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.

Multi-objective analysis of impacts of distributed generation placement on the operational characteristics of networks for distribution system planning

Recent advances in energy technology generation and new directions in electricity regulation have made distributed generation (DG) more widespread, with consequent significant impacts on the operational characteristics of distribution networks. For this reason, new methods for identifying such impacts are needed, together with research and development of new tools and resources to maintain and facilitate continued expansion towards DG. This paper presents a study aimed at determining appropriate DG sites for distribution systems. The main considerations which determine DG sites are also presented, together with an account of the advantages gained from correct DG placement. The paper intends to define some quantitative and qualitative parameters evaluated by Digsilent (R), GARP3 (R) and DSA-GD software. A multi-objective approach based on the Bellman-Zadeh algorithm and fuzzy logic is used to determine appropriate DG sites. The study also aims to find acceptable DG locations both for distribution system feeders, as well as for nodes inside a given feeder. (C) 2010 Elsevier Ltd. All rights reserved.

Modeling associations between genetic markers using Bayesian networks

Motivation: Understanding the patterns of association between polymorphisms at different loci in a population ( linkage disequilibrium, LD) is of fundamental importance in various genetic studies. Many coefficients were proposed for measuring the degree of LD, but they provide only a static view of the current LD structure. Generative models (GMs) were proposed to go beyond these measures, giving not only a description of the actual LD structure but also a tool to help understanding the process that generated such structure. GMs based in coalescent theory have been the most appealing because they link LD to evolutionary factors. Nevertheless, the inference and parameter estimation of such models is still computationally challenging. Results: We present a more practical method to build GM that describe LD. The method is based on learning weighted Bayesian network structures from haplotype data, extracting equivalence structure classes and using them to model LD. The results obtained in public data from the HapMap database showed that the method is a promising tool for modeling LD. The associations represented by the learned models are correlated with the traditional measure of LD D`. The method was able to represent LD blocks found by standard tools. The granularity of the association blocks and the readability of the models can be controlled in the method. The results suggest that the causality information gained by our method can be useful to tell about the conservability of the genetic markers and to guide the selection of subset of representative markers.

Power quality analysis applying a hybrid methodology with wavelet transforms and neural networks

A hybrid system to automatically detect, locate and classify disturbances affecting power quality in an electrical power system is presented in this paper. The disturbances characterized are events from an actual power distribution system simulated by the ATP (Alternative Transients Program) software. The hybrid approach introduced consists of two stages. In the first stage, the wavelet transform (WT) is used to detect disturbances in the system and to locate the time of their occurrence. When such an event is flagged, the second stage is triggered and various artificial neural networks (ANNs) are applied to classify the data measured during the disturbance(s). A computational logic using WTs and ANNs together with a graphical user interface (GU) between the algorithm and its end user is then implemented. The results obtained so far are promising and suggest that this approach could lead to a useful application in an actual distribution system. (C) 2009 Elsevier Ltd. All rights reserved.

Using Bayesian networks with rule extraction to infer the risk of weed infestation in a corn-crop

This paper describes the modeling of a weed infestation risk inference system that implements a collaborative inference scheme based on rules extracted from two Bayesian network classifiers. The first Bayesian classifier infers a categorical variable value for the weed-crop competitiveness using as input categorical variables for the total density of weeds and corresponding proportions of narrow and broad-leaved weeds. The inferred categorical variable values for the weed-crop competitiveness along with three other categorical variables extracted from estimated maps for the weed seed production and weed coverage are then used as input for a second Bayesian network classifier to infer categorical variables values for the risk of infestation. Weed biomass and yield loss data samples are used to learn the probability relationship among the nodes of the first and second Bayesian classifiers in a supervised fashion, respectively. For comparison purposes, two types of Bayesian network structures are considered, namely an expert-based Bayesian classifier and a naive Bayes classifier. The inference system focused on the knowledge interpretation by translating a Bayesian classifier into a set of classification rules. The results obtained for the risk inference in a corn-crop field are presented and discussed. (C) 2009 Elsevier Ltd. All rights reserved.

An approach based on neural networks for estimation and generalization of crossflow filtration processes

The crossflow filtration process differs of the conventional filtration by presenting the circulation flow tangentially to the filtration surface. The conventional mathematical models used to represent the process have some limitations in relation to the identification and generalization of the system behaviour. In this paper, a system based on artificial neural networks is developed to overcome the problems usually found in the conventional mathematical models. More specifically, the developed system uses an artificial neural network that simulates the behaviour of the crossflow filtration process in a robust way. Imprecisions and uncertainties associated with the measurements made on the system are automatically incorporated in the neural approach. Simulation results are presented to justify the validity of the proposed approach. (C) 2007 Elsevier B.V. All rights reserved.

