Removal of Cibacron Blue 3G-A from industrial wastewater using agricultural waste products as adsorbents

Abstract

RPHCL has a porous structure with a random distribution of distinct pore diameters.

In this work, the efficacy of agricultural waste was tested in removing Cibacron Blue 3G-A (CCB) colour from contaminated textile wastewater. In this study, Rhamnus pits (RP) were used as a readily accessible waste material as an adsorbent by carbonizing and impregnating it with a strong base (KOH) and a strong acid (HCL), respectively. The method of impregnation was carried out systematically. The carbonization process involved inciting the RP pits at a temperature of 300 °C, followed by impregnating some of the sample with hydrochloric acid HCL (a strong acid) and some with sodium hydroxide KOH (a strong base), and finally inciting the sample at a temperature of 600 °C as part of the activation process. One kind of adsorbent was not treated with any kind of impregnation; another type was treated with a strong acid; and a third type was treated with a strong base. (CCB) dye used as adsorbed the adsorption capacity and maximum adsorption rate were determined after variables affecting the adsorption process were analyzed. Results showed that KOH- and HCL-impregnated samples removed 96.6% and 88.24% of the sample, respectively, more effectively than the untreated control sample, which removed just 62.42%. Adsorption was performed at 30 degrees Celsius (86 degrees Fahrenheit), 0.05 g/10 ml of dye solution, 60 minutes, and a concentration of 25 mg/l of dye solution. The equilibrium adsorption curves were calculated using two widely used adsorption isotherm models. For KOH-activated RP pits, the Langmuir isotherm model offered a good fit to the data. In this study, the comparison and contrast are according to the pseudo-second-order and pseudo-first-order kinetic models for adsorption. It has been shown that the adsorption rate rapidly rises initially but gradually reduces when equilibrium is reached. When applied to the data, the pseudo-second-order model demonstrated an excellent fit with a confidence level of 0.999.