Predictive Modeling for Kinetics of Ethyl Ester Production from Vegetable Oils

Transesterification is the most used reaction for production of fatty esters. Fatty esters can be used in food industry applications, as in the fatty acid production and in the emulsifiers generation. The transesterification occurs in three reversible steps with the production of di- and monoacylglycerols as intermediate components, and glycerol and ester as final products. The study of the kinetics of the transesterification reaction is important to improve the production of fatty esters, to evaluate the influence of the process parameters and to use these data in simulations of new plant configurations. Previous works consider a kinetic model for each specific oil, so it is not possible to generalize the parameters for different fatty sources. This work aims to the development a unique generic model to predict kinetic parameters for the transesterification of different oils. The proposed model was adjusted considering kinetic parameters obtained from the transesterification of different fatty sources (soybean, palm, sunflower, canola, micro-algae and trioctanoate) using a molar ratio of ethanol to oil of 9:1 and 1.0 wt% of sodium ethoxide as catalyst under stirring of 400 rpm. The proposed model considers the molecular weight and the iodine value as set parameters and it was fitted using the Pyomo library available at Python language. The mean relative error was 3%, with a variation in the relative error associated with the kinetic parameters varying in the range of 0.1 to 14%, demonstrating that the model is suitable for the prediction of the kinetic reaction constants of ethanolysis for different vegetable oils. The model proposed by means of a simple equation is able to adjust the kinetic parameters considering only the index of iodine and the molecular weight, and it can be an important tool for simple and fast prediction of kinetic data to be used in process simulation.