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

Large-scale recovery of C-phycocyanin from Spirulina platensis using expanded bed adsorption chromatography

C-phycocyanin was purified on a large scale by a combination of expanded bed adsorption, anion-exchange chromatography and hydroxyapatite chromatography from inferior Spirulina platensis that cannot be used for human consumption. First, phycobiliproteins were extracted by a simple, scaleable method and then were recovered by Phenyl-Sepharose chromatography in an expanded bed column. The purity (the A(620)/A(280) ratio) of C-phycocyanin isolated with STREAMLINE (TM) Column was up to 2.87, and the yield was as high as 31 mg/g of dried S. platensis. After the first step, we used conventional anion-exchange chromatography for the purification steps, with a yield of 7.7 mg/g of dried S. platensis at a purity greater than 3.2 and with an A(620)/A(650) index higher than 5.0. The fractions from anion-exchange chromatography with a level of purity that did not conform to the above standard were subjected to hydroxyapatite chromatography, with a C-PC yield of 4.45 mg/g of dried S. platensis with a purity greater than 3.2. The protein from both purification methods showed one absolute absorption peak at 620 nm and a fluorescence maximum at 650 nm, which is consistent with the typical spectrum of C-phycocyanin. SDS-PAGE gave two bands corresponding to 21 and 18 kDa. In-gel digestion and LC-ESI-MS showed that the protein is C-phycocyanin. (c) 2006 Elsevier B.V. All rights reserved.

Isolation and purification of phycoerythrin from red alga Garcilaria verrucosa by expanded-bed-adsorption and ion-exchange chromatogaphy

R-phycoerythrin was isolated and purified from Gracilaria verrucosa on an expanded-bed adsorption column combined with ion-exchange chromatography, which can effectively solve the problem of blockage of chromatographic columns due to polysaccharides during isolation and purification of phycobiliproteins. 0.1 M (NH4)(2)SO4 proved best to elute R-phycoerythrin from the expanded-bed column, and desalted 0.1 M (NH4)(2)SO4 eluate was used on an ion-exchange column to purify the R-phycoerythrin. Using this two-stage chromatography, the purity (OD565/OD280) of the R-phycoerythrin from G. verrucosa is increased to 4.4, and the yield of purified R-phycoerythrin can reach 0.141 mg . g(-1) of the frozen alga.

Large-scale isolation and purification of R-phycoerythrin from red alga Palmaria palmata using the expanded bed adsorption method

R-phycoerythrin, a light-harvesting protein in some marine algae, and can be widely used in medicine, was isolated and purified from a red alga, Palmaria palmata (Lannaeus) Kuntze, using the streamline column (expanded bed adsorption) combined with ion-exchange chromatography. Because the crude extract was applied to the column upwardly, the column would not be blocked by polysaccharides usually very abundant in the extract of marine alga, this kind of blockage could hardly lie overcome in ordinary chromatographic column. After applying the crude extract containing 0.5 mol/L (NH4)(2)SO4, (NH4)(2)SO4 solution of different concentrations (0.2 mol/L, 0.1 mol/L and 0.05 mol/L) was used to elute the column downwardly and the eluates were collected and desalted. The desalted eluates were then applied onto all ion-exchange chromatographic column loaded with Q-sepharose for further purification of the R-phycoerythrin. Through these two steps, the purity (OD565/OD280) of the R-phycoerythrin from P. palmata was up to 3.5, more than 3.2, the commonly accepted criterion for purity, and the yield of the purified R-phycoerythrin could reach 0.122 mg/g of frozen P. palmata, much higher than that of phycobiliproteins purified with the previous methods. The result indicated that the cost of R-phycoerythrin will drop down with the method reported in this article.

Isolation of monocytes from human peripheral blood using immuno-affinity expanded-bed adsorption

A novel technique for the separation of monocytes from human peripheral blood preparations has been developed. The technique is based on the use of expanded-bed adsorption and a solid perfluorocarbon derivatized with avidin or streptavidin for the indirect positive or negative capture of cells labeled with biotinylated monoclonal antibodies. The perfluorocarbon support was prepared and characterized and the contactor design and operating conditions, that enable cells to be selectively isolated, were investigated. Experiments consisted of applying an immunolabeled pulse of 1 x 10(8) peripheral blood mononuclear cells (PBMCs), isolated by density gradient centrifugation, directly onto a refrigerated expanded bed. The major cell types remaining were T-lymphocytes, B-lymphocytes, and monocytes. Monocytes could be positively adsorbed, following labeling with anti-CD14 mAb, with a clearance of up to 89% and a depletion factor of 7.6. They could also be

Recovery of the solitons using a lattice Boltzmann model

We formulate a lattice Boltzmann model which simulates Korteweg-de Vries equation by using a method of higher moments of lattice Boltzmann equation. Using a series of lattice Boltzmann equations in different time scales and the conservation law in time scale to, we obtain equilibrium distribution function. The numerical examples show that the method can be used to simulate soliton.

