Volume No. 9 Issue No.: 4 Page No.: 1109-1116 April-June 2015

 

OPTIMIZATION OF FLUORIDE REMOVAL SYSTEM USING Ocimum SP. LEAVES AND RAGI SEED HUSK BY APPLYING BIO-STATISTICAL TOOLS

 

Upendra R.S.*, Pratima Khandelwal1, Amiri Z.R.2, Amulya Achar, Kumari B.G., Sowmaya M. and Tejaswini J.1

1. Department of Biotechnology, New Horizon College of Engineering, Marathahalli, Bangalore, Karnataka (INDIA) 2. Department of Food Science & Technology, Sari Agricultural Sciences and Natural Resources University, Sari (IRAN)

 

Received on : January 10, 2015

 

ABSTRACT

 

Fluoride is a naturally occurring element in water systems and enters food chain mostly through drinking water. The WHO permissible limit of fluoride in water is 1.0 mg/l. At < 1.0 mg/l, it inhibits dental caries, at > 1.0 mg/l causes molting of teeth, lesion of endocrine glands, thyroid, liver and other organs. At still higher concentration (3-6 mg/l), it causes skeletal fluorosis. Existing fluoride removal techniques havenít been very effective as these remove fluoride only up to 2 mg/l. Therefore, an economically viable, eco-friendly and easy method for defluoridation of drinking water is highly desirable. In the present investigation, Ocimum sp. leaves along with ragi seed husk was used as natural fluoride adsorbents and the process parameters such as absorbent dosage (1-10 g/l), pH (3-12) and contact time (10-150 min) were optimized using Central Composite Design (CCD) of Response Surface Methodology (RSM). The fluoride content in the water was quantitatively determined by UV spectrophotometric analysis and the presence of fluoride in the treated Ocimum sp. leaves were identified with EDAX analysis. RSM design optimized conditions i.e - 5.5 g/l each of Ocimum sp. leaves and ragi seed husk, 6.0 pH and 50 min contact time gave the end values of 0.43 mg/l of fluoride. The optimized values of RSM with respect to the end fluoride content (0.43 mg/l) after treatment process were validated using feed forward model of Artificial Neural Network (ANN). ANN predicted value (0.4250 mg/l) was very close to the optimized experimental value of RSM design (0.43 mg/l) and the error was 0.049. In conclusion, an optimized process was developed for the removal of fluoride from the drinking water using Ocimum sp. leaves and Ragi seed husk as natural fluoride adsorbents. Final concentration of 0.43 mg/l of fluoride was achieved from initial concentration of 10mg/l.

 

Keywords : Fluoride removal, Adsorption, Ocimum sp. leaves and Ragi seed husk, Response Surface Methodology (RSM), Artificial Neural Network (ANN), EDAX analysis

 

 

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