Selection of Prediction Methods for Thermophysical Properties for Process Modeling and Product Design of Biodiesel Manufacturing

Yung-Chieh Su, Y. A. Liu, Carlos Axel Díaz Tovar, Rafiqul Gani

Research output: Contribution to journalJournal articleResearchpeer-review


To optimize biodiesel manufacturing, many reported studies have built simulation models to quantify the relationship between operating conditions and process performance. For mass and energy balance simulations, it is essential to know the four fundamental thermophysical properties of the feed oil: liquid density (ρL), vapor pressure (Pvap), liquid heat capacity (CPL), and heat of vaporization (ΔHvap). Additionally, to characterize the fuel qualities, it is critical to develop quantitative correlations to predict three biodiesel properties, namely, viscosity, cetane number, and flash point. Also, to ensure the operability of biodiesel in cold weather, one needs to quantitatively predict three low-temperature flow properties: cloud point (CP), pour point (PP), and cold filter plugging point (CFPP). This article presents the results from a comprehensive evaluation of the methods for predicting these four essential feed oil properties and six key biodiesel fuel properties. We compare the predictions to reported experimental data and recommend the appropriate prediction methods for each property based on accuracy, consistency, and generality. Of particular significance are (1) our presentation of simple and accurate methods for predicting the six key fuel properties based on the number of carbon atoms and the number of double bonds or the composition of total unsaturated fatty acid methyl esters (FAMEs) and (2) our posting of the Excel spreadsheets for implementing all of the evaluated accurate prediction methods on our group Web site ( for the reader to download without charge.
Original languageEnglish
JournalIndustrial & Engineering Chemistry Research
Issue number11
Pages (from-to)6809-6836
Publication statusPublished - 2011


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