Simulation of Lead Removal Using Palm Kernel Shell Activated Carbon in a Packed Bed Column

EasyChair Preprint 6781

Abstract

The abundancy and excellent adsorption efficiency of palm kernel shell activated carbon (PKSAC) make it popularly studied as potential adsorbent for heavy metal removal from wastewater, especially lead (Pb2+) generated by lead-based industry. However, the practicability of PKSAC is limited to laboratory scale. This work focuses on dynamic simulation of Pb2+ adsorption by PKSAC packed bed column using Aspen Adsorption V10® to investigate the feasibility of PKSAC for industrial scale Pb2+ removal. The physical properties of PKSAC and equilibrium data of Pb2+ adsorption obtained from existing laboratory experiments were inserted into a liquid plug flow bed with linear lumped resistance model. The column performance in terms of breakthrough, saturation, and adsorption capacity were evaluated and Average Relative Error (ARE) was applied to affirm the reliability of the data and model. The adsorption efficiency was also found to be proportional to the bed diameter and height, and retrograde with increment of flowrate and Pb2+ concentration. The temperature did not show significant effect to the column breakthrough and adsorption capacity. In comparison with other type of adsorbent material which reached breakthrough at 2.3h, while PKSAC packed bed column achieved significantly longer breakpoint at 19 days, under constant operational parameters and bed dimensions. To further optimize the column for industrial use with 3 months service time, the packed bed was sized to 7 m height and 0.5 m diameter to treat effluent that contains 26 mg/L of Pb2+ at 100 m3/day.

Keyphrases: Isotherm, Lead adsorption, Palm kernel shell activated carbon, dynamic simulation, scale-up

@booklet{EasyChair:6781,
  author    = {Rachel Fran Mansa and Ming Ling Ting and Audrey Osong Patrick and Sivakumar Kumaresan},
  title     = {Simulation of Lead Removal Using Palm Kernel Shell Activated Carbon in a Packed Bed Column},
  howpublished = {EasyChair Preprint 6781},
  year      = {EasyChair, 2021}}