Synthetic dyes can be hazardous to the ecosystem, even at low concentrations in the effluent. In this research, the Heracleum persicum stems-Fe3O4 (MHPS) adsorbent performance for the removal of crystal violet (CV) and methylene blue (MB) from binary aqueous solutions was investigated in a batch method under the influence of different parameters. In addition, predictive models for the adsorption process were developed using machine learning techniques such as artificial neural networks and random forests. ANN and RF models achieved high R2 values of 0.9501 and 0.9797 for CV, 0.9471, and 09,834 for MB, respectively, and obtained low MSE values of 0.07107 and 0.03405 for CV, 0.09933, and 0.02908 for MB. The proposed adsorbent is cheap and eco-friendly and, on the other hand, is easily collected by the magnetic field. The adsorbent was characterized by applying FESEM-EDX, FESEM, BET, and FTIR. Various isotherm and kinetics models for the simultaneous adsorption of CV and MB were investigated in aqueous solutions. The adsorption isotherm and kinetics studies explain that the extended Langmuir model and pseudo-second-order models are best suited for CV and MB in the binary solution. The exothermic adsorption was achieved in the temperature range of 5-45 °C.
Keywords: Adsorbent; Artificial neural network; Crystal violet; Environmental remediation; Methylene blue; Random forest model.
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