The dynamic behavior of a parametrically excited time-periodic MEMS taking into account parametric errors

Micro-electromechanical systems (MEMS) are micro scale devices that are able to convert electrical energy into mechanical energy or vice versa. In this paper, the mathematical model of an electronic circuit of a resonant MEMS mass sensor, with time-periodic parametric excitation, was analyzed and controlled by Chebyshev polynomial expansion of the Picard interaction and Lyapunov-Floquet transformation, and by Optimal Linear Feedback Control (OLFC). Both controls consider the union of feedback and feedforward controls. The feedback control obtained by Picard interaction and Lyapunov-Floquet transformation is the first strategy and the optimal control theory the second strategy. Numerical simulations show the efficiency of the two control methods, as well as the sensitivity of each control strategy to parametric errors. Without parametric errors, both control strategies were effective in maintaining the system in the desired orbit. On the other hand, in the presence of parametric errors, the OLFC technique was more robust.

Viabilidade econômica da venda de energia elétrica em cogeração sob condições de risco: um estudo de caso

The availability of the electrical energy, in sufficient quantities and in competitive prices is a crucial factor to the economic development. The trade-in of the excess electrical energy produced in a system of cogeneration can be seen as an alternative to the creation of an additional source of revenues for ethanol power plants sector, besides contributing to the complementation of the Brazilian electrical headquarter with renewable sources. The objective of this study was to evaluate the economic feasibility of the implementation of a cogeneration electrical central using the excess of sugar cane bagasse and selling the excess of electrical energy with prices of the market. An ethanol power plant located in the state of Sao Paulo was used to this study. It was used the case study methodology, evaluating the potential of the investment under the viewpoint of the Net Present Value (NPV), Payback and Internal Rate of Return (IRR), and complementing the results of the Accounting Results (AC). It was created three alternative scenarios to reflect the level of the risk of every studied situation: the most likely, an optimistic and a pessimistic, each one with its assumptions. The Monte Carlo Simulations was used to insert the elements of risk to each scenario. The results showed that the project is feasible in all NPV scenarios. And the Payback and IRR analysis confirmed these evidences. The valuation with the AR showed that the project is most risky at the pessimistic scenario, but is feasibly in the most likely and the optimistic scenarios. It was concluded that the project is economic viable. However, the economic viability shown in the results is based on the maintenance of the future prices on the levels of the historical prices used in the analysis.

O emprego de técnicas de qualidade de serviço em redes sem fio aplicado à automação predial

Pós-graduação em Engenharia Elétrica - FEIS

Proposta de metodologia para gestão de produção, visando redução no consumo de energia elétrica em uma unidade fabril do setor de bens de consumo

Actually the energy efficiency is making more space in the industry, due to the search for the sustainability, the electrical energy costs reduction, the goals achievement or the efficiency of production processes. In consumer goods industries, such a beverage industry, as the work is based, the productivity is directly related to the electrical energy consumption. The development of methodologies and/or routines, in addition to some tools which allow to align more efficiently these two aspects (production and consumption of electrical energy), in the viewpoint of the Energy Conservation, is very important. In this case, the study will show the Plant Modulation concepts, a production management methodology, based in some factors related to the productive process, installed equipment, production supplies and energy cost. The proposed methodology was implanted in a plant along 2015 and show the results, in face to confirm its efficiency. Finally, in this study, it was shown the capacity of Plant Modulation to positively impact in the energy efficiency inside a big industry

Estimativa do potencial de acréscimo da oferta de energia no setor sucroalcooleiro paulista a partir de cogeração e gaseificação do bagaço de cana-de-açúcar com utilização de ciclo combinado

This work evaluates the existing potential in the state of Sao Paulo for the generation of electrical energy using the sugar cane bagasse as fuel. As the bagasse is a by-product of the sugarcane and alcohol industry and it is produced in large scale in the country, mainly in the state of Sao Paulo, it is important to develop researches that aim the best utilization of this input. In order to determine its potential, at first, a study was conducted considering the utilization of the cogeneration, which is a common practice in the plants of the sector. However, it was concluded that the cogeneration could provide a higher quantity of energy if more modern technologies and more efficient processes were used. Another study to estimate the potential considered a system of gasification of the sugar cane bagasse integrated with the combined cycle (BIG/GTCC). It was concluded that this technology can provide a considerable increase in the electrical supply. In this work it was also developed an energetic study based on real data from a plant located in the state of Sao Paulo. A thermodynamic analysis was done in the existing equipment of the cogeneration section of the plant. And another analysis was done considering the implementation of the BIG/GTCC technology to the cogeneration system. Comparing the results of both settings, it was concluded that the utilization of the sugar cane bagasse integrated to a combined cycle increased considerably the efficiency in the generation of electricity of the plant, increasing more than six times its production capacity of electrical energy

