Enhancing arsenic sequestration on ameliorated waste molasses nanoadsorbents using response surface methodology and machine-learning frameworks.

The development of a novel nanobiosorbent derived from waste molasses for the adsorptive removal of arsenic (As) has been attempted in this study. Waste molasses were chemically ameliorated through a solvothermal route for the incorporation of iron oxide, thereby producing iron oxide incorporated carbonaceous nanomaterial (IOCN). Synthesis of IOCN was confirmed through transmission electron microscopy (TEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and atomic emission spectroscopy (AES) analysis. The surface area and porous behavior of IOCN were elucidated by Brunauer-Emmett-Teller (BET) assessments. The experimental conditions for adsorption were first modeled using response surface methodology (RSM) based on the central composite design (CCD), considering the parameters: adsorbate dosage, adsorbent dosage, pH, and contact time. RSM optimizations were improved upon using a three-layer feed-forward multilayer perceptron (MLP) based Artificial Neural Network (ANN) model. Optimization through ANN model resulted in the increase of the maximal As adsorption efficiency to ~ 96% for IOCN. The IOCN isotherm plots show the best fit for the Sips isotherm, and the reaction kinetics follows the pseudo-second-order model, indicating the chemisorption mechanism for As adsorption. Evidence for direct coordination of As to the surface of adsorbents was further confirmed by FTIR spectroscopic studies before and after As adsorption. The high adsorption efficiencies and the low-cost facile synthesis of the IOCN nanosorbent from agro-industrial waste indicate their potential for commercial applications.