Kinetic modeling of catalytic esterification of non-edible macauba pulp oil using macroporous cation exchange resin

Abstract

Conversion of non-edible oils through heterogeneous catalysis and their kinetic aspects are essential in order to produce economically viable biodiesel. Especially for oils containing high Free Fatty Acids (FFA) content since those cannot be viably converted through homogenous catalysis. Herein, the heterogeneous catalytic esterification kinetics of macauba pulp acid oil was exploited. The reaction tests were conducted in a stirred batch reactor with ethanol, and a cation exchange resin (Purolite® CT275)as a catalyst. The reaction kinetics was modeled based on a pseudo-homogeneous (PH) model of second order, showing good agreement with the experimental data acquired under different reaction conditions. The reaction progress was monitored by validated HPLC and GC methods. The internal and external mass transfer resistances were evaluated, and show that the rate limiting step is the surface reaction. To assess the catalyst stability, scanning electron microscopy was performed on both fresh and spent (after 10 reaction cycles) catalysts; reuse of catalyst was conducted without further treatment. The catalyst recycling demonstrates good performance where 85% of its performance was retained, and the SEM images show that the materials were structurally stable. Because the Pseudo-Homogeneous model properly described the kinetic aspects of the esterification process it was possible to estimate thermodynamic parameters by using the Arrhenius plot. The activation energy of the forward reaction was determined as 44.0 kJ/mol, while for the reverse reaction was 11.7 kJ/mol. Therefore, the kinetic and thermodynamic descriptions along with the catalyst stability upon several cycles without any further treatment reflect the viability of these resins for industrial application in the energy sector. Furthermore, combining these features with the low cost of macauba pulp acid oil, we conclude that the process is suitable for scaling up studies.