Tubulações de PRFV com adição de areia quartzosa visando sua aplicação na indústria do petróleo

Fillers are often added in composites to enhance performance and/or to reduce cost. Fiberglass pipes must meet performance requirements and industrial sand is frequently added for the pipe to be cost competitive. The sand is added to increase pipe wall thickness, thus increase pipe stiffness. The main goal of the present work is to conduct an experimental investigation between pipes fabricated with and without de addition of sand, to be used in the petroleum industry. Pipes were built using E-glass fibers, polyester resin and siliceous sand. The fabrication process used hand lay up and filament winding and was divided in two different parts: the liner and the structural wall. All tested pipes had the same liner, but different structural wall composition, which is the layer where siliceous sand may be added or not. The comparative investigation was developed considering the results of longitudinal tensile tests, hoop tensile tests, hydrostatic pressure leak tests and parallel-plate loading stiffness tests. SEM was used to analyze if the sand caused any damage to the glass fibers, during the fabrication process, because of the fiber-sand contact. The procedure was also used to verify the composite conditions after the hydrostatic pressure leak test. The results proved that the addition of siliceous sand reduced the leak pressure in about 17 %. In the other hand, this loss in pressure was compensated by a stiffness increment of more than 380 %. MEV analyses show that it is possible to find damage on the fiber-sand contact, but on a very small amount. On most cases, the contact occurs without damage evidences. In summary, the addition of sand filler represented a 27.8 % of cost reduction, when compared to a pipe designed with glass fiber and resin only. This cost reduction combined to the good mechanical tests results make siliceous sand filler suitable for fiberglass pressure pipes

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