Removal of aqueous methylene blue dye over Vallisneria Natans biosorbent using artificial neural network and statistical response surface methodology analysis

Abstract

Azo-dye compounds prevalent in textile wastewater pose environmental risks. This research focuses on examining the kinetics and removal percentage of methylene blue (MB) using the biosorbent Vallisneria Natans, also known as pata dam (PD). Eight isotherm adsorption models were explored to comprehend the adsorption behaviours. Here, the data was better fit by Toth isotherm than others, indicating the nature of the adsorbent surface. The MB adsorption data were well fit with pseudo-second-order kinetic model, indicating a constant rate evaluation. The artificial neural network (ANN) and response surface methodology (RSM) effectively determined the conditions that resulted in an observed 88.34 % removal of methylene blue (MB). These optimal conditions comprised a 0.85 g/L adsorbent dose, a pH level of 7, a temperature of 42 °C, and a dye concentration of 40 mg/L. The best-performing ANN architecture was a 4–8–1 topology. ANN demonstrate superior modelling capabilities with an R2 = 0.99405, compared to RSM, with an R2 = 0.9837 when predicting the removal percentage of MB. Fourier transform infrared spectroscopy (FTIR) plays a crucial role in comprehending the interactions occurring during the adsorption of MB on the surface of the PD biosorbent. These findings showed that PD could effectively remove MB contaminated wastewater.