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A Comparison of Central Composite Design and Taguchi Method for Optimizing Fenton Process Hindawi Publishing Corporation e Scientific World Journal Volume 2014, Article ID 869120, 14 pages http://dx.doi.org/10.1155/2014/869120 Research Article A Comparison of Central Composite Design and Taguchi Method for Optimizing Fenton Process Anam Asghar, Abdul Aziz Abdul Raman, and Wan Mohd Ashri Wan Daud Chemical Engineering Department, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia Correspondence should be addressed to Abdul Aziz Abdul Raman; [email protected] Received 21 April 2014; Revised 22 July 2014; Accepted 6 August 2014; Published 27 August 2014 AcademicEditor:SevalKutluAkalSolmaz Copyright © 2014 Anam Asghar et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In the present study, a comparison of central composite design (CCD) and Taguchi method was established for Fenton oxidation. [ ] +2 +2 Dye ini,Dye:Fe ,H2O2 :Fe , and pH were identified control variables while COD and decolorization efficiency were selected responses. 9 orthogonal array and face-centered CCD were used for the experimental design. Maximum 99% decolorization and 80% COD removal efficiency were obtained under optimum conditions. squared values of 0.97 and 0.95 for CCD and Taguchi method, respectively, indicate that both models are statistically significant and are in well agreement with each other. Furthermore, Prob >less than 0.0500 and ANOVA results indicate the good fitting of selected model with experimental results. Nevertheless, possibility of ranking of input variables in terms of percent contribution to the response value has made Taguchi method a suitable approach for scrutinizing the operating parameters. For present case, pH with percent contribution of 87.62% and 66.2% was ranked as the most contributing and significant factor. This finding of Taguchi method was also verified by 3D contour plots ofCCD. Therefore, from this comparative study, it is concluded that Taguchi method with 9 experimental runs and simple interaction plots is a suitable alternative to CCD for several chemical engineering applications. 1. Introduction several variables in different combinations to get the best possible result [8]. Optimization techniques are broadly Fenton oxidation is an efficient and widely applied AOPs, +2 classified into two categories: univariate and multivariate which utilizes ferrous iron (Fe ) and hydrogen peroxide approach. Univariate approach, also known as one-factor-at- (H2O2) under acidic conditions to produce hydroxyl radi- ∙ ∙ time (OFAT) approach, involves variation of one parameter cal (HO ) (reaction 1). Formation of HO is advantageous at a time. Nevertheless, multivariate approach has several because it is highly oxidative ( = 2.8 eV) and nonselective advantages over OFAT. For example [9], (1) it provides global in nature [1, 2]. However, the performance of Fenton reaction knowledge in its whole experimental domain while OFAT is influenced by several parameters such as pH, reaction time, gives local knowledge where experiment is performed; (2) it +2 initial concentrations of Dye, H2O2,andFe catalysts [3, 4]. is possible to study the interaction between the factors and +2 Moreover, excess consumption of chemicals (Fe salt, H2O2 nonlinear relationships with the responses, (3) the number of acid/base) and high cost of H2O2 have made this process experiments required to optimize the process is considerably economically nonviable [5, 6]. Therefore, a systematic way of lesser than that of OFAT approach; (4) within the experi- planning, execution, and statistical evaluation of process is mental domain at each point, quality of the information is required which is only possible through optimization process higher and can be known through the leverage. The stepwise [7]. Consider execution of optimization is presented in Figure 1. +2 +3 ∙ − Driven by the need of reducing the number of experi- Fe + H2O2 → Fe + HO + OH (1) ments, cost, time, and physical efforts, design of experiment In context to any engineering problem, optimization refers to (DOE) is an important statistical and mathematical tool improving the performance of system or process by applying for solving complex and multifactor engineering problems. 