Transmission lines distance protection using artificial neural networks

This work presents the development and implementation of an artificial neural network based algorithm for transmission lines distance protection. This algorithm was developed to be used in any transmission line regardless of its configuration or voltage level. The described ANN-based algorithm does not need any topology adaptation or ANN parameters adjustment when applied to different electrical systems. This feature makes this solution unique since all ANN-based solutions presented until now were developed for particular transmission lines, which means that those solutions cannot be implemented in commercial relays. (c) 2011 Elsevier Ltd. All rights reserved.

Ethanol production process from banana fruit and its lignocellulosic residues: Energy analysis

Tropical countries, such as Brazil and Colombia, have the possibility of using agricultural lands for growing biomass to produce bio-fuels such as biodiesel and ethanol. This study applies an energy analysis to the production process of anhydrous ethanol obtained from the hydrolysis of starch and cellulosic and hemicellulosic material present in the banana fruit and its residual biomass. Four different production routes were analyzed: acid hydrolysis of amylaceous material (banana pulp and banana fruit) and enzymatic hydrolysis of lignocellulosic material (flower stalk and banana skin). The analysis considered banana plant cultivation, feedstock transport, hydrolysis, fermentation, distillation, dehydration, residue treatment and utility plant. The best indexes were obtained for amylaceous material for which mass performance varied from 346.5 L/t to 388.7 L/t, Net Energy Value (NEV) ranged from 9.86 MJ/L to 9.94 MJ/L and the energy ratio was 1.9 MJ/MJ. For lignocellulosic materials, the figures were less favorable: mass performance varied from 86.1 to 123.5 L/t, NEV from 5.24 10 8.79 MJ/L and energy ratio from 1.3 to 1.6 MJ/MJ. The analysis showed, however, that both processes can be considered energetically feasible. (C) 2010 Elsevier Ltd. All rights reserved.

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.

EXERGOECONOMIC COMPARISON OF ABSORPTION REFRIGERATION SYSTEMS INCLUDING A HIBRID ABSORPTION-EJECTO COMPRESSION CHILLER

The study of absorption refrigeration systems has had increasing importance in recent years due to the fact that the primary energy that is used in an absorption system can be heat available from a residual source or even a renewable one. Therefore, these systems not only use energy that would be rejected by the environment, but also they avoid the consumption of expensive fossil or electrical energies. The production cost of the mechanical work necessary to obtain a kW of refrigeration for mechanical compression cycle is normally higher than the cost for recovering the needed heat to obtain the same kW in an absorption cycle. Also, the use of these systems reduces impact on the environment by decreasing the emission of CO(2). We intend to show the performance of a hybrid absorption-ejecto compression chiller compared to conventional double- and single-effect water/lithium bromide systems, by means of an exergetic and exergoeconomic analysis of these configurations in order to calculate the exergy-based cost of a final product. The vapor compression refrigeration system is included in the results, as a comparisson to the performance of the absorption refrigeration systems analyzed.

Energy and exergy analysis of ethanol production process from banana fruit

An alternative for ethanol production, is the use of vegetable waste, such as excess of banana production, that are evaluated in 2,400,000 t/year, which includes: residual banana fruit and lignocellulosic material. This paper analyzes the energetic and exergetic behavior to carry the process developed at laboratory scale to a plant processing of banana for the ethanol production, involving: growing and transport of the vegetable material, hydrolysis of banana fruit, sugar fermentation, ethanol distillation and utility plant. Finally, energy and exergy indicators are obtained. The results show a positive energy balance when banana fruit is used for ethanol production, but some process modification must be done looking for improving the exergetic efficiency in ethanol production.

Energy dissipation in depth-sensing indentation as a characteristic of the nanoscratch behavior of coatings

Wear behavior of coatings has usually been described in terms of mechanical properties such as hardness (H) and effective elastic modulus (E*). Alternatively, an energy approach appears as a promising analysis taking into account the influence of those properties. In a nanoindentation test, the dissipated energy depends not only on the hardness and elastic modulus, but also on the elastic recovery (W(e)). This work aims to establish a relation between plastic deformation energy (E(p)) during depth-sensing indentation method and the grooving resistance of coatings in nanoscratch tests. An energy dissipation coefficient (K(d)) was defined, calculated as the ratio of the plastic to the total deformation energy (E(p)/E(t)), which represents the energy dissipation of materials. Reactive depositions using titanium as the target and nitrogen and methane as reactive gases were obtained by triode magnetron sputtering, in order to assess wear and nanoindentation data. A topographical, chemical and microstructural characterization has been conducted using X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), wave dispersion spectroscopy (WDS), scanning electron (SEM) and atomic force microscopy (AFM) techniques. Nanoscratch results showed that the groove depth was well correlated to the energy dissipation coefficient of the coatings. On the other hand, a reduction in the coefficient was found when the elastic recovery was increased. (C) 2009 Elsevier B.V. All rights reserved.

Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption

There are several ways to attempt to model a building and its heat gains from external sources as well as internal ones in order to evaluate a proper operation, audit retrofit actions, and forecast energy consumption. Different techniques, varying from simple regression to models that are based on physical principles, can be used for simulation. A frequent hypothesis for all these models is that the input variables should be based on realistic data when they are available, otherwise the evaluation of energy consumption might be highly under or over estimated. In this paper, a comparison is made between a simple model based on artificial neural network (ANN) and a model that is based on physical principles (EnergyPlus) as an auditing and predicting tool in order to forecast building energy consumption. The Administration Building of the University of Sao Paulo is used as a case study. The building energy consumption profiles are collected as well as the campus meteorological data. Results show that both models are suitable for energy consumption forecast. Additionally, a parametric analysis is carried out for the considered building on EnergyPlus in order to evaluate the influence of several parameters such as the building profile occupation and weather data on such forecasting. (C) 2008 Elsevier B.V. All rights reserved.

Recycling Krylov subspaces for efficient large-scale electrical impedance tomography

Electrical impedance tomography (EIT) captures images of internal features of a body. Electrodes are attached to the boundary of the body, low intensity alternating currents are applied, and the resulting electric potentials are measured. Then, based on the measurements, an estimation algorithm obtains the three-dimensional internal admittivity distribution that corresponds to the image. One of the main goals of medical EIT is to achieve high resolution and an accurate result at low computational cost. However, when the finite element method (FEM) is employed and the corresponding mesh is refined to increase resolution and accuracy, the computational cost increases substantially, especially in the estimation of absolute admittivity distributions. Therefore, we consider in this work a fast iterative solver for the forward problem, which was previously reported in the context of structural optimization. We propose several improvements to this solver to increase its performance in the EIT context. The solver is based on the recycling of approximate invariant subspaces, and it is applied to reduce the EIT computation time for a constant and high resolution finite element mesh. In addition, we consider a powerful preconditioner and provide a detailed pseudocode for the improved iterative solver. The numerical results show the effectiveness of our approach: the proposed algorithm is faster than the preconditioned conjugate gradient (CG) algorithm. The results also show that even on a standard PC without parallelization, a high mesh resolution (more than 150,000 degrees of freedom) can be used for image estimation at a relatively low computational cost. (C) 2010 Elsevier B.V. All rights reserved.

MODELLING AND CONTROL OF ERSW PROCESSES BY NEURONAL NETWORK

A study on the use of artificial intelligence (AI) techniques for the modelling and subsequent control of an electric resistance spot welding process (ERSW) is presented. The ERSW process is characterized by the coupling of thermal, electrical, mechanical, and metallurgical phenomena. For this reason, early attempts to model it using computational methods established as the methods of finite differences, finite element, and finite volumes, ask for simplifications that lead the model obtained far from reality or very costly in terms of computational costs, to be used in a real-time control system. In this sense, the authors have developed an ERSW controller that uses fuzzy logic to adjust the energy transferred to the weld nugget. The proposed control strategies differ in the speed with which it reaches convergence. Moreover, their application for a quality control of spot weld through artificial neural networks (ANN) is discussed.

Comparison between DC(+) and Square Wave AC SAW Current Outputs to Weld AISI 304 for Low-Temperature Applications

Welded equipment for cryogenic applications is utilized in chemical, petrochemical, and metallurgical industries. One material suitable for cryogenic application is austenitic stainless steel, which usually doesn`t present ductile/brittle transition temperature, except in the weld metal, where the presence of ferrite and micro inclusions can promote a brittle failure, either by ferrite cleavage or dimple nucleation and growth, respectively. A 25-mm- (1-in.-) thick AISI 304 stainless steel base metal was welded with the SAW process using a 308L solid wire and two kinds of fluxes and constant voltage power sources with two types of electrical outputs: direct current electrode positive and balanced square wave alternating current. The welded joints were analyzed by chemical composition, microstructure characterization, room temperature mechanical properties, and CVN impact test at -100 degrees C (-73 degrees F). Results showed that an increase of chromium and nickel content was observed in all weld beads compared to base metal. The chromium and nickel equivalents ratio for the weld beads were always higher for welding with square wave AC for the two types of fluxes than for direct current. The modification in the Cr(eq)/Ni(eq) ratio changes the delta ferrite morphology and, consequently, modifies the weld bead toughness at lower temperatures. The oxygen content can also affect the toughness in the weld bead. The highest absorbed energy in a CVN impact test was obtained for the welding condition with square wave AC electrical output and neutral flux, followed by DC(+) electrical output and neutral flux, and square wave AC electrical output and alloyed flux.