Modeling and analysis of laminate composite plates with embedded active-passive piezoelectric networks

The objective of this work is to present the finite element modeling of laminate composite plates with embedded piezoelectric patches or layers that are then connected to active-passive resonant shunt circuits, composed of resistance, inductance and voltage source. Applications to passive vibration control and active control authority enhancement are also presented and discussed. The finite element model is based on an equivalent single layer theory combined with a third-order shear deformation theory. A stress-voltage electromechanical model is considered for the piezoelectric materials fully coupled to the electrical circuits. To this end, the electrical circuit equations are also included in the variational formulation. Hence, conservation of charge and full electromechanical coupling are guaranteed. The formulation results in a coupled finite element model with mechanical (displacements) and electrical (charges at electrodes) degrees of freedom. For a Graphite-Epoxy (Carbon-Fibre Reinforced) laminate composite plate, a parametric analysis is performed to evaluate optimal locations along the plate plane (xy) and thickness (z) that maximize the effective modal electromechanical coupling coefficient. Then, the passive vibration control performance is evaluated for a network of optimally located shunted piezoelectric patches embedded in the plate, through the design of resistance and inductance values of each circuit, to reduce the vibration amplitude of the first four vibration modes. A vibration amplitude reduction of at least 10 dB for all vibration modes was observed. Then, an analysis of the control authority enhancement due to the resonant shunt circuit, when the piezoelectric patches are used as actuators, is performed. It is shown that the control authority can indeed be improved near a selected resonance even with multiple pairs of piezoelectric patches and active-passive circuits acting simultaneously. (C) 2010 Elsevier Ltd. All rights reserved.

Gait Pattern Adaptation for an Active Lower-Limb Orthosis Based on Neural Networks

This work deals with neural network (NN)-based gait pattern adaptation algorithms for an active lower-limb orthosis. Stable trajectories with different walking speeds are generated during an optimization process considering the zero-moment point (ZMP) criterion and the inverse dynamic of the orthosis-patient model. Additionally, a set of NNs is used to decrease the time-consuming analytical computation of the model and ZMP. The first NN approximates the inverse dynamics including the ZMP computation, while the second NN works in the optimization procedure, giving an adapted desired trajectory according to orthosis-patient interaction. This trajectory adaptation is added directly to the trajectory generator, also reproduced by a set of NNs. With this strategy, it is possible to adapt the trajectory during the walking cycle in an on-line procedure, instead of changing the trajectory parameter after each step. The dynamic model of the actual exoskeleton, with interaction forces included, is used to generate simulation results. Also, an experimental test is performed with an active ankle-foot orthosis, where the dynamic variables of this joint are replaced in the simulator by actual values provided by the device. It is shown that the final adapted trajectory follows the patient intention of increasing the walking speed, so changing the gait pattern. (C) Koninklijke Brill NV, Leiden, 2011

Remote control of CNC machines using the CyberOPC communication system over public networks

During the last few years, the evolution of fieldbus and computers networks allowed the integration of different communication systems involving both production single cells and production cells, as well as other systems for business intelligence, supervision and control. Several well-adopted communication technologies exist today for public and non-public networks. Since most of the industrial applications are time-critical, the requirements of communication systems for remote control differ from common applications for computer networks accessing the Internet, such as Web, e-mail and file transfer. The solution proposed and outlined in this work is called CyberOPC. It includes the study and the implementation of a new open communication system for remote control of industrial CNC machines, making the transmission delay for time-critical control data shorter than other OPC-based solutions, and fulfilling cyber security requirements.

Artificial neural networks analysis of Sao Paulo subway tunnel settlement data

Artificial neural networks have been used to analyze a number of engineering problems, including settlement caused by different tunneling methods in various types of ground mass. This paper focuses on settlement over shotcrete- supported tunnels on Sao Paulo subway line 2 (West Extension) that were excavated in Tertiary sediments using the sequential excavation method. The adjusted network is a good tool for predicting settlement above new tunnels to be excavated in similar conditions. The influence of network training parameters on the quality of results is also discussed. (C) 2007 Elsevier Ltd. All rights reserved.

Treatment of linear alkylbenzene sulfonate in a horizontal anaerobic immobilized biomass reactor

Linear alkylbenzene sulfonate (LAS) is an anionic surfactant widely used to manufacture detergents and found in domestic and industrial wastewater. LAS removal was evaluated in a horizontal anaerobic immobilized biomass reactor. The system was filled with polyurethane foam and inoculated with sludge that was withdrawn from an up flow anaerobic sludge blanket reactor that is used to treat swine wastewater. The reactor was fed with easily degradable substrates and a solution of commercial LAS for 313 days. The hydraulic retention time applied was 12 h. The system was initially operated without detergent and resulted to 94% reduction of demand. The mass balance in the system indicated that the LAS removal efficiency was 45% after 180 days. From the 109th day to the 254th day, a removal efficiency of 32% was observed. The removal of LAS was approximately 40% when 1500 mg of LAS were applied in the absence of co-substrates suggesting that the LAS molecules were used selectively. Microscopic analyses of the biofilm revealed diverse microbial morphologies and denaturing gradient gel electrophoresis profiling showed variations in the total bacteria and sulfate-reducing bacteria populations. 16S rRNA sequencing and phylogenetic analyses demonstrated that members of the order Clostridiales were the major components of the bacterial community in the last step of the reactor operation. (c) 2009 Elsevier Ltd. All rights reserved.

