Harmonic identification using parallel neural networks in single-phase systems

In this paper, artificial neural networks are employed in a novel approach to identify harmonic components of single-phase nonlinear load currents, whose amplitude and phase angle are subject to unpredictable changes, even in steady-state. The first six harmonic current components are identified through the variation analysis of waveform characteristics. The effectiveness of this method is tested by applying it to the model of a single-phase active power filter, dedicated to the selective compensation of harmonic current drained by an AC controller. Simulation and experimental results are presented to validate the proposed approach. (C) 2010 Elsevier B. V. All rights reserved.

Innovation concept for measurement gross error detection and identification in power system state estimation

In this study, the innovation approach is used to estimate the measurement total error associated with power system state estimation. This is required because the power system equations are very much correlated with each other and as a consequence part of the measurements errors is masked. For that purpose an index, innovation index (II), which provides the quantity of new information a measurement contains is proposed. A critical measurement is the limit case of a measurement with low II, it has a zero II index and its error is totally masked. In other words, that measurement does not bring any innovation for the gross error test. Using the II of a measurement, the masked gross error by the state estimation is recovered; then the total gross error of that measurement is composed. Instead of the classical normalised measurement residual amplitude, the corresponding normalised composed measurement residual amplitude is used in the gross error detection and identification test, but with m degrees of freedom. The gross error processing turns out to be very simple to implement, requiring only few adaptations to the existing state estimation software. The IEEE-14 bus system is used to validate the proposed gross error detection and identification test.

Data Mining Applied to Harmonic Current Sources Identification in Residential Consumers

This work proposes a method based on both preprocessing and data mining with the objective of identify harmonic current sources in residential consumers. In addition, this methodology can also be applied to identify linear and nonlinear loads. It should be emphasized that the entire database was obtained through laboratory essays, i.e., real data were acquired from residential loads. Thus, the residential system created in laboratory was fed by a configurable power source and in its output were placed the loads and the power quality analyzers (all measurements were stored in a microcomputer). So, the data were submitted to pre-processing, which was based on attribute selection techniques in order to minimize the complexity in identifying the loads. A newer database was generated maintaining only the attributes selected, thus, Artificial Neural Networks were trained to realized the identification of loads. In order to validate the methodology proposed, the loads were fed both under ideal conditions (without harmonics), but also by harmonic voltages within limits pre-established. These limits are in accordance with IEEE Std. 519-1992 and PRODIST (procedures to delivery energy employed by Brazilian`s utilities). The results obtained seek to validate the methodology proposed and furnish a method that can serve as alternative to conventional methods.

Cluster identification and characterization in the riser of a circulating fluidized bed from numerical simulation results

A methodology of identification and characterization of coherent structures mostly known as clusters is applied to hydrodynamic results of numerical simulation generated for the riser of a circulating fluidized bed. The numerical simulation is performed using the MICEFLOW code, which includes the two-fluids IIT`s hydrodynamic model B. The methodology for cluster characterization that is used is based in the determination of four characteristics, related to average life time, average volumetric fraction of solid, existing time fraction and frequency of occurrence. The identification of clusters is performed by applying a criterion related to the time average value of the volumetric solid fraction. A qualitative rather than quantitative analysis is performed mainly owing to the unavailability of operational data used in the considered experiments. Concerning qualitative analysis, the simulation results are in good agreement with literature. Some quantitative comparisons between predictions and experiment were also presented to emphasize the capability of the modeling procedure regarding the analysis of macroscopic scale coherent structures. (c) 2007 Elsevier Inc. All rights reserved.

A hybrid Particle Swarm Optimization - Simplex algorithm (PSOS) for structural damage identification

This study proposes a new PSOS-model based damage identification procedure using frequency domain data. The formulation of the objective function for the minimization problem is based on the Frequency Response Functions (FRFs) of the system. A novel strategy for the control of the Particle Swarm Optimization (PSO) parameters based on the Nelder-Mead algorithm (Simplex method) is presented; consequently, the convergence of the PSOS becomes independent of the heuristic constants and its stability and confidence are enhanced. The formulated hybrid method performs better in different benchmark functions than the Simulated Annealing (SA) and the basic PSO (PSO(b)). Two damage identification problems, taking into consideration the effects of noisy and incomplete data, were studied: first, a 10-bar truss and second, a cracked free-free beam, both modeled with finite elements. In these cases, the damage location and extent were successfully determined. Finally, a non-linear oscillator (Duffing oscillator) was identified by PSOS providing good results. (C) 2009 Elsevier Ltd. All rights reserved

