Removal of COD and Ammonia Nitrogen by a Sawdust/Bentonite-Augmented SBR Process

Parsa Mohajeri, Mohammad Razip Selamat, Hamidi Abdul Aziz and Carol Smith
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Parsa Mohajeri: Department of Soil and Physical Science, Soil and Environmental Research, Lincoln University, Lincoln 7647, Canterbury, New Zealand
Mohammad Razip Selamat: School of Civil Engineering, Universiti Sains Malaysia, Nibong Tebal 14300, Seberang Perai Selatan, Pulau Pinang, Malaysia
Hamidi Abdul Aziz: School of Civil Engineering, Universiti Sains Malaysia, Nibong Tebal 14300, Seberang Perai Selatan, Pulau Pinang, Malaysia
Carol Smith: Department of Soil and Physical Science, Soil and Environmental Research, Lincoln University, Lincoln 7647, Canterbury, New Zealand

Clean Technol., 2018, vol. 1, issue 1, 1-16

Abstract: Water pollutant removal by biomass adsorbent has been considered innovative and cost-effective, and thus commendable for application in industry. However, certain important aspects have been overlooked by researchers, namely the efficiency in the operation time and pollutant removal. In this research, landfill leachate samples with organic components were treated using a bentonite-enriched sawdust-augmented sequencing batch reactor (SBR) process. By modifying the pH, the sawdust samples were categorized into three groups: the acidic, the alkaline, and the neutral. To bentonite samples, the pH-adjusted sawdust was added at 10%, 20%, and 30% amounts by mass, respectively. At the optimum aeration rate of 7.5 L/min and contact period of 22 h, the treatment achieved 99.28% and 95.41% removal of chemical oxygen demand (COD) and NH 3 -N with bentonite, respectively. For both pollutants, in the presence of sawdust, the removal was only reduced by about 17% with the contact period reduced to 2 h, which was a considerable achievement.

Keywords: ammonia nitrogen; bentonite; chemical oxygen demand (COD); response surface methodology (RSM); sawdust; sequencing batch reactor (SBR) (search for similar items in EconPapers)
JEL-codes: Q2 Q3 Q4 Q5 (search for similar items in EconPapers)
Date: 2018
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