Temporal Analysis of Sunflower Oil Transesterification with Ionic Liquid [N4444][L-arg]: Evaluation of Kinetics and Catalyst Efficiency

• Grant number: 25/04356-1
• Support Opportunities: Scholarships abroad - Research
• Start date: December 15, 2025
• End date: February 14, 2026
• Field of knowledge: Engineering - Chemical Engineering - Chemical Technology
• Principal Investigator: Daniela Helena Pelegrine Guimarães
• Grantee: Daniela Helena Pelegrine Guimarães
• Host Investigator: Maria Isabel da Silva Nunes
• Host Institution: Escola de Engenharia de Lorena (EEL). Universidade de São Paulo (USP). Lorena , SP, Brazil
• Institution abroad: Universidade de Aveiro (UA), Portugal

Abstract

The growing energy demand drives biodiesel production, with amine-based ionic liquids standing out as promising catalysts due to their low cost and simple synthesis. PhD student Luís Alberto Gallo-García, supervised by the proposer and in collaboration with the host researcher at the University of Aveiro, recently conducted a study on the transesterification of sunflower oil using the ionic liquid L-argininate tetrabutylammonium ([N4444][L-arg]). The study investigated various experimental conditions, such as temperature, reaction time, oil/methanol (O/M) molar ratio, and catalyst dosage, with the yield of FAME (fatty acid methyl esters) as the response variable. Based on the results obtained, it is proposed to continue the research, focusing on the temporal analysis of the transesterification process. The plan includes experiments with different FAME conversion conditions (optimal, high, medium, and low) and parameters such as temperature, reaction time, O/M molar ratio, and catalyst dosage. Samples will be collected at intervals from 0 to 100 minutes to monitor the evolution of triglycerides, diglycerides, monoglycerides, and FAME by NMR. The goal is to quantify the conversion of triglycerides, evaluate the efficiency of the [N4444][L-arg] catalyst, and determine the ideal time to maximize biodiesel yield. The data will provide insights into the process kinetics and catalyst behavior, with mathematical modeling of transesterification allowing the prediction of the reaction profile, guiding experiments, and simulating conditions, offering a better fit than traditional models.