Removal of oil from seawater using charcoal and rice hull

14th International Conference on Science, Engineering and Technology

自然科学;工业技术

AbdullahM, Aslam^1 ; Choudhary, Ayush^1

Department of Chemical Engineering, School of Civil and Chemical Engineering, VIT University, Vellore

Tamil Nadu

632014, India^1

关键词: Adsorption capacities;    Adsorption columns;    Adsorption equilibria;    Batch experiments;    Freundlich adsorption isotherms;    Isotherm equations;    Natural adsorbents;    Removal efficiencies;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/263/3/032007/pdf
DOI  :  10.1088/1757-899X/263/3/032007

来源: IOP

【 摘 要 】

Adsorption has been implemented to remove oil from sea water, because it is very efficient and can ensure purification to any required level in the case of multi-step arrangement of the process. Oil was expelled from seawater in a clump procedure using 2 different natural adsorbents which are charcoal and rice hull. Samples collected after adsorption were characterized using UV Spectroscopy and subsequently, the adsorption capacity of crude oil was studied. The outcome of contact time and adsorbent dosage were studies concerning their impact on removal of oil. It was seen that as the adsorbent amount and contact time are increased, oil removal is enhanced. Charcoal was proven to be the most efficient adsorbent with removal efficiency of 99.67% followed by rice hull. After performing batch experiments, continuous adsorption in an adsorption column was also accomplished for the two of most efficient adsorbents after including beef tallow as our third adsorbent in discussion. Effect of bed height with respect to removal efficiency was studied here. The result showed that with increase in bed height, removal efficiency increases too. Adsorption isotherms of oil on adsorbents were determined and correlated with the usual isotherm equations such as Langmuir and Freundlich. The Freundlich adsorption isotherm model was found to correlate the adsorption equilibrium data better than the Langmuir model.