Pentachlorophenol (PCP) dechlorination in horizontal-flow anaerobic immobilized biomass (HAIB) reactors

This study verifies the potential applicability of horizontal-flow anaerobic immobilized biomass (HAIB) reactors to pentachlorophenol (PCP) dechlorination. Two bench-scale HAIB reactors (R1 and R2) were filled with cubic polyurethane foam matrices containing immobilized anaerobic sludge. The reactors were then continuously fed with synthetic wastewater consisting of PCP, glucose, acetic acid, and formic acid as co-substrates for PCP anaerobic degradation. Before being immobilized in polyurethane foam matrices, the biomass was exposed to wastewater containing PCP in reactors fed at a semi-continuous rate of 2.0 mu g PCP g(-1) VS. The applied PCP loading rate was increased from 0.05 to 2.59 mg PCP l(-1) day(-1) for RI, and from 0.06 to 4.15 mg PCP l(-1) day(-1) for R2. The organic loading rates (OLR) were 1.1 and 1.7 kg COD m(-3) day(-1) at hydraulic retention times (HRT) of 24 h for R1 and 18 In for R2. Under such conditions, chemical oxygen demand (COD) removal efficiencies of up to 98% were achieved in the HAIB reactors. Both reactors exhibited the ability to remove 97% of the loaded PCP. Dichlorophenol (DCP) was the primary chlorophenol detected in the effluent. The adsorption of PCP and metabolites formed during PCP degradation in the packed bed was negligible for PCP removal efficiency. (C) 2009 Elsevier Ltd. All rights reserved.

Dynamics of sulfidogenesis associated to methanogenesis in horizontal-flow anaerobic immobilized biomass reactor

Two bench-scale horizontal anaerobic fixed bed reactors were tested to remove both sulfate and organic matter from wastewater. First, the reactors (R1 and R2) were supplied with synthetic wastewater containing sulfate and a solution of ethanol and volatile fatty acids. Subsequently, RI and R2 were fed with only ethanol or acetate, respectively. The substitution to ethanol in R1 increased the sulfate reduction efficiency from 83% to nearly 100% for a chemical oxygen demand to sulfate (COD/sulfate) ratio of 3.0. In contrast, in R2, the switch in carbon source to acetate strongly decreased sulfidogenesis and the maximum sulfate reduction achieved was 47%. Process stability in long-term experiments and high removal efficiencies of both organic matter and sulfate were achieved with ethanol as the sole carbon source. The results allow concluding that syntrophism instead of competition between the sulfate reducing bacteria and acetoclastic methanogenic archaeal populations prevailed in the reactor. (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.

Evaluation of the microbial diversity in a horizontal-flow anaerobic immobilized biomass reactor treating linear alkylbenzene sulfonate

The purpose of this work was to assess the degradation of linear alkylbenzene sulfonate (LAS) in a horizontal-flow anaerobic immobilized biomass (HAIB) reactor. The reactor was filled with polyurethane foam where the sludge from a sanitary sewage treatment was immobilized. The hydraulic detention time (HDT) used in the experiments was of 12 h. The reactor was fed with synthetic substrate (410 mg l(-1) of meat extract, 115 mg l(-1) of starch, 80 mg l(-1) of saccharose, 320 mg l(-1) of sodium bicarbonate and 5 ml l(-1)of salt solution) in the following stages of operation: SI-synthetic substrate, SII-synthetic substrate with 7 mg l(-1) of LAS, SIII-synthetic substrate with 14 mg l(-1) of LAS and SIV-synthetic substrate containing yeast extract (substituting meat extract) and 14 mg l(-1) of LAS, without starch. At the end of the experiment (313 days) a degradation of similar to 35% of LAS was achieved. The higher the concentration of LAS, the greater the amount of foam for its adsorption. This is necessary because the isotherm of LAS adsorption in the foam is linear for the studied concentrations (2 to 50 mg l(-1)). Microscopic analyses of the biofilm revealed diverse microbial morphologies, while Denaturing Gradient Gel Eletrophoresis (DGGE) profiling showed variations in the population of total bacteria and sulphate-reducing bacteria (SRB). The 16S rRNA gene sequencing and phylogenetic analyses revealed that the members of the order Clostridiales were the major components of the bacterial community in the last reactor operation step.