Response Surface Method as an Efficient Tool for Modeling and Optimizing Performance

Abstract

Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques useful for developing, improving and optimizing processes. In this paper, the Central Composite Design (CCD) method for fitting a second-order model was used and illustrated with the data extracted from Ipadeola (1990) as found in Ilimiese (2008) to examine the effects of extraction time (t ), solvent volume (V ), ethanol concentration (C ) and temperature (T ) on the yield and phosphatidylcholine enrichment (PCE) of deoiled rapeseed lecithin when fractionated with ethanol. The significance of the linear, quadratic and interaction terms was first examined and it was discovered that each of them significantly contributes to the response model at 0.05 level, which implies that the fitted second-order model significantly explains the response surface. Then the analysis proceeds to locate the set of the levels of the factors that optimize the predicted response (the stationary point). This point is the combination of the design variable levels where the predicted response is at its optimum, and it was found that the estimated maximum response yield of deoiled rapeseed lecithin is yˆ 21.47 at the stationary point (t = 1.82545 = 1.83 min of reaction time, V = 8.66455 = 8.66 liter solvent volume, Conc. = 96.67299 = 96.67% of ethanol concentration, and T = 22.93308 = 22.93 ºC temperature). The canonical analysis was carried out and it was detected that the stationary point is a saddle point.