Flouride (F) content in groundwater above 1.5 mg/L is a serious concern all over the world due to adverse health effects beyond that concentration. Hence, apart from its monitoring, an effective and low-cost removal technique is necessary. In this work, the removal of fluoride using fly ash has been studied. Batch adsorption studies, using laboratory shaker, were performed to examine the effect of pH in the range 2-10, adsorbent size (0.225 mm, 0.45 mm, and 0.90 mm), and adsorbent dose, varied from 2-6 g/L, for a contact time of 4 hours in all the experiments. The optimum condition for the F” removal was observed as pH:5, adsorbent size:0.225 mm and adsorbent dose: 5 mg/L, providing 67.20 % F“removal from the solution. Different kinetic models were tested and it was found that adsorption kinetics is of Pseudo second order and Elovich type. The study showed that fly ash can be effectively used for fluoride removal from water.

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The high fluoride (F-) content in drinking water is highly hazardous to human health. Bagasse is a solid waste generated in the cane-based sugar industry. It can be used to get energy after firing in boilers or used to produce activated charcoal. The activated carbon is used as an adsorbent material to remove pollutants from water. In the present study, the activated carbon prepared from bagasse was used to remove F-contain in water. Batch adsorption studies were performed to examine the effect of temperature (T), treatment time (tR), and initial fluoride concentration (Fi-) on F- removal. Response surface methodology (RSM) was used to generate a mathematical model and for the optimization of parameters. The optimum operating condition was evaluated to be T = 26 oC, treatment time (tR) = 3.5 h, and Fi- = 25.14 mg/L, at which F-concentration in solution after treatment reached to 0.8 mg/L. The predicted values of F- in the solution obtained from the quadratic model were found to be well-matched with the experimental data. The model gave significant coefficients of determination R2 = 99.61%, R2 (adjusted) = 99.11%, and R2(predicted) = 97.71%, which shows that the model developed from RSM is highly accurate and well represents the process with its process parameters.