On the analysis of notched concrete beams: From measurement with digital image correlation to identification with boundary element method of a cohesive model

A way of coupling digital image correlation (to measure displacement fields) and boundary element method (to compute displacements and tractions along a crack surface) is presented herein. It allows for the identification of Young`s modulus and fracture parameters associated with a cohesive model. This procedure is illustrated to analyze the latter for an ordinary concrete in a three-point bend test on a notched beam. In view of measurement uncertainties, the results are deemed trustworthy thanks to the fact that numerous measurement points are accessible and used as entries to the identification procedure. (C) 2010 Elsevier Ltd. All rights reserved.

Identification of new ozonation disinfection byproducts of 17 beta-estradiol and estrone in water

Estrogens are a class of micro-pollutants found in water at low concentrations (in the ng L(-1) range), but often sufficient to exert estrogenic effects due to their high estrogenic potency. Disinfection of waters containing estrogens through oxidative processes has been shown to lead to the formation of disinfection byproducts, which may also be estrogenic. The present work investigates the formation of disinfection byproducts of 17 beta-estradiol (E2) and estrone (E1) in the treatment of water with ozone. Experiments have been carried out at two different concentrations of the estrogens in ground water (100 ng L(-1) and 100 mu g L(-1)) and at varying ozone dosages (0-30 mg L(-1)). Detection of the estrogens and their disinfection byproducts in the water samples has been performed by means of ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) with a triple quadrupole (QqQ) and a quadrupole-time of flight (QqTOF) instrument. Both E2 and El have been found to form two main byproducts, with molecular mass (MM) 288 and 278 in the case of E2, and 286 and 276 in the case of El, following presumably the same reaction pathways. The E2 byproduct with MM 288 has been identified as 10epsilon-17beta-dihydroxy-1,4-estradieno-3-one (DEO), in agreement with previously published results. The molecular structures and the formation pathways of the other three newly identified byproducts have been suggested. These byproducts have been found to be formed at both high and low concentrations of the estrogens and to be persistent even after application of high ozone dosages. (C) 2011 Elsevier Ltd. All rights reserved.

Anaerobic degradation of linear alkylbenzene sulfonate (LAS) in fluidized bed reactor by microbial consortia in different support materials

Four anaerobic fluidized bed reactors filled with activated carbon (R1), expanded clay (R2), glass beads (R3) and sand (R4) were tested for anaerobic degradation of LAS. All reactors were inoculated with sludge from a UASB reactor treating swine wastewater and were fed with a synthetic substrate supplemented with approximately 20 mg l(-1) of LAS, on average. To 560 mg l(-1) COD influent, the maximum COD and LAS removal efficiencies were mean values of 97 +/- 2% and 99 +/- 2%, respectively, to all reactors demonstrating the potential applicability of this reactor configuration for treating LAS. The reactors were kept at 30 degrees C and operated with a hydraulic retention time (HRT) of 18 h. The use of glass beads and sand appear attractive because they favor the development of biofilms capable of supporting LAS degradation. Subsequent 16S rRNA gene sequencing and phylogenetic analysis of samples from reactors R3 and R4 revealed that these reactors gave rise to broad microbial diversity, with microorganisms belonging to the phyla Bacteroidetes, Firmicutes, Actinobacteria and Proteobacteria, indicating the role of microbial consortia in degrading the surfactant LAS. (C) 2010 Elsevier Ltd. All rights reserved.

