS included the quantification of R², mean squared error (MSE), root mean square error (RMSE), and mean absolute error (MAE) metrics. The regression coefficients from the process modelling demonstrated that RSM (R² = 0.9216), ANN (R² = 0.8864), and ANFIS (R²

SUBMITTER: Kumari S 

PROVIDER: S-EPMC11757716 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature