Evaluating the predicting capability of response surface methodology on biodiesel production from grapeseed bio-oil

The present study is focussed to understand the importance and consequence of alternative possibilities of simulating, modeling, and optimizing methodologies and of the procedural enhancement in soft computing, particularly, in the process of transesterification to produce biodiesel. Here, grapeseed oil is extracted from its feedstock through a combination of cold pressing and soxhlet extraction process. Single-stage transesterification was employed with catalyst concentration, molar ratio, and reaction duration as the process parameters for the transformation of mono-alky triglycerides to fatty-acid methyl esters. The effectiveness and coherence of the response surface methodology (RSM) tool in predicting the biodiesel yield were measured in accordance with central composite designusing the interdependence ANOVA coefficients. Furthermore, the optimal process parameters of the transesterification process which produced maximum grapeseed biodiesel were compared with the outcome of RSM methodology to understand its efficacy. The RSM tool predicted the maximum yield of biodiesel as 97.62% from catalyst concentration of 1.045 g of NaOH, molar ratio of 0.2758 v/v, and reaction duration of 66.6 min, which was then validated experimentally with a yield of 97.7% grapeseed biodiesel. The grapeseed biodiesel thus produced was tested for its physiochemical properties and was found to be within ASTM standards.