Optimization of Parameters for Chemical Activation Reaction

The utilization of renewable and cheaper precursors to prepare activated carbon produces useful and economically feasible adsorbent but also contributes towards minimizing the solid wastes. The preparation method can be optimized to deter­mine the effect of the main parameters associated with the process are: Impregnation ratio, activation temperature, acid concentration, activation time, the precursor materials nature, the activation type (chemical and physical activa­tion), and pyrolysis temperature, all these affect the properties of the resulting activated carbon (Girgis and El-Hendawy 2002; Haimour and Emeish 2006; Diao et al. 2002).

The processing conditions are generally expressed in terms of some properties, among which: surface area, cation-exchange capacity (CEC), phenol and methylene blue, bulk density and adsorption efficiency towards iodine are frequently consid­ered (Vernersson et al. 2002; Yang and Lua 2006; Haimour and Emeish 2006). However, methylene blue is the most accepted probe molecules for the determina­tion of the ability of sorbent for the removal of large molecules whereas the iodine number shows indication on microporosity and consequently on the specific surface area of the sorbent materials (Baccar et al. 2010). Therefore, to determine the most important factor and their regions of interest it is essential to study these factors and their effects on the production of activated catalyst.

Experimental design technique is an important tool which provides statistical models in understanding the interactions among the parameters that have been opti­mized (Alam et al. 2007). The major benefit of using Response Surface Methodology (RSM) is to reduce the number of experimental trials required to evaluate several parameters and their interactions (Lee et al. 2000). RSM is a collection of statistical and mathematical techniques useful for developing, improving and optimizing pro­cesses. RSM involves three main stages: (1) design and experiments, (2) response surface modeling through regression, (3) optimization (Myers and Montgomery 1995). Based on this central composite design (CCD), quadratic models were devel­oped. The analysis of variance (ANOVA) on each experimental design response was recognized from them (Ahmad and Hameed 2010). Previously RSM was applied for the preparation of activated carbons using precursors such as Luscar char (Azargohar and Dalai 2005), Turkish lignite (Karacan et al. 2007), and olive-waste cakes (Bagaoui et al. 2001).

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