Techno-economic analysis of monoglyceride and diglyceride production from waste cooking oil

Abstract

Monoglycerides (MGs) and their derivatives are widely used and considered the most important group of emulsifiers. These emulsifiers can be produced from waste cooking oils (WCOs) via two-step transesterification-glycerolysis or hydrolysis-glycerolysis processes. The transesterification-glycerolysis process has the advantages in terms of a high reaction rate and a high process yield. For hydrolysis, triglycerides are converted to free fatty acids (FFA) at higher reaction temperature, but water is used as a reactant. Therefore, the separation process is not complicated. This study simulated the techno-economic comparison of four different scenarios of MG and DG production using ASPEN Plus V11 simulation software. In both scenarios I and IV, MG and DG are synthesized from WCO via transesterification and glycerolysis. Scenario I used a homogenous catalyst and scenario IV used a heterogenous catalyst. In scenarios II and III, MG and DG are synthesized from WCO via hydrolysis and glycerolysis. Scenario III, the make-up glycerol was added to achieve the glycerol to FFA molar ratio of 1:1 for improvement from scenario II. From the simulation results, transesterification-glycerolysis processes provided higher MG production than that of hydrolysis-glycerolysis process. The maximum MG productivity was obtained from the scenario I while the scenario IV can reduce one distillation for separation and recycle of unreacted glycerol to reduced energy consumption in the glycerolysis and purification sections as compared with scenario I. Moreover, the make-up glycerol for glycerolysis in scenario III increased overall energy consumption as well as productivity of MG compared with scenario II. The scenario IV presented the lowest COMd and the minimum MG production cost. The MG production cost per ton of MG was lower than the current MG price for scenarios I, III, and IV, indicating that these processes can be feasible economically. Moreover, by-product price was the most sensitive parameter for scenarios I, III, and IV.