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The performance of the Nano-magnetite Fe 3 O 4 impregnated onto walnut shell (Fe 3 O 4 -WNS), which possessed the adsorption features of walnut shell and the magnetic property of Fe 3 O 4 , was investigated for the elimination of the methyl violet and Rhodamine 6G from contaminated aqueous solutions. The effects of different experimental variables on the removal efficiency of the cited dyes were examined. Then these variables were used as the inputs to generate linear and non-linear models such as the multiple linear regression, random forest, and artificial neural network to predict the removal efficiency of these dye species at different experimental conditions. The validation studies of these models were performed using the test set, which was not present in the modeling procedure. It was found that ANN had a higher ability to predict the adsorption process under different experimental conditions, and could be applied for the development of an automated dye wastewater removal plant. Also the maximum adsorption capacity (q max ) indicated that the q max value for Fe 3 O 4 -WNS for removal of cationic dyes was comparable or better than that for some reported adsorbents. Also it should be cited that exhausted Fe 3 O 4 -WNS was regenerated using dishwashing liquid, and reused for removal of the cited dye species from aqueous solutions. [ABSTRACT FROM AUTHOR]

[Display omitted] • Methylene blue and Crystal violet removal studies using magnetic biosorbents prepared from Heracleum Persicum waste. • The quasi-second-order kinetic model best showed the methylene blue and crystalline violet data in the binary system. • The extended Langmuir multicomponent model better fit the methylene blue and crystal violet experimental data in binary aqueous solution. • RF has a higher predictive ability than ANN due to its better statistical parameters. 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. [ABSTRACT FROM AUTHOR]