Adsorptive Removal of Phenol by Activated Alumina and Activated Carbon from Coconut Coir and Rice Husk Ash

Abstract

Phenol (C6H5OH) is considered as a serious environmental pollutant, and therefore, the study for its removal from wastewater by adsorption has gained momentum by many researchers. The purpose of this research was to study the phenol removal efficiency using three adsorbents viz. activated alumina and activated carbon from coconut coir and rice husk ash. Initially, the characterizations of the adsorbents were performed. The phenol removal percentage was then investigated in batch experiments with the change of process variables, e.g., initial phenol concentration, contact time, pH, temperature, and adsorbent dose. The experimental results showed that at optimum conditions, the maximum phenol removals for activated alumina and activated carbon from coconut coir and rice husk ash were 21.8%, 95.2%, and 94.23% respectively. These results were tested using several isotherms and kinetic and thermodynamic models. The test of kinetic models showed that pseudo-second-order model was fitted better than the pseudo-first-order model for all three adsorbents. The test of isotherm models showed that the Freundlich isotherm was better for activated alumina and activated carbon from coconut coir, whereas the Langmuir isotherm was better for rice husk ash. The thermodynamic study showed that the adsorption process was non-spontaneous, non-random, and exothermic for activated alumina; spontaneous, non-random, and exothermic for activated carbon from coconut coir; and spontaneous, random, and endothermic for rice husk ash. The safe disposals of the spent adsorbents were also deliberated in this study. The research discovered that the preference of adsorbents for phenol removal was rice husk ash, activated carbon from coconut coir, and activated alumina. The novelty of this study was that the paper had included exhaustive analysis using testing of numerous models viz. pseudo-first-order model, pseudo-second-order model, Reichenberg model, Fick model, Furusawa and Smith model, Elovich model, Boyd model, Langmuir model, Freundlich model, Temkin model, and Dubinin–Radushkevich model.