Application of Response Surface Methodology in the Statistical Analysis of Biodiesel Production from Microalgae Oil

Abstract

Incessant use of fuel from petroleum is found to unmanageable because of its contribution to the depletion of ozone layer thereby causing excessive accumulation of greenhouse gases in the environment. Biodiesel has become the surest alternative due to its environmental and economic advantages over crude diesel. In this study, response surface methodology was used to optimize the production methyl ester. The effects of five reaction variables: methanol/oil molar ratio ( X 1 ), catalyst concentration ( X 2 ), temperature (X 3 ), time (X 4 ), and mixing rate (X 5 ) on transesterification of crude microalgae oil was investigated. A Central Composite Design (CCD) consisting five factors at five levels was used to analyze the transesterification of microalgae oil. Transesterification process was optimized as response to increase the yield. Thirty-four experimental runs resulted from the analysis. The biodiesel yield was characterized to determine its quality. A second order quadratic polynomial model was deduced to predict the methyl ester yield and the ANOVA test showed the developed model to be significant ( P < 0.05). The R 2 adj values of 0.9078 indicated that the regression model was a good one. RSM was also successfully applied to assess the effects of multiple variables, including the alcohol/oil molar ratio, catalyst concentration, temperature, rate of mixing, and reaction time, for the production of biodiesel from the crude algae oil. A statistical model predicted that optimal conditions were as follows: methanol/oil molar ratio 6.1; temperature, 55°C; time, 45min, catalyst concentration, 1.0%; and rate of mixing, 300rpm. These optimized conditions were validated and actual biodiesel yield of 94.362% confirming the efficacy of the model.The physicochemical analysis of the biodiesel oil from microalgae indicated it is reliable and usable for industrial uses having compared it with commercial diesel.