Adsorption characteristics of Castor Leaf Powder (CLP) for the Removal of Crystal Violet and Brilliant Green Dyes

Abstract

Adsorption is a mass transfer process for the dye removal from wastewater; generally the dyes are removed by using synthetic adsorbent such as activated carbon, zeolite and many other adsorbent. But, the problem is synthetic adsorbent is too costly and is not easily available. So our main focus is on use of natural adsorbent that are readily available and the adsorbent does not have any commercial value. In this work we have used CLP (Castor leaf powder) synthesized from mature Castor leaf, having high adsorption capacity for dye and it is relatively cheap. This work presents the experimental studies on the adsorptive removal of Brilliant green and Crystal violet dye using a natural adsorbent CLP. Preparation methodology for CLP involved grinding, drying (sun drying), boiling and drying in hot air oven at 105 °C. The synthesized CLP was then used to study the effect of various parameters namely pH, temperature, agitation speed, adsorption time, CLP loading initial dye concentration and ultra-sonication time on the dye removal efficiency. Then, the optimal process parameters for the dye–CLP system were determined. It was observed from the experimental analysis that the dye removal efficiency increases with increasing the adsorbent dosage, adsorption time as well as stirrer speed and the optimal values of CLP dosage, adsorption time and stirrer speed were found to be 10 g/L, 2 h and 200 rpm respectively for Crystal violet and 15g/L, 2h and 300 rpm respectively for Brilliant green. On the other hand, the dye removal efficiency decreased with increasing the initial dye concentration in the solution. The optimum pH observed 8 and 10 for Crystal violet and Brilliant green dyes removal respectively. During comparison analysis CLP shows the greater removal efficiency than synthetic zeolite. Finally, the data were fitted with various equilibrium and kinetic models and the model parameters were obtained by curve fitting and regression analysis.