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A Novel Reference Property-Based Approach to Predict Properties of Diesel Blended with Biodiesel Produced from Different Feedstocks
ISSN: 1946-3952, e-ISSN: 1946-3960
Published December 22, 2021 by SAE International in United States
Citation: Bukkarapu, K., Suraj, C., and Krishnasamy, A., "A Novel Reference Property-Based Approach to Predict Properties of Diesel Blended with Biodiesel Produced from Different Feedstocks," SAE Int. J. Fuels Lubr. 15(1):73-98, 2022, https://doi.org/10.4271/04-15-01-0004.
Considering the biodiesel composition, blend percentage, and temperature as input variables in the models to predict biodiesel-diesel blends’ properties is imperative. However, there are no models available in the literature to predict the properties of biodiesel-diesel blends that consider all these variables. The accuracy of spray and combustion models for diesel engines depends on the accuracy at which the fuel properties are estimated. Thus, straightforward approaches to accurately predict the properties of biodiesel-diesel blends are required. A novel reference property-based approach is proposed in the present work to predict the biodiesel-diesel blends’ properties to address this research gap. Models available in the literature correlating the properties of interest to fuel temperature were modified by including a reference property measured at 293 K. The effect of biodiesel composition and blend percentage are captured by including a reference property as an input variable. For model calibration, an extensive experimental database is created with 119 samples, including the density, dynamic viscosity, and kinematic viscosity of diesel, biodiesel produced from different feedstocks, and biodiesel-diesel blends at temperatures ranging from 293 K to 373 K. The proposed models predicted the dynamic viscosity, kinematic viscosity, and density of 24 external validation samples with a mean absolute percentage error (MAPE) of 2.15%, 4.73%, and 0.13%, respectively. Overall, the models developed in the present study are reliable and straightforward that can predict the properties of diesel, biodiesels, and biodiesel-diesel blends by considering the effects of biodiesel composition, biodiesel blend proportion in diesel, and temperature simultaneously, unlike the available literature models.