Análise estrutural da deformação cenozóica na Bacia de Cumuruxatiba (BA)

The Cumuruxatiba basin is located at the southern coast State of Bahia in northeastern of Brazil. This basin was formed in distensional context, with rifting and subsequent thermal phase during Neocomian to late Cretaceous. At Cenozoic ages, the Abrolhos magmatism occurs in the basin with peaks during the Paleocene and Eocene. In this period, there was a kinematic inversion in the basin represented by folds related to reverse faults. Structural restoration of regional 2D seismic sections revealed that most of the deformation was concentrated at the beginning of the Cenozoic time with the peak at the Lower Eocene. The post-Eocene is marked by a decrease of strain rate to the present. The 3D structural modeling revealed a fold belt (trending EW to NE-SW) accommodating the deformation between the Royal Charlotte and Sulphur Minerva volcanic highs. The volcanic eruptions have caused a differential overburden on the borders of the basin. This acted as the trigger for halokinesis, as demonstrated by physical modeling in literature. Consequently, the deformation tends to be higher in the edges of the basin. The volcanic rocks occur mainly as concordant structures (sills) in the syn-tectonic sediment deposition showing a concomitant deformation. The isopach maps and diagrams of axis orientation of deformation revealed that most of the folds were activated and reactivated at different times during the Cenozoic. The folds exhibit diverse kinematic patterns over time as response to behavior of adjacent volcanic highs. These interpretations allied with information on the petroleum system of the basin are important in mapping the prospects for hydrocarbons

Recuperação e purificação de quitosanases usando adsorção em leito expandido com streamline DEAE com modelagem e simulação usando redes neurais

Expanded Bed Adsorption (EBA) is an integrative process that combines concepts of chromatography and fluidization of solids. The many parameters involved and their synergistic effects complicate the optimization of the process. Fortunately, some mathematical tools have been developed in order to guide the investigation of the EBA system. In this work the application of experimental design, phenomenological modeling and artificial neural networks (ANN) in understanding chitosanases adsorption on ion exchange resin Streamline® DEAE have been investigated. The strain Paenibacillus ehimensis NRRL B-23118 was used for chitosanase production. EBA experiments were carried out using a column of 2.6 cm inner diameter with 30.0 cm in height that was coupled to a peristaltic pump. At the bottom of the column there was a distributor of glass beads having a height of 3.0 cm. Assays for residence time distribution (RTD) revelead a high degree of mixing, however, the Richardson-Zaki coefficients showed that the column was on the threshold of stability. Isotherm models fitted the adsorption equilibrium data in the presence of lyotropic salts. The results of experiment design indicated that the ionic strength and superficial velocity are important to the recovery and purity of chitosanases. The molecular mass of the two chitosanases were approximately 23 kDa and 52 kDa as estimated by SDS-PAGE. The phenomenological modeling was aimed to describe the operations in batch and column chromatography. The simulations were performed in Microsoft Visual Studio. The kinetic rate constant model set to kinetic curves efficiently under conditions of initial enzyme activity 0.232, 0.142 e 0.079 UA/mL. The simulated breakthrough curves showed some differences with experimental data, especially regarding the slope. Sensitivity tests of the model on the surface velocity, axial dispersion and initial concentration showed agreement with the literature. The neural network was constructed in MATLAB and Neural Network Toolbox. The cross-validation was used to improve the ability of generalization. The parameters of ANN were improved to obtain the settings 6-6 (enzyme activity) and 9-6 (total protein), as well as tansig transfer function and Levenberg-Marquardt training algorithm. The neural Carlos Eduardo de Araújo Padilha dezembro/2013 9 networks simulations, including all the steps of cycle, showed good agreement with experimental data, with a correlation coefficient of approximately 0.974. The effects of input variables on profiles of the stages of loading, washing and elution were consistent with the literature