Linear and nonlinear kinetics analysis and adsorption characteristics of packed bed column for phenol removal using rice husk-activated carbon

The linear and nonlinear kinetics analysis and adsorption characteristics of phenol adsorption onto activated carbon synthesized from rice husk biomass were investigated in a packed bed column. Several analyses such as physical properties, BET surface area, pore size distribution, FTIR, and SEM were used to investigate the adsorption properties of the rice husk-activated carbon (RHAC). The column adsorption studies indicated that the adsorption of phenol onto RHAC is favored by an increase in bed height and a decrease in solution flow rate, influent phenol concentration, and particle size. Various dynamic adsorption parameters depicting the adsorption characteristics of phenol onto RHAC were estimated from the breakthrough analysis of the experimental data. The fitting of the experimental data to the Thomas, Adams-Bohart, Yoon-Nelson, and Wolborska models using linear and nonlinear regression techniques showed that the four models gave good fits to the experimental data. The R-2 values for the regressed lines ranged from 0.6827 to 0.9918, and 0.9958 to 1.0000 for the linear and nonlinear regression techniques, respectively. Experimentally, a maximum adsorption capacity value of 14.57 mg/g was obtained; at the same experimental conditions, 14.88 mg/g was predicted by the nonlinear regression, while 9.78 mg/g was predicted by the linear regression of the Thomas model. The results affirmed the potency of RHAC for the treatment of phenol-contaminated wastewater. It provided comprehensive data needed for the design of phenol adsorption columns using RHAC. It equally revealed that a better model analysis would be achieved with the application of nonlinear regression.

Automated summary

The study investigated the kinetics analysis and adsorption characteristics of phenol onto activated carbon synthesized from rice husk biomass. Experimental data fitting to various models showed good fits, with nonlinear regression techniques providing more accurate results. The study confirmed the potential of rice husk activated carbon for treating phenol-contaminated wastewater and highlighted the importance of nonlinear regression for better model analysis.