首页|Thermodynamically predicting liquid/solid phase change of long-chain fatty acid methyl esters (FAMEs) and its application in evaluating the low-temperature performance of biodiesel

Thermodynamically predicting liquid/solid phase change of long-chain fatty acid methyl esters (FAMEs) and its application in evaluating the low-temperature performance of biodiesel

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Background: Long-chain FAMEs, also called biodiesel, are viewed as the alternative to petroleum diesel for renewability and sustainability. Because the fatty acid profiles significantly varied for the sources, the compositions significantly affected the biodiesel properties, such as cloud points. The cloud point of biodiesel indicates the phenomenon of solid-liquid phase change. Previous models for cloud point predictions were limited to known components in the mixtures. Methods: The cloud points of the binary, ternary and multicomponent mixtures of fatty acid methyl esters were measured in this study. A cloud point prediction model was established based on phase equilibrium with the modified Universal functional activity coefficient (UNIFAC) model and excess fusion enthalpy for predicting the nonideal behaviors of the components resulting from the molecular shape and molecule interactions. Significant findings: The developed thermodynamic model accurately predicts the cloud point according to compositions. The model extends the application scope to the low-temperature range. There had eutectic points in the phase diagrams when the mixtures consisted of either saturated FAMEs or unsaturated FAMEs. This study proved that saturated FAMEs regulate the cloud points, but unsaturated FAMEs affect them through group interactions. Moreover, the proposed model can predict unknown FAMEs mixture as it is built on the group contributions. (c) 2022 Taiwan Institute of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

BiodieselThermodynamicUNIFAC modelModified UNIFAC modelCloud pointCOLD FLOWCLOUD POINTCRYSTALLIZATIONEQUILIBRIADIESELMODELIMPROVEMENTENTHALPIESMIXTURES

Liu, Junli、Tao, Bernard

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Purdue Univ

2022

Journal of the Taiwan Institute of Chemical Engineers

Journal of the Taiwan Institute of Chemical Engineers

EISCI
ISSN:1876-1070
年,卷(期):2022.135
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