Detecting the effects of diversity on measures of ecosystem function: experimental design, null models and empirical observations

Here we present a novel experimental approach to examine the relationship between diversity and ecosystem Function. We develop four null predictive models, with which to differentiate between the 'sampling effect' - the chance inclusion of a highly productive species, and 'species complementarity' - the complementary use of resources by species that differ in their niche or resource use. We investigate the effects of manipulating species and functional richness on ecosystem function in marine benthic system and using empirical data from our own experiments we illustrate the application of these models.

Real-time application of a constrained predictive controller based on dynamic neural networks with feedback linearization

This paper describes an experimental application of constrained predictive control and feedback linearisation based on dynamic neural networks. It also verifies experimentally a method for handling input constraints, which are transformed by the feedback linearisation mappings. A performance comparison with a PID controller is also provided. The experimental system consists of a laboratory based single link manipulator arm, which is controlled in real time using MATLAB/SIMULINK together with data acquisition equipment.

Estimation of electrical machine speed using sensorless technology and neural networks

The use of sensorless technologies is an increasing tendency on industrial drivers for electrical machines. The estimation of electrical and mechanical parameters involved with the electrical machine control is used very frequently in order to avoid measurement of all variables related to this process. The cost reduction may also be considered in industrial drivers, besides the increasing robustness of the system, as an advantage of the use of sensorless technologies. This work proposes the use of a recurrent artificial neural network to estimate the speed of induction motor for sensorless control schemes using one single current sensor. Simulation and experimental results are presented to validate the proposed approach. ©2008 IEEE.

Damage identification and condition assessment of building structures using frequency response functions and neural networks

This thesis investigated the viability of using Frequency Response Functions in combination with Artificial Neural Network technique in damage assessment of building structures. The proposed approach can help overcome some of limitations associated with previously developed vibration based methods and assist in delivering more accurate and robust damage identification results. Excellent results are obtained for damage identification of the case studies proving that the proposed approach has been developed successfully.

A new family of soft transition converters: Design and dynamic model

The soft switching converters evolved through the resonant load, resonant switch, resonant transition and active clamp converters to eliminate switching losses in power converters. This paper briefly presents the operating principle of the new family of soft transition converters; the methodology of design of these converters is presented through an example. In the proposed family of converters, the switching transitions of both the main switch and auxiliary switch are lossless. When these converters are analysed in terms of the pole current and throw voltage, the defining equations of all converters belonging to this family become identical.Such a description allows one to define simple circuit oriented model for these converters. These circuit models help in evaluating the steady state and dynamic model of these converters. The standard dynamic performance functions of the converters are readily obtainable from this model. This paper presents these dynamic models and verifies the same through measurements on a prototype converter.

Prediction of Ground Water Levels in the Uplands of a Tropical Coastal Riparian Wetland using Artificial Neural Networks

Artificial Neural Networks (ANNs) have been found to be a robust tool to model many non-linear hydrological processes. The present study aims at evaluating the performance of ANN in simulating and predicting ground water levels in the uplands of a tropical coastal riparian wetland. The study involves comparison of two network architectures, Feed Forward Neural Network (FFNN) and Recurrent Neural Network (RNN) trained under five algorithms namely Levenberg Marquardt algorithm, Resilient Back propagation algorithm, BFGS Quasi Newton algorithm, Scaled Conjugate Gradient algorithm, and Fletcher Reeves Conjugate Gradient algorithm by simulating the water levels in a well in the study area. The study is analyzed in two cases-one with four inputs to the networks and two with eight inputs to the networks. The two networks-five algorithms in both the cases are compared to determine the best performing combination that could simulate and predict the process satisfactorily. Ad Hoc (Trial and Error) method is followed in optimizing network structure in all cases. On the whole, it is noticed from the results that the Artificial Neural Networks have simulated and predicted the water levels in the well with fair accuracy. This is evident from low values of Normalized Root Mean Square Error and Relative Root Mean Square Error and high values of Nash-Sutcliffe Efficiency Index and Correlation Coefficient (which are taken as the performance measures to calibrate the networks) calculated after the analysis. On comparison of ground water levels predicted with those at the observation well, FFNN trained with Fletcher Reeves Conjugate Gradient algorithm taken four inputs has outperformed all other combinations.