Removal of iron metal from industrial wastewater using natural rice husks and its pretreated activated carbon forms

This study contains a lab-scale investigation into the feasibility of applying an adsorption technique to treat wastewater polluted with iron metal exhaust from battery manufacturing. Rice husks have been prepared for use in three forms; natural rice husks without any activation, activated carbon forms of rice husks pretreated with sulfuric acid (H2SO4), and activated carbon forms of rice husks pretreated with potassium hydroxide (KOH). Firstly, the waste materials rice husks were heated to pre-carbonize at 275 Co for 95 minutes, and then for 20 hours, the two carbonized materials were soaked in a (4molar) acid or basic solution. Finally, with the presence of N2 flow gas, these materials were activated at 900 Co for 120 minutes. Activated carbon using KOH provided the best condition for the removal of Iron Ions than carbon activated using H2SO4. The adsorption process was studied using one type of heavy metal iron ion. At temperatures of (25, 30, 35, and 40) Co, the kinetics and adsorption isotherms of iron were evaluated for a concentration range of (20-100) mg/l. The maximum removal ratios of iron for natural rice husks and sulfuric acid or hydroxide potassium activation after 1h were 53.35%, 92.9%, and 98.5%, respectively. The maximum adsorption capacity of iron onto natural rice husks, rice husks pretreated with H2SO4 or KOH, and into activated carbons was (40,85 and 96.5) mg/g respectively, at a concentration of 100 ppm and at 25Co. The equilibrium adsorption curves were obtained using two well-known models of adsorption isotherm models. For iron ion on all types of prepared rice husk shapes, the data was fairly well fitted with Freundlich-isotherm. Two kinetics, pseudo-second order and pseudo-first order, were used to study the kinetics of adsorption. The results revealed a sharp rise in adsorption rate initially until equilibrium is achieved. With a confidence level of 0.99, the pseudo-second-order model accurately reflected the data.