2 The Scientific World Journal Defining problems and responses/variables Selecting process factors Screening factors Eliminate/add factors No Significant factors Yes Selecting design (factorial, RSM, Taguchi, etc.) and planning experiments Analysis and evaluation of models Model fitness Actual and predicted ANOVA variation No Evaluate the Objective parameters and type achieved? of design chosen Yes Optimization Conducting confirmation experiments Figure 1: Flow chart for optimization. It includes response surface methodology (RSM), factorial can be analyzed through / ratio and ANOVA with design, and in some special cases artificial neural network simultaneously evaluating the significance of the factors in (ANN) [10]. However, central composite design (CCD), D- terms of their contribution to the response values. optimal, and Box-Behnken are found to be widely used opti- Available works on optimization of Fenton oxidation mization techniques for Fenton oxidation [11–13]becauseof mainly focus on CCD under RSM [15–17]. Nevertheless, the advantage of optimizing multifactor problems with opti- fewer studies based on Taguchi method are found in the mum number of experimental runs. Besides, these methods literature [18–20] but which technique is better for Fenton have limitation of increased number of experiments if several oxidation is still not inclusive. Therefore, this study was factors were selected for process optimization. For example, planned to compare two experimental design techniques and [ ] Box-Behnken suggests 54 experiments if six factors are Fenton oxidation with four parameters such as pollutants ini, +2 +2 required to be studied and with CCD this number increases Dye : Fe ,H2O2 :Fe ,andpHwasselectedasacasestudy. up to 80. Thus, with multiple factors these techniques are not For comparison, frequently used CCD and robust Taguchi appropriate because it increases the cost of chemicals, time, experimental design technique were chosen with a special and physical efforts. Therefore, a simplified design strategy is focus on comparing the optimization and detailed statistical requiredthatcanbeusedtoovercometheseproblems. analysis of the experimental results. Taguchi method is a robust statistical tool that allows the independent evaluation of the responses with minimum 2. Background number of experiments. It employs orthogonal arrays for experimental design and / ratio instead of responses 2.1. Response Surface Methodology and Central Composite itself to determine the optimum settings of control fac- Design. RSM, a multivariate statistical tool, consists of a tors and thus neglects the variations caused by uncontrol- group of mathematical and statistical techniques that are lable factors [14]. With this method, experimental results based on the fit of empirical models to the experimental The Scientific World Journal 3 Selecting process factors and their levels Determining value of , face centered value is desirable Initial phase Selecting responses Or Conducting experiments Model selection ANOVA F test Lack of fitness Adjusted and Adequate precision 2 (significant) (significant) predicted R value (S/N > 4) Analysis Obtaining model equation Comparing predicted and actual values Model graphs (2D and 3D contour) No Objective Evaluate the Decision reason achieved Yes Optimization Confirmation experiment Figure 2: Central composite design flow diagram. data obtained in relation to experimental design [21]. It (c) center points which represent replicate terms; center employs lower order polynomial [22]andithasalreadybeen points provide a good and independent estimate of proved to be a reliable statistical method for chemical process the experimental error. applications [23, 24]. In RSM category, CCD which is appropriate for fitting Considering these points, the number of experiments second order polynomial equations has been frequently designed by CCD will be discussed for optimizing several research problems. A CCD 2 has three groups of design points [25]: = +2+, (2) (a) two-level factorial or fractional factorial design points where is the total number of experiments, is the (2 ), consisting of possible combinations of +1 and −1 number of factors studied, and is the number of replicates. levels of factor; Central composite design under RSM is normally performed either by using design expert software or Minitab. In this (b) 2 axial points (sometimes called star points) fixed study, design expert (Version: 8.0.7.1) is used as optimization axially at a distance say from the center to generate software. The steps that will be followed for the central quadratic terms; composite design (CCD) are presented in Figure 2. 4 The Scientific World Journal In CCD, value of alpha is important to calculate as it Identifying factors and responses could determine the location of axial points in experimental domain. Depending on alpha value, design is spherical, orthogonal, rotatable, or face centered. Practically, it is in betweenfacecenteredandsphericalandiscalculatedas Selecting suitable orthogonal array 0.25 =(2) . (3) Conducting experiments Value of alpha equals 1 is desirable because it ensures the position of axial point within factorial
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