Simulation of an Industrial-Scale Reactive Liquid–Liquid Extraction Tower Using Polar PC-SAFT Toward Understanding and Improving the Hydrolysis of Triglycerides

Abstract

A comprehensive model for simulation and optimization of industrial-scale splitting towers that is able to predict the yield for the hydrolysis of bio-based triglyceride feedstocks is presented in this work. This model includes a variable glycerol equilibrium ratio, which is a function of the composition and temperature and is calculated using the polar version of the perturbed chain statistical association fluid theory (PC-SAFT), the autocatalytic effect of fatty acids in hydrolysis, and isomerization of poly-unsaturated fatty acids. Model validation is performed using process data from three real-life splitting towers covering four feedstock types, i.e., tallow, rapeseed oil, palm oil, and palm fatty acid distillate. Due to the composition gradients of the organic phase throughout the tower, it is crucial to properly account for the changes in the glycerol equilibrium ratio. The importance of feedstock flow rate, water/oil ratio, and temperature profile throughout the tower is analyzed and confirmed by sensitivity analysis. Our results show that modifying the temperature profile may shift the reaction equilibrium toward the fatty acid product. This knowledge is crucial for improving the energy and resource efficiency of fatty acid production, thereby improving its economic and environmental sustainability.

A Predictive Approach towards Using PC-SAFT for Modeling the Properties of Shale Oil

“…In the last two decades, it has become more common to use equations of state based on statistical associating fluid theory (SAFT), especially perturbed-chain statistical associating fluid theory (PC-SAFT), for modeling oils, including bio-oils [1,[13][14][15][16][17][18]. Moreover, several studies indicate the importance and applicability of these equations in the energy industry [19][20][21].…”