Modeling of Basil Essential Oil Extraction With Variable Diffusion Coefficient

Abstract

Essential oils are complex mixtures of volatile compounds found at low levels in aromatic plant specific parts. Soluble in organic solvents, these oils are characterized by their fragrance and antimicrobial activity, being widely used in industries. The mathematical modeling of essential oil extraction is a relevant step in the project of industrial plants because it is about achieving optimized operational conditions. A model is available in the literature describing the extraction process of basil essential oil, based on diffusion within the leaf. Parametric equations were tested for the variable diffusion coefficient with the concentration, resulting in a significant improvement in the description process, when compared to papers presented in the literature.

Simulation of a single spherical droplet drying considering shrinking and temperature profiles: first drying stage

“…A strategy that can also be adopted to improve the performance of the proposed model is to seek to predict the variation of parameters through empirical correlations. Silveira et al (2017) tested several parametric equations for the variable diffusion coefficient with the concentration, obtaining a significant improvement in the description of the process, when compared with works presented in the literature.…”

Comparing different methods for estimating kinetic parameters of whey protein heat-induced denaturation in infant milk formulas

“…The parameters errors were calculated according to Bates and Watts (1988) from the standard deviation of each parameter, with assumptions of linearity of the model in the neighborhood of the optimal solution, and residuals of mean 0 and constant variance. The standard deviations were obtained as described by Silveira et al (2017) and by Bates and Watts (1988).…”

The Role of Char Formation in Equilibrium Modeling of Gasification Processes

“…Mathematical modeling is a useful tool for the investigation of such thermal processes, as it can be used to study a given system without the need to run several experimental tests (Fournel et al, 2015). The models can be built by studying the system behavior through equations that describe the physical and chemical phenomena involved in the process, such as Computational Fluid Dynamics or transient models (Silveira et al, 2017;Rajh et al, 2016;Siqueira et al, 2012). Although these types of models can describe several phenomena, they require considerable detailed information about the system and computational resources.…”