RSM-based predictive model for efficient surfactant removal using sustainable adsorbents from synthetic SDBS solution

The presented study aimed to assess two sustainable organic biosorbents, Medicago sativa (MG) and Moringa oleifera (MO), for efficient removal of sodium dodecylbenzene sulfonate (SDBS) from its synthetic solution. Optimizations were carried out on for the following parameters: pH of the synthetic solution, initial SDBS concentration, quantity of biomass, contact time and biomass size. For further characterization, Fourier Transform Infrared (FTIR) spectroscopy, X-ray Diffraction (XRD) and Field Emission Scanning Electron Microscopy (FESEM) were employed. FTIR band shifts for MO and MG biosorbents near 1300–1460 cm −1 suggested S=O stretching vibrations, correlating with SDBS adsorption. XRD peaks at 27–29° were associated with the presence of sulphate groups as a result of SDBS adsorption. FESEM images of biosorbents with dispersed morphology before adsorption, and congregation morphology post-adsorption, recommended the occurrence of better SDBS adsorption. Isothermal experimental data with coefficient of determination (R 2 > 0.99) and a maximum adsorption capacity (Q max mg g −1 ) of 28.73 for MO and 19.64 for MG demonstrated the best fit with the Langmuir model. Further statistical analysis based on response surface methodology (RSM) was utilized to design a predictive model (PM) for the efficient SDBS removal. RSM studies correlated the suitability of MO and MG biomasses with the coefficient of determination (R² ≈ 0.9) for efficient SDBS removal. The p-value from the F-test for both biomasses showed values < 0.0001, giving evidence of SDBS adsorption. Thus, the efficacy of MO and MG organic biomasses for efficient adsorptive treatment of anionic surfactant SDBS has been highlighted in the present study. The proposed predictive model, using a synthetic solution of surfactant, SDBS, intends to provide insight and assistance with wastewater treatment plant (WWTP) management.