TAILIEUCHUNG - A response surface methodology approach for the optimization of Cu removal using rice husk– derived activated carbon
In this study, we have used the potassium hydroxide (KOH) as an eco–friendly and favorable activating agent to develop the porous and defect structure of activated carbon. Otherwise, the response surface methodology (RSM) has been applied to investigate the effects of the adsorption parameters including initial concentration, adsorbent dosage, and pH of solution on the percentage of Cu2+ removal. | Journal of Science and Technology 54 (4B) (2016) 123-131 A RESPONSE SURFACE METHODOLOGY APPROACH FOR THE OPTIMIZATION OF CU2+ REMOVAL USING RICE HUSK– DERIVED ACTIVATED CARBON Long Giang Bach1, Bui Thi Phuong Quynh1, Van Thi Thanh Ho2, Nguyen Thi Thuong1, Dinh Thi Thanh Tam1, Trinh Duy Nguyen1, Tran Van Thuan1, * 1 NTT Institute of High Technology, Nguyen Tat Thanh University, 298–300A Nguyen Tat Thanh, Ho Chi Minh City, Vietnam 2 Hochiminh City University of Natural Resources and Environment, 236B Le Van Sy, Ho Chi Minh City, Vietnam * Email: tranvt@ Received: August 2016; Accepted for publication: 10 November 2016 ABSTRACT In this study, we have used the potassium hydroxide (KOH) as an eco–friendly and favorable activating agent to develop the porous and defect structure of activated carbon. Otherwise, the response surface methodology (RSM) has been applied to investigate the effects of the adsorption parameters including initial concentration, adsorbent dosage, and pH of solution on the percentage of Cu2+ removal. The RSM–based two order regression polynomial models were found to be statistically significant by values of the coefficients of determination (R2) closer than and the P–values F were less than and determination of coefficient R2 was closer . The adequate precision (AP) ratio was used to measure to noise ratio. This ratio greater than indicated an adequate signal and the proposed model could be used to navigate the design space. In addition, the predicted and actual values positioned at the straight line revealed high fitness of model (Figure 2a). Otherwise, lack of fit (LOF) value was statistically insignificant to indicate the model fitted data well. Table 2. Matrix of observed and predicted values No Response (Cu2+ removal) Variables x1 (Ci, ppm) x2 (dosage, g/L) x3 (pH) Actual (%) Predicted (%) 1 25 2 2 75 2 126 A response surface methodology approach for the .
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