Predicting engine fuel properties of biodiesel and biodiesel-diesel blends using spectroscopy based approach

Biodiesel can be produced from several feedstocks, resulting in significant composition and fuel properties variations. Predicting fuel properties can significantly reduce engine development time with biodiesel produced from various feedstocks. The applicability of spectroscopy-based support vector regression (SVR) models to predict biodiesel properties are established in the literature. However, similar models for biodiesel-diesel blends are not available. SVR models were developed in the present study based on Fourier Transform Infrared (FTIR) spectra of biodiesel and biodiesel-diesel blends to predict important engine fuel properties. Additionally, SVR models were developed to predict biodiesel blend proportion in biodiesel-diesel blends based on their spectral data. Models were calibrated with 120 samples, comprising 50 biodiesel-diesel blends with 5 to 95% (v/v) biodiesel and 70 biodiesels. The models were validated using 60 samples, including 27 biodiesel-diesel blends and 33 biodiesels. The developed models resulted in a MAPE of 3%, 2.1%, 3%, and 1.19%, respectively, in predicting the blend proportion, viscosity, cetane number, and calorific value. Further, the prediction performance of the SVR models is compared with the conventional principal component regression and partial least square regression models, whose outcome suggest SVR as the most suitable approach.

Automated summary

This study developed SVR models based on FTIR spectra of biodiesel and biodiesel-diesel blends to predict important engine fuel properties. The models showed good performance in predicting the blend proportion, viscosity, cetane number, and calorific value. Compared with other regression models, SVR was found to be the most suitable approach.