Adsorptive removal of surfactant using dolochar: A kinetic and statistical modeling approach

Abstract

Disturbingly high rates of consumption of surfactants in household and industries have led to mark them as emerging contaminants in the environment. In the present work, removal of sodium dodecyl sulfate (SDS), an anionic surfactant, using an industrial waste (dolochar) was explored. The adsorbent material was characterized with the help of Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD). Kinetic evaluation was performed using first, pseudo-first, second, and pseudo-second order models. Adsorption of SDS over dolochar was expressed best by pseudo-second order kinetic model with regression coefficient (R2) of.99. Three input parameters including adsorbent dose (20�10�g/L), initial concentration (30�100�mg/L) of the surfactant, and contact time (2�60�min) were chosen for optimization using response surface methodology based on Box�Behnken design (BBD) approach. A total of 15 experiments were run to examine the effect of these variables on removal of SDS by dolochar in a multivariate system. A regression analysis indicated the experimental data fitted well to a quadratic polynomial model with coefficient of regression (R2) as.99. ANOVA and lack-of-fit test depicted the precision and efficiency of the model. The optimized conditions for SDS removal were found to be adsorbent dose 16.62�g/L, contact time 40�min, and initial concentration 47�mg/L with removal efficiency as 98.91%. Practitioner points: Daily ablutions and use of personal care products introduce a number of surfactants and recalcitrant compounds into the environment. Adsorption is a handy and easy to operate treatment technique to remove graywater pollutants. Kinetic and statistical modeling may be recommended as one of the most prominent tools to understand the removal mechanism. Decentralized treatment of graywater using industrial wastes is recommended as sustainable solution in the developing nations. � 2019 Water Environment Federation