Microbial processes and bacterial populations associated to anaerobic treatment of sulfate-rich wastewater

A pilot-scale (1.2 m(3)) anaerobic sequencing batch biofilm reactor (ASBBR) containing mineral coal for biomass attachment was fed with sulfate-rich wastewater at increasing sulfate concentrations. Ethanol was used as the main organic source. Tested COD/sulfate ratios were of 1.8 and 1.5 for sulfate loading rates of 0.65-1.90 kgSO(4)(2-)/cycle (48 h-cycle) or of 1.0 in the trial with 3.0 gSO(4)(2-) l(-1). Sulfate removal efficiencies observed in all trials were as high as 99%. Molecular inventories indicated a shift on the microbial composition and a decrease on species diversity with the increase of sulfate concentration. Beta-proteobacteria species affiliated with Aminomonas spp. and Thermanaerovibrio spp. predominated at 1.0 gSO(4)(2-) l(-1). At higher sulfate concentrations the predominant bacterial group was Delta-proteobacteria mainly Desulfovibrio spp. and Desulfomicrobium spp. at 2.0 gSO(4)(2-) l(-1), whereas Desulfurella spp. and Coprothermobacter spp. predominated at 3.0 gSO(4)(2-) l(-1). These organisms have been commonly associated with sulfate reduction producing acetate, sulfide and sulfur. Methanogenic archaea(Methanosaeta spp.)was found at 1.0 and 2.0 gSO(4)(2-) l(-1). Additionally, a simplified mathematical model was used to infer on metabolic pathways of the biomass involved in sulfate reduction. (C) 2009 Elsevier Ltd. All rights reserved.

Performance of a horizontal-flow anaerobic immobilized biomass (HAIB) reactor and dynamics of the microbial community during degradation of pentachlorophenol (PCP)

The anaerobic biological treatment of pentachlorophenol (PCP) and methanol as the main carbon source was investigated in a horizontal-flow anaerobic immobilized biomass (HAIB) reactor at 30 +/- 1 degrees C, during a 220-day trial period. The reactor biomass was developed as an attached biofilm on polyurethane foam particles, with 24 h of hydraulic retention time. The PCP concentrations, which ranged from 2.0 to 13.0 mg/L, were controlled by adding synthetic substrate. The HAIB reactor reduced 97% of COD and removed 99% of PCP. The microbial biofilm communities of the HAIB reactor amended with PCP, without previous acclimatization, were characterized by polymerase chain reaction (PCR) and amplified ribosomal DNA restriction analysis (ARDRA) with specific Archaea oligonucleotide primers. The ARDRA technique provided an adequate analysis of the community, revealing the profile of the selected population along the reactor. The biomass activities in the HAIB reactor at the end of the experiments indicated the development of PCP degraders and the maintenance of the population of methanogenic Archaea, ensuring the high efficiency of the system treating PCP with added methanol as the cosubstrate. The use of the simplified ARDRA method enabled us to monitor the microbial population with the addition of high concentrations of toxic compounds and highlighting a selection of microorganisms in the biofilm. (C) 2008 Published by Elsevier Ltd.

Effects of bed materials on the performance of an anaerobic sequencing batch biofilm reactor treating domestic sewage

The objective of this study was to determine the best performance of an anaerobic sequencing batch biofilm reactor (AnSBBR) based on the use of four different bed materials as support for biomass immobilization. The bed materials utilized were Polyurethane foam (PU), vegetal carbon (VC), synthetic pumice (SP), and recycled low-density polyethylene (PE). The AnSBBR. with I total volume Of 7.2 L, was operated in 8-h batch cycles over 10 months, and fed with domestic sewage with an average influent chemical oxygen demand (COD) of 358 +/- 110 mg/L. The average effluent COD values were 121 +/- 31, 208 +/- 54, 233 +/- 52, and 227 +/- 51 mg/L. for PU, VC, SP, and PE, respectively. A modified first-order kinetic model was adjusted to temporal profiles of COD during a batch cycle, and the apparent kinetic constants were 0.52 +/- 0.05, 0.37 +/- 0.05, 0.80 +/- 0.04, and 0.30 +/- 0.021h(-1) for PU, VC, SP, and PE, respectively. Specific substrate utilization rates of 1.08, 0.11, and 0.86 mg COD/mg VS day were obtained for PU, VC, and PE, respectively. Although SP yielded the highest kinetic coefficient, PU was considered the best support, since SP presented loss of chemical constituents during the reactor`s operational phase. In addition, findings oil the microbial community were associated with the reactor`s performance data. Although PE did not show a satisfactory performance, an interesting microbial diversity was found oil its surface. Based oil the morphology and denaturing gradient gel electrophoresis (DGGE) results, PE showed the best capacity for promoting the attachment of methanogenic organisms, and is therefore a material that merits further analysis. PU was considered the Most suitable material showing the best performance in terms of efficiency of solids and COD removal. (C) 2007 Elsevier Ltd. All rights reserved.

