Fabrication of MnWO4 modified ZnS nanocomposite as an effective nanoadsorbent: Experimental investigation and neural network modeling

The purpose of this study was to determine the adsorptive characteristics of a MnWO4/ZnS nanocomposite for removing Amaranth dye from an aqueous solution. A simple chemical precipitation approach was used to make the MnWO4/ZnS nanocomposite. The crystal structure, morphology, and pore size of the resulting nanocomposites were evaluated by UV-vis-DRS, FT-IR, XRD, SEM, and EDAX. In a laboratory batch adsorption experiment, the effect of operational parameters such as adsorbent dose, starting dye concentration, agitation speed, contact time, and temperature was investigated to optimize the conditions for maximum amaranth removal. To reduce the number of trials and the associated costs, an artificial neural network (ANN) was used to forecast dye removal effectiveness. With a tangent sigmoid transfer function (tansig) at the hidden layer and a linear transfer function (purelin) at the output layer, a backpropagation neural network with Levenberg–Marquardt training algorithm was utilized to predict adsorption efficiency. For amaranth dye, a contact time of 180 minutes, an adsorbent dosage of 0.35 g/L, and an initial dye concentration of 10 μM resulted in a 96 % dye removal. Different models were used to fit the equilibrium isotherm data. Langmuir and Temkin models have high R2 and are in good agreement with the experimental data (0.9966 and 0.9927). The pseudo-second-order model is appropriate to describe the experimental data, and film diffusion may be involved in the sorption process, according to the kinetic analysis. When the experimental data was compared to the dye adsorption efficiency predicted by the artificial neural network model, it was discovered that this model can accurately predict the behavior of the amaranth dye adsorption process on MnWO4/ZnS under various conditions.