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  • The characteristic technique of optimization involves varyin

    2018-10-24

    The characteristic technique of optimization involves varying one variable at a time and keeping the other constant. This Romidepsin method is not only laborious but does not also represent the complete effects of the variables in the process and ignores the combined interactions between the variables (Survase et al., 2006). In contrast, response surface methodology (RSM) is an experiential modeling technique used to establish the relationship between a set of controllable experimental factors and the observed results. RSM analyzes the effect of the independent variables both in alone or in combination in the processes (Ghosh et al., 2012). In addition, this Romidepsin methodology also generates mathematical and statistical models to understand the interactions among the optimized parameters. Recently, this tool has been used extensively in many areas of scientific research for process optimization (Alam et al., 2007; Basri et al., 2007; Carvalho et al., 1997; Spangler and Emert, 1986; Betiku and Adesina, 2013; Betiku and Taiwo, 2015). The main objective of RSM is to determine the optimum operational conditions for the system or to determine a region that satisfies the operating specifications (Montgomery, 1991). In general, RSM is a collection of mathematical and statistical techniques useful for developing, improving and optimizing processes and can be used to evaluate the relative significance of several affecting factors even in the presence of complex interactions. The design procedure for the RSM involves commonly four steps (Gunaraj and Murugan, 1999). These are designing of a series of experiments for adequate and reliable measurement of the response of interest, developing a mathematical model of the second order response surface with the best fittings, finding the optimal set of experimental parameters that produce a maximum or minimum value of response and representing the direct and interactive effects of process parameters through two- and three-dimensional (3D) plots.
    Experimental
    Results and discussion
    Conclusions By using central composite design and quadratic programming, sets of experimental data and ANOVA, mathematical model equations were derived for apparent viscosity of iron ore-water slurries. The effects of the process variables on apparent viscosity of iron ore-water slurries were described using 3D response surface plots, which are simulations from the models. Predicted values obtained by the statistical model using the model equations were in reasonable agreement with the observed values. The adequacy of the model was confirmed by the coefficient of multiple regressions, indicating a reasonably good model for practical implementation. In the present models, R2 and F-values implied the fitness of the model. The residual analysis was also carried out for judging model adequacy. The apparent viscosity was found to be the quadratic function of particle diameter, solid concentrations, microwave exposure time and shear rate. Among the parameter interactional effect, the shear rate and particle diameter interaction had the maximum effect on apparent viscosity. Based on the empirical models, an optimum study was conducted to identify the suitable operating range for each of the parameters that provide the minimum apparent viscosity within the range of parameter values tested in this study. These were: particle diameter 122.32 μm, solid concentration 30.91%, microwave exposure time 90 s and shear rate 413.78 s−1; under these conditions the apparent viscosity was 21.31 mPa·s. The results indicated that optimization by using response surface methodology can be useful in improving the apparent viscosity of iron ore-water slurries.
    Acknowledgment The authors thankfully acknowledge the reviewers for suggesting valuable technical comments of this paper. The author is very much thankful to the sponsor CSIR (Council of Scientific and Industrial Research), New Delhi for their financial grant to carry out the present research work.