Predicting the dynamic and kinematic viscosities of biodiesel–diesel blends using mid- and near-infrared spectroscopy

This work reports the use of mid- and near-infrared spectroscopy (MIR and NIR) to predict the kinematic and dynamic viscosities of biodiesel–diesel blends. A partial least square regression (PLSR) modeling method was employed to develop the calibration models based on information from four commonly used biodiesel and three different commercial diesel fuels. For MIR spectroscopy, wavelengths in the fingerprint region of 550–1500 cm−1 were chosen for developing the model. The root mean square error of prediction (RMSEP) for kinematic viscosity and dynamic viscosity were 0.114 and 0.119 mm2/s, respectively, based on the validation set that consisted of 26 biodiesel–diesel blend samples made of six different biodiesel and three different diesel fuels. For the NIR spectroscopy, the PLSR model established using the spectral regions of 1100–1500 nm, 1600–1700 nm, and 1800–2200 nm obtained better results. The RMSEP were 0.070 mm2/s for kinematic viscosity and 0.062 mm2/s for dynamic viscosity prediction. The results indicated that both MIR and NIR can be used to accurately predict the viscosities of biodiesel–diesel blends, but better results can be obtained using NIR spectroscopy.

► Mid- and near-infrared spectroscopy were able to predict biodiesel viscosities.
► Better predictions were achieved with NIR than with MIR spectroscopy.
► The RMSEP was 0.070 mm2/s for kinematic viscosity prediction using the NIR model.
► The RMSEP was 0.062 mm2/s for dynamic viscosity prediction using the NIR model.