Fermentative hydrogen production by microbial consortium

Heat pre-treatment of the inoculum associated to the pH control was applied to select hydrogen-producing bacteria and endospores-forming bacteria. The source of inoculum to the heat pre-treatment was from a UASB reactor used in the slaughterhouse waste treatment. The molecular biology analyses indicated that the microbial consortium presented microorganisms affiliated with Enterobacter cloacae (97% and 98%), Clostridium sp. (98%) and Clostridium acetobutyricum (96%), recognized as H, and volatile acids` producers. The following assays were carried out in batch reactors in order to verify the efficiencies of sucrose conversion to H-2 by the microbial consortium: (1) 630.0 mg sucrose/L, (2) 1184.0 mg sucrose/L, (3) 1816.0 mg sucrose/L and (4) 4128.0 mg sucrose/L. The subsequent yields were obtained as follows: 15% (1.2 mol H-2/mol sucrose), 20% (1.6 mol H-2/mol sucrose), 15% (1.2 mol H-2/mol sucrose) and 4% (0.3 mol H-2/mol sucrose), respectively. The intermediary products were acetic acid, butyric acid, methanol and ethanol in all of the anaerobic reactors. (C) 2008 International Association for Hydrogen Energy. Published by Elsevier Ltd. All rights reserved.

Learning Profile Identification Based on the Analysis of the User Context of Interaction

One of the e-learning environment goal is to attend the individual needs of students during the learning process. The adaptation of contents, activities and tools into different visualization or in a variety of content types is an important feature of this environment, bringing to the user the sensation that there are suitable workplaces to his profile in the same system. Nevertheless, it is important the investigation of student behaviour aspects, considering the context where the interaction happens, to achieve an efficient personalization process. The paper goal is to present an approach to identify the student learning profile analyzing the context of interaction. Besides this, the learning profile could be analyzed in different dimensions allows the system to deal with the different focus of the learning.

Fast experimental identification of suspension models for control design

This paper presents both the theoretical and the experimental approaches of the development of a mathematical model to be used in multi-variable control system designs of an active suspension for a sport utility vehicle (SUV), in this case a light pickup truck. A complete seven-degree-of-freedom model is successfully quickly identified, with very satisfactory results in simulations and in real experiments conducted with the pickup truth. The novelty of the proposed methodology is the use of commercial software in the early stages of the identification to speed up the process and to minimize the need for a large number of costly experiments. The paper also presents major contributions to the identification of uncertainties in vehicle suspension models and in the development of identification methods using the sequential quadratic programming, where an innovation regarding the calculation of the objective function is proposed and implemented. Results from simulations of and practical experiments with the real SUV are presented, analysed, and compared, showing the potential of the method.

Dynamic Imaging in Electrical Impedance Tomography of the Human Chest With Online Transition Matrix Identification

One of the electrical impedance tomography objectives is to estimate the electrical resistivity distribution in a domain based only on electrical potential measurements at its boundary generated by an imposed electrical current distribution into the boundary. One of the methods used in dynamic estimation is the Kalman filter. In biomedical applications, the random walk model is frequently used as evolution model and, under this conditions, poor tracking ability of the extended Kalman filter (EKF) is achieved. An analytically developed evolution model is not feasible at this moment. The paper investigates the identification of the evolution model in parallel to the EKF and updating the evolution model with certain periodicity. The evolution model transition matrix is identified using the history of the estimated resistivity distribution obtained by a sensitivity matrix based algorithm and a Newton-Raphson algorithm. To numerically identify the linear evolution model, the Ibrahim time-domain method is used. The investigation is performed by numerical simulations of a domain with time-varying resistivity and by experimental data collected from the boundary of a human chest during normal breathing. The obtained dynamic resistivity values lie within the expected values for the tissues of a human chest. The EKF results suggest that the tracking ability is significantly improved with this approach.