Embrittlement of electrical steel laminations by nitrogen pick-up during heat treatment

Heat treated electrical steel laminations have shown evidence of low ductility behavior, characterized by a small number of bends till fracture, on repeated bending tests. The laminations were produced using a new grade of electrical steel with much lower aluminum content than usual. The problem happens when the oxygen potential (measured by the dew point of the atmosphere) of the heat treatment atmosphere is abnormally high. Furthermore, ductility can be restored by a low-oxygen potential heat treatment. Although the heat treatment resulted in a loss of ductility, the magnetic properties were not deteriorated. The low ductility samples always show intergranular fracture, whereas the un-treated laminations fracture by cleavage. The low ductility is associated with the formation of silicon manganese nitride precipitates formed at grain boundaries, although they are not the cause of the low ductility. Ductility could be restored by a low dew point heat treatment but the inclusions remained in the grain boundaries. The low ductility and its recovery must be ascribed to the presence of nitrogen atoms segregated to the grain boundaries when the heat treatment atmosphere has a high oxygen potential. The lack of aluminum in the composition of the steel hinders the scavenging effect of this element on nitrogen atoms in solution in the steel. (C) 2009 Elsevier Ltd. All rights reserved.

Interface Energy Measurement of MgO and ZnO: Understanding the Thermodynamic Stability of Nanoparticles

Nanomaterials have triggered excitement in both fundamental science and technological applications in several fields However, the same characteristic high interface area that is responsible for their unique properties causes unconventional instability, often leading to local collapsing during application Thermodynamically, this can be attributed to an increased contribution of the interface to the free energy, activating phenomena such as sintering and grain growth The lack of reliable interface energy data has restricted the development of conceptual models to allow the control of nanoparticle stability on a thermodynamic basis. Here we introduce a novel and accessible methodology to measure interface energy of nanoparticles exploiting the heat released during sintering to establish a quantitative relation between the solid solid and solid vapor interface energies. We exploited this method in MgO and ZnO nanoparticles and determined that the ratio between the solid solid and solid vapor interface energy is 11 for MgO and 0.7 for ZnO. We then discuss that this ratio is responsible for a thermodynamic metastable state that may prevent collapsing of nanoparticles and, therefore, may be used as a tool to design long-term stable nanoparticles.

Hysteresis loss subdivision

Assuming that different energy dissipation mechanisms are at work along hysteresis, a hysteresis loss subdivision procedure has been proposed, using the induction at maximum permeability ( around 0.8 T, in electrical steels) as the boundary between the ""low-induction`` and the ""high-induction`` regions. This paper reviews the most important results obtained in 10 years of investigation of the effect of microstructure on these components of the hysteresis loss. As maximum induction increases, the ""low-induction loss`` increases linearly up to 1.2 T, while the ""high-induction loss`` is zero up to 0.7 T and then increases as a power law with n = 5. Low-induction loss behavior is linearly related to H(c) between 0.4 and 1.2 T. Grain size has a larger influence on low-induction losses than on high-induction losses. Texture has a much stronger influence on high loss than on low-induction loss, and it is related to the average magnetocrystalline energy. 6.5%Si steel shows smaler hysteresis loss at 1.5 T than 3.5%Si steel only because of its smaler high-induction component. The abrupt increase in hysteresis loss due to very small plastic deformation is strongly related to the high-induction loss component. These results are discussed in terms of energy dissipation mechanisms such as domain wall movement, irreversible rotation and domain wall energy dissipation at domain nucleation and annihilation. (C) 2008 Elsevier B.V. All rights reserved.

Thermomechanical processing for recovery of desired < 001 > fiber texture in electric motor steels

A processing route has been developed for recovering the desired lambda fiber in iron-silicon electrical steel needed for superior magnetic properties in electric motor application. The lambda fiber texture is available in directionally solidified iron-silicon steel with the < 001 > columnar grains but was lost after heavy rolling and recrystallization required for motor laminations. Two steps of light rolling each followed by recrystallization were found to largely restore the desired fiber texture. This strengthening of the < 001 > fiber texture had been predicted on the basis of the strain-induced boundary migration (SIBM) mechanism during recrystallization of lightly rolled steel from existing grains of near the ideal orientation, due to postulated low stored energies. Taylor and finite element models supported the idea of the low stored energy of the lambda fiber grains. The models also showed that the lambda fiber grains, though unstable during rolling, only rotated away from their initial orientations quite slowly.

The influence of different voltage waveforms and grain sizes in electrical steels losses

Square and two-level pulse width modulation (PWM) magnetic induction waveforms are investigated and their effect on electrical steels losses as a function of the grain size is determined. The increase of hysteresis losses-as compared to that resulting from sinusoidal voltages-occurs only for two-level PWM waveforms. Total losses are lower for square waveform, and the difference between losses under square and sinusoidal waveform increase with increasing grain size, result explained with the loss separation model. (C) 2008 Elsevier B.V. All rights reserved.