Graphene oxide-sulfated lanthanum oxy-carbonate nanocomposite as an adsorbent for the removal of malachite green from water samples with application of statistical optimization and machine learning computations

Abstract

Sulfated lanthanum oxy-carbonate nanorods (S-La2O2(CO3) NRs) was synthesized by the reverse micelle microemulsion method and then used to modify graphene oxide nanosheets to synthesize of graphene oxide-sulfated lanthanum oxy-carbonate (GO-S-La2O2(CO3)) nanocomposite. By characterization of S-La2O2(CO3) NRs and GO-S-La2O2(CO3) nanocomposite by the Fourier Transform-Infrared (FT-IR) Spectrophotometry, Field Emission-Scanning Electron Microscopy (FE-SEM), Energy-dispersive X-ray spectroscopy (EDS), Transmission Electron Microscopy (TEM) and X-ray diffraction analysis (XRD), GO-S-La2O2(CO3) was used for treatment of malachite green (MG). To find the optimum removal percentage (RP), influencing parameters were investigated by the response surface methodology based on central composite design (RSM-CCD). Adsorption mechanism was evaluated by Dubinin–Radushkevich (D-R), Langmuir, Temkin, Freundlich (two parameter equations) and Sips (Three parameter equations) isotherms and based on the results the adsorption of MG into the GO-S-La2O2(CO3) nanocomposite obeyed by the Freundlich isotherm with the maximum adsorption capacity of 555.5 mg g−1. Also, the results of kinetic analysis show that the adsorption of MG onto the GO-S-La2O2(CO3) nanocomposite followed by the pseudo second order kinetic model. For estimation of adsorption behavior, different machine learning techniques are used and based on the results; ANFIS model has the confidential operation because of fuzzy procedure and flexibility of data mining in distributed adsorption data.