This work reports high-pressure cloud point data for the quaternary system glycerol + olive oil + n-butane + AOT surfactant. The static synthetic method, using a variable-volume view cell, was employed for obtaining the experimental data at pressures up to 27 MPa. The effects of glycerol/olive oil concentration and surfactant addition on the pressure transition values were evaluated in the temperature range from 303 K to 343 K. For the system investigated, vapor-liquid (VLE), liquid-liquid (LLE) and vapor-liquid-liquid (VLLE) equilibrium were recorded. It was experimentally observed that, at a given temperature and surfactant content, an increase in the concentration of glycerol/oil ratio led to a pronounced increase in the slope of the liquid-liquid coexistence curve. A comparison with results reported for the same system but using propane as solvent showed that much lower pressure transition values are obtained when using n-butane.

Minimization of Gibbs free energy using activity coefficient models and nonlinear equation solution techniques is commonly applied to phase stability problems. However, when conventional techniques, such as the Newton-Raphson method, are employed, serious convergence problems may arise. Due to the existence of multiple solutions, several problems can be found in modeling liquid-liquid equilibrium of multicomponent systems, which are highly dependent on the initial guess. In this work phase stability analysis of liquid-liquid equilibrium is investigated using the NRTL model. For this purpose, two distinct stochastic numerical algorithms are employed to minimize the tangent plane distance of Gibbs free energy: a subdivision algorithm that can find all roots of nonlinear equations for liquid-liquid stability analysis and the Simulated Annealing method. Results obtained in this work for the two stochastic algorithms are compared with those of the Interval Newton method from the literature. Several different binary and multicomponent systems from the literature were successfully investigated.

This work reports the effects of temperature (20 to 50ºC) and pressure (100 to 250 bar) on the extraction yield, chemical characteristics and antimicrobial activity of extracts of Peumus boldus Molina obtained by compressed carbon dioxide extraction. Results showed that the extraction variables affect the extraction yield and the chemical distribution of the major compounds present in the extracts. The extracts were chemically analyzed with regard to 1,8-cineole, trans-sabinene, pinocarveol, pinocarvone, 4-terpineol, ascaridole, piperitone oxide, limonene dioxide and n-eicosane in a GC/MSD. Antimicrobial tests demonstrated that the high-pressure CO2 extracts had activity against 13 bacteria and that better action was verified with extracts obtained at a lower CO2 extraction density and a higher temperature.

Phase equilibrium calculations at high pressures have been a continuous challenge for scientists and engineers. Traditionally, this task has been performed by solving a system of nonlinear algebraic equations originating from isofugacity equations. The reliability and accuracy of the solutions are strongly dependent on the initial guess, especially due to the fact that the phase equilibrium problems frequently have multiple roots. This work is focused on the application of a subdivision algorithm for thermodynamic calculations at high pressures. The subdivision algorithm consists in the application of successive subdivisions at a given initial interval (rectangle) of variables and a systematic test to verify the existence of roots in each subinterval. If the interval checked passes in the test, then it is retained; otherwise it is discharged. The algorithm was applied for vapor-liquid, solid-fluid and solid-vapor-liquid equilibrium as well as for phase stability calculations for binary and multicomponent systems. The results show that the proposed algorithm was capable of finding all roots of all high-pressure thermodynamic problems investigated, independent of the initial guess used.

Rapid expansion of supercritical solution (RESS) is a promising technique for microparticle production. The literature points out that the RESS technique can be applied to process a wide range of materials including ceramics, polymers, biopolymers, pharmaceutics, and organic compounds. In order to achieve an adequate understanding of the RESS process, it is necessary to conduct more comprehensive studies involving the hydrodynamic modeling of the fluid flow through the capillary, the phase behavior of a expanding mixture, and also with regard to the microparticle formation mechanism. In this context, this work is focused on the analysis of process hydrodynamic modeling and on phase equilibrium modeling (thermodynamic modeling of supersaturation), with a phase stability test coupled. The Gibbs tangent plane distance for the phase stability test was minimized using the simulated annealing algorithm. The results showed that the thermodynamic variables exert a remarkable effect simulated variables (temperature, velocity, density and degree of supersaturation) for the profile.

Neural networks and hybrid models were used to study substrate and product inhibition observed in the enzymatic hydrolysis of cellobiose at 40ºC, 50ºC and 55ºC, pH 4.8, using cellobiose solutions with or without the addition of exogenous glucose. Firstly, the initial velocity method and nonlinear fitting with Statistica<FONT FACE=Symbol>Ò</FONT> were used to determine the kinetic parameters for either the uncompetitive or the competitive substrate inhibition model at a negligible product concentration and cellobiose from 0.4 to 2.0 g/L. Secondly, for six different models of substrate and product inhibitions and data for low to high cellobiose conversions in a batch reactor, neural networks were used for fitting the product inhibition parameter to the mass balance equations derived for each model. The two models found to be best were: 1) noncompetitive inhibition by substrate and competitive by product and 2) uncompetitive inhibition by substrate and competitive by product; however, these models’ correlation coefficients were quite close. To distinguish between them, hybrid models consisting of neural networks and first principle equations were used to select the best inhibition model based on the smallest norm observed, and the model with noncompetitive inhibition by substrate and competitive inhibition by product was shown to be the best predictor of cellobiose hydrolysis reactor behavior.