A predictive approach in modeling and simulation of heat and mass transfer during microwave heating. Application to SFME of essential oil of Lavandin Super

• Published in: Chemical engineering science 2012-01, Vol.68 (1), p.192-201
• Main Authors: Navarrete, A., Mato, R.B., Cocero, M.J.
• Format: Article
• Language: English
• Subjects: Applied sciences; Biological and medical sciences; Chemical engineering; Computer simulation; energy efficiency; Energy use; equations; Essential oils; Evaporation; Exact sciences and technology; Extraction; Food engineering; Food industries; Food processing; Fundamental and applied biological sciences. Psychology; General aspects; heat; Heat and mass transfer. Packings, plates; Heat transfer; Heating; mass transfer; Mathematical models; Microwave heating; microwave treatment; Microwaves; Simulation; simulation models
• ISSN: 0009-2509; 1873-4405
• DOI: 10.1016/j.ces.2011.09.026

Solvent Free Microwave Extraction (SFME) is a new and faster process with higher energy efficiency used to extract essential oils. Nevertheless, so far no modeling efforts have been found on the subject. In this work a predictive modeling approach to the microwave extraction of essential oils applied to the SFME is proposed. The model includes the interaction between electromagnetic energy and the material through the use of Maxwell's equations together with estimation of the dielectric constants. The evaporation of water from the plant is also simulated. The results indicate that it is possible to model this process with a predictive approach of the properties of the system. The microwave heating is underpredicted in the area closer to the walls, thus the evaporation resulting in simulations is lower than the measured values. Nevertheless, the results show a behavior very similar to the experimental values. ▶ First predictive 3D model of extraction of essential oils with microwaves. ▶ General behavior of the system and hot spots are predicted. ▶ Transport of the vapor produced by microwave heating will improve accuracy. ▶ Predictions are better at higher power values. ▶ Moisture is the variable of the plant that most influences the process.