Optimization of flocculation process by microbial coagulant in river water

The existing process of coagulation and flocculation are using chemicals that known as cationic coagulant such as alum, ferric sulfate, calcium oxide, and organic polymers. Thus, this study concentrates on optimizing of flocculation process by microbial coagulant in river water. Turbidity and suspen...

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Bibliographic Details
Main Authors: Murad, Fatin Nabilah, Alam, Md. Zahangir, Salleh, Md. Noor, Al-Mamun, Abdullah
Format: Conference or Workshop Item
Language:English
Published: Kulliyyah of Engineering, International Islamic University Malaysia 2016
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Online Access:http://irep.iium.edu.my/51780/1/51780_optimization_of_flocculation.pdf
http://irep.iium.edu.my/51780/
http://www.iium.edu.my/icbioe/2016/
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Institution: Universiti Islam Antarabangsa Malaysia
Language: English
Description
Summary:The existing process of coagulation and flocculation are using chemicals that known as cationic coagulant such as alum, ferric sulfate, calcium oxide, and organic polymers. Thus, this study concentrates on optimizing of flocculation process by microbial coagulant in river water. Turbidity and suspended solids are the main constraints of river water quality in Malaysia. Hence, a study is proposed to produce microbial coagulants using wastewater as nutrients source for river water treatment. The parameters that have been highlighted to optimize the flocculation activity are pH, bioflocculant dosage and effluent concentration. The research was done in the jar test process and after that the process parameters for maximum turbidity removal was validated. The chosen microbial that was used as the bioflocculant producer is Aspergillus niger. The highest flocculating activity is on day seven of cultivation and bioflocculant was obtained from the supernatant. The optimum pH and bioflocculant dosage for an optimize sedimentation process are between 4-5 and less than 3 mL respectively. The model was validated by using a river water sample from Sg. Pusu and the result shows that the model was acceptable.