Abstract
As biodiesel is basically composed by fatty acid esters that can be methyl or ethyl or propyl or butyl (FAME or FAEE. FAPE or FABE, respectively). Its kinematic viscosity (KV) is usually higher than the fossil diesel. Current diesel vehicle engines are not adapted to use pure biodiesel with higher KV since it may cause deposits in engines and diminish efficiency during fuel combustion due to its poor atomization. Therefore, monitoring and predicting biodiesel KV is crucial to meet specifications defined by regulatory agencies for final diesel-biodiesel blends. One cheap and fast manner to obtain properties of fuels is applying predictive methods considering largely available data and related parameters such as composition, degree of unsaturation, density, and/or temperature. Therefore, the present study applied multivariate exploratory techniques such as Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) to investigate the influence of esters composition from vegetable oils and algae oils on KV. As a result, some saturated esters and one type of unsaturated ester were related to KV. Therefore, they were used as variables for a new method to predict KV at 40 °C. It presented satisfactory accuracy with deviation AAD = 0.25 mm²/s and %AAD = 5.28 considering all biodiesel types. However, a different performance of KV prediction was observed between vegetable and algae biodiesel.
Keywords: PCA; HCA; Ester composition profile; viscosity prediction me
