Estimating Thermodynamic Properties of Pure Triglyceride Systems Using the Triglyceride Property Calculator

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• 作者：Arun S. Moorthy ; Rong Liu…
• 关键词：Empirical modeling ; Numerical simulation ; Solid–liquid equilibrium ; Triglyceride properties
• 刊名：Journal of the American Oil Chemists' Society
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：94
• 期：2
• 页码：187-199
• 全文大小：
• 刊物类别：Chemistry and Materials Science
• 刊物主题：Industrial Chemistry/Chemical Engineering; Biomaterials; Agriculture; Food Science; Biotechnology;
• 出版者：Springer Berlin Heidelberg
• ISSN：1558-9331
• 卷排序：94

文摘

To date, the most comprehensive model for predicting thermodynamic properties of pure triglycerides was presented by Wesdorp in “Liquid-multiple solid phase equilibria in fats: theory and experiments” (1990). In this paper, we present (1) corrections to the published model, as well as (2) a software implementation of the model for numerical assessment. The software tool, Triglyceride Property Calculator (TPC), uses a semi-empirical model to estimate the enthalpy of fusion and melting temperature for a given triglyceride based on its molecular composition and polymorphic form. These estimates are compared to experimentally collected data when available. The web application is available at http://www.crcfoodandhealth.com (under research tools) and through the AOCS Lipid Library. The quality of estimates is characterized according to defined counting metrics and presented for TAG subcategories. Additionally, the extrapolative value of the TPC is assessed by checking for consistency with underlying thermodynamic constraints. The current TPC implementation is effective in describing experimentally collected melting point data, with greater than 91% of the fitted values falling within 10% of the actual data. The TPC is also very good at describing collected enthalpy data. The underlying semi-empirical model and parameter set perform well in ensuring enthalpy predictions are thermodynamically consistent, however, extrapolated melting temperatures appear unreliable. Developing models and parameter sets that ensure thermodynamic consistency is a priority with future TPC iterations.