Flash point prediction of tailor-made green diesel blends using UNIFAC-based models

Abstract

Flash point of tailor-made green diesel is an important property for safety regulation. Based on the previous analysis, the prediction accuracy of the Liaw model through UNIFAC-type models is found to be satisfactory for the mixtures of B5 palm oil biodiesel with ester and ether, except for B5-alcohol blends. To fill up the research gap, the aim of this study is to improve the prediction efficiency of the model for green diesel blends containing alcohol. The improvement is done by adjusting the group interaction parameters for Original-UNIFAC and NIST-UNIFAC model according to the experimental flash point data. A significant improvement of prediction results were obtained with a reduction of the prediction errors (calculated using the average absolute relative deviation - AARD) from about 7.32 and 6.39 % for Original-UNIFAC and NIST-UNIFAC to around 1.2 % for both models using the revised group interaction parameter set that containing the revised parameters of alcohol and alkyl chains group. Overall, the prediction accuracies obtained by using Original-UNIFAC and NIST-UNIFAC model are similar when revised group interaction parameters are used