LHV and HHV prediction model using regression analysis with the help of bond energies for biodiesel

Lower heating value (LHV) is one of the most important fuel properties of an engine in terms of fuel economy and thermal efficiency. For this reason, various prediction models have been proposed in the literature to determine the LHV. In addition, higher heating value (HHV) prediction model, whose independent variables are generally preferred fuel characteristics such as density, viscosity, molecular weight, iodine value, cetane number, etc., has also been proposed. In this study, atomic bond structures in the fatty acid methyl and ethyl esters that make up biodiesel were investigated. Using the bond energy between atoms, LHV was predicted and HHV was calculated using the combustion reaction of esters. While determining the values of the bond energies, the experimental studies on esters were examined and the average bond energies of the ester were determined. LHV and HHV values of sixty-two esters were used as a training dataset for the second prediction model. The prediction equation for LHV was created by multiple linear regression analysis (R2 and Adj.R2 = 99.99%; p-value < 0.001) using the mass ratio of carbon (C), the mass ratio of oxygen (O), the mass ratio of hydrogen (H), and the unsaturation degree (UD). In this regression model, the standard error is 0.054 for sixty-two observations. Besides, the HHV estimation equation was obtained by developing the LHV equation. The validation of the model was carried out with LHV and HHV values measured in studies in the literature.