Flash point prediction of tailor-made green diesel blends containing B5 palm oil biodiesel and alcohol

The flash point prediction accuracy of the Liaw model through UNIFAC-type models was evaluated and improved for B5 palm oil biodiesel-alcohol blends. The UNIFAC group interaction parameters of alcohol-alkyl chains (OH-CH2), alcohol-double bonded alkyl chains (OH-CC) and alcohol-esters group (OH-CCOO) were revised comprehensively to attend this improvement. The prediction accuracies (calculated as average absolute relative deviation (AARD)) were improved by solely revising the interaction parameters of OH-CH2 with similar AARD for all UNIFAC type models (from 7.0% and 5.8% to 1.1% for Original and NIST UNIFAC; from 6.6% and 6.2% to 1.3% for Modified UNIFAC (Dortmund) and NIST-Modified UNIFAC). The revised parameters were further validated using the test set data of B5-alcohol blends and B5-ethyl levulinate (EL)-butanol (BU) blends. Satisfactory improvements were obtained; but for B5-EL-BU blends, only the Original UNIFAC and NIST-UNIFAC model showed improvement. The presented study showed that by solely revising the interaction parameters of OH-CH3, a better flash point prediction is gained for the green diesel blends containing palm oil biodiesel and alcohol.