Pour point prediction of biodiesel-ethanol blends by near-infrared spectroscopy

Abstract

Pour Point measurement is essential to assess biodiesel use in cold weather. In this work, biodiesel samples were obtained from 4 different types of cooking oil (sunflower oil, sunflower and soya oil, sunflower and corn oil, and corn oil). Biodiesel mixtures with ethanol were prepared by weight (% w/w). The pour point of the mixtures was measured according to the ASTM D97-08 procedure, which needs a cooling bath, able to attain very low temperatures (till −16 °C in the present work), and it is based on visual inspection of the mixture flowing ability. However, this procedure is tedious and time-consuming.NIR spectra of the biodiesel-ethanol mixtures were obtained in a very short time (approximately 1 min), and partial least-squares regression (PLS) was used to create a calibration model for the pour point prediction. In addition, the same spectra were used to develop a model for the ethanol content. Both models showed good prediction capability, with cross-validation standard error of prediction of 0.6177 for the percentage of ethanol and 0.9899 for the pour point. Although the error of prediction was higher for pour point than for ethanol, residual prediction deviation (RDP) for pour point was 3.4, and for ethanol it was 27, the lower RPD may be considered an excellent value for most analytical purposes.