Modeling and Design of Renewable Propane Production Through Hydrotreatment of Vegetable Oils

Abstract

With the growing demand for sustainable energy solutions, renewable propane (rC3) can be a suitable alternative to fossil liquefied petroleum gas (LPG), with potential lower environmental impacts. This study aims to design and simulate a rC3 production process via hydrotreatment of vegetable oils (HVO) to access the technical performance of this route. A comparison between various feedstocks (soybean, sunflower, canola, and palm oils) and downstream processes, namely, cryogenic distillation and chemical absorption, is discussed. The results were evaluated in terms of the key performance parameters: rC3 yield, specific hydrogen consumption, specific energy consumption, and CO₂ emissions. Moreover, an artificial neural network (ANN) model was developed to predict the key performance parameters based on the triglyceride composition of vegetable oils. The rC3 yield was close to 5 wt% for all vegetable oils, and the highest yield was obtained via palm oil hydrotreatment. The rC3 purity obtained in both separation processes was greater than 90%, with chemical absorption separation resulting in lower CO₂ emissions and lower energy consumption than the cryogenic distillation process. The ANN application for predicting the key performance parameters based on triglyceride composition presented correlation agreement > 0.9930 with the simulation results.