Abstract
Sixteen biodiesel cetane number (CN) predictive models developed since the early 1980s have been gathered and compared in order to assess their predictive capability, strengths and shortcomings. All are based on the fatty acid (FA) composition and/or the various metrics derived directly from it, namely, the degree of unsaturation, molecular weight, number of double bonds and chain length. The models were evaluated against a broad set of experimental data from the literature comprising 50 series of measured CNs and FA compositions. It was found that models based purely on compositional structure manifest the best predictive capability in the form of coefficient of determination R2. On the other hand, more complex models incorporating the effects of molecular weight, degree of unsaturation and chain length, although reliable in their predictions, exhibit lower accuracy. Average and maximum errors from each model’s predictions were also computed and assessed.
Highlights
The level of research carried out in recent decades regarding the use of biodiesel in engines has been intense
The degree of unsaturation and the chain length of the biodiesel are computed based on its fatty acids (FA) composition, and the cetane number (CN) is derived from Equation (10); a very high R2 value of 95.15% was reported
Another similar approach was followed by Mishra et al [28] in 2016, who developed the following relation based on 42 measured data sets of CN and FA composition: CN = 63.41 − 0.073 · degree of unsaturation (DU) + 0.035 · straight-chain saturation factor (SCSF) − 3.26 × 10−4 · DU · SCSF
Summary
The level of research carried out in recent decades regarding the use of biodiesel in engines has been intense. One unique aspect of biodiesels (and their parent oils/fats) is the fact that they are produced from a variety of vegetable or animal feedstock [1,2,3,4,5] These possess different compositional structures in the form of fatty acids (FA), as summarized in Table 1 for the most influential ones. These values (average for each feedstock) range from lower than that of the respective automotive diesel fuel up to much higher. Owing to its significance in engine performance and emissions, as well as to the fact that its experimental determination is time consuming, costly and scientifically challenging, it is not surprising that several CN predictive models have been developed in the past. Please notecorrelated that for theCN comparative evaluation,weight only models based solely the Ramirez-Verduzco et al [15]
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