Packed bed column adsorption of phenol onto corn cob activated carbon: linear and nonlinear kinetics modeling

Abstract

In the present study, linear and nonlinear regression analysis for packed bed column adsorption of phenol onto corn cob activated carbon was investigated. The activation of the corn cob provided the activated carbon with enhanced surface area and micropore volume of 903.7 m2/g and 0.389 cm3/g respectively. The analysis of the physical properties of the corn cob activated carbon (CCAC) revealed that it contained 33.47% of fixed carbon, 5.82% of ash content, 18.01% of volatile matter, 0.63 g/mL of bulk density, 5.50% of moisture content, and a pH of 6.30. SEM images indicated the presence of interspatial pores within the matrix of the adsorbent, while the FTIR analysis revealed that the major functional groups in CCAC were alkanol, alkanes, alkyls, carboxylic acids, ethers, esters, and nitro compounds. The effect of the process parameters influencing the dynamic adsorption process was investigated at flow rates (9–18 mg/min), initial phenol concentration (100-300 mg/L), bed height (5–10 cm), and particle size (300-800 µm). The breakthrough time and adsorption capacity increased with an increase in bed height but decreased with an increase in flow rate, initial phenol concentration, and particle size. At optimum conditions of bed height 10 cm, initial phenol concentration, 100 mg/L, flow rate, 9 mL/min, and particle size 300 µm, the adsorption capacity at breakthrough (qb), adsorption capacity at saturation (qs), volume of effluent treated at saturation (Veff,s), length of mass transfer zone (MTZ), adsorbent exhaustion rate (AER), fractional bed utilization (FBU) factor, Reynolds number (Re), Sherwood number (Sh), and the percentage phenol removal (Ys) were 2.143 mg/g, 8.570 mg/g, 12.96 L, 7.50 cm, 30.86 g/L, 0.25, 17.93, 28.85, and 66.13% respectively. The linear and nonlinear regression analysis of the Thomas, Adams-Bohart, and Wolborska models fitted better with the experimental data than the Yoon–Nelson model. Generally, the nonlinear regression proved to be a better tool for dynamic adsorption model analysis as the model parameters generated by the technique have a better correlation with the experimental data when compared to those obtained via linear regression. Conclusively, this study has shown that CCAC can successfully be used for the removal of phenol from aqueous solutions. It also demonstrated that the modeling approach significantly affects the outcome of the analysis of dynamic adsorption systems.