Efeito do filme de carbono DLC aplicado por plasma como recobrimento em Polietileno de Ultra Densidade e Alto Peso Molecular (UHWPE) e planejamento virtual utilizando a técnica de elementos finitos em cirúrgias de próteses totais da ATM

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Long-term recovery patterns of arctic tundra after winter seismic exploration

In response to the increasing global demand for energy, oil exploration and development are expanding into frontier areas of the Arctic, where slow-growing tundra vegetation and the underlying permafrost soils are very sensitive to disturbance. The creation of vehicle trails on the tundra from seismic exploration for oil has accelerated in the past decade, and the cumulative impact represents a geographic footprint that covers a greater extent of Alaska’s North Slope tundra than all other direct human impacts combined. Seismic exploration for oil and gas was conducted on the coastal plain of the Arctic National Wildlife Refuge, Alaska, USA, in the winters of 1984 and 1985. This study documents recovery of vegetation and permafrost soils over a two-decade period after vehicle traffic on snow-covered tundra. Paired permanent vegetation plots (disturbed vs. reference) were monitored six times from 1984 to 2002. Data were collected on percent vegetative cover by plant species and on soil and ground ice characteristics. We developed Bayesian hierarchical models, with temporally and spatially autocorrelated errors, to analyze the effects of vegetation type and initial disturbance levels on recovery patterns of the different plant growth forms as well as soil thaw depth. Plant community composition was altered on the trails by species-specific responses to initial disturbance and subsequent changes in substrate. Long-term changes included increased cover of graminoids and decreased cover of evergreen shrubs and mosses. Trails with low levels of initial disturbance usually improved well over time, whereas those with medium to high levels of initial disturbance recovered slowly. Trails on ice-poor, gravel substrates of riparian areas recovered better than those on ice-rich loamy soils of the uplands, even after severe initial damage. Recovery to pre-disturbance communities was not possible where trail subsidence occurred due to thawing of ground ice. Previous studies of disturbance from winter seismic vehicles in the Arctic predicted short-term and mostly aesthetic impacts, but we found that severe impacts to tundra vegetation persisted for two decades after disturbance under some conditions. We recommend management approaches that should be used to prevent persistent tundra damage.

Proposta de metodologia para gestão de produção, visando redução no consumo de energia elétrica em uma unidade fabril do setor de bens de consumo

Actually the energy efficiency is making more space in the industry, due to the search for the sustainability, the electrical energy costs reduction, the goals achievement or the efficiency of production processes. In consumer goods industries, such a beverage industry, as the work is based, the productivity is directly related to the electrical energy consumption. The development of methodologies and/or routines, in addition to some tools which allow to align more efficiently these two aspects (production and consumption of electrical energy), in the viewpoint of the Energy Conservation, is very important. In this case, the study will show the Plant Modulation concepts, a production management methodology, based in some factors related to the productive process, installed equipment, production supplies and energy cost. The proposed methodology was implanted in a plant along 2015 and show the results, in face to confirm its efficiency. Finally, in this study, it was shown the capacity of Plant Modulation to positively impact in the energy efficiency inside a big industry

Estimativa do potencial de acréscimo da oferta de energia no setor sucroalcooleiro paulista a partir de cogeração e gaseificação do bagaço de cana-de-açúcar com utilização de ciclo combinado

This work evaluates the existing potential in the state of Sao Paulo for the generation of electrical energy using the sugar cane bagasse as fuel. As the bagasse is a by-product of the sugarcane and alcohol industry and it is produced in large scale in the country, mainly in the state of Sao Paulo, it is important to develop researches that aim the best utilization of this input. In order to determine its potential, at first, a study was conducted considering the utilization of the cogeneration, which is a common practice in the plants of the sector. However, it was concluded that the cogeneration could provide a higher quantity of energy if more modern technologies and more efficient processes were used. Another study to estimate the potential considered a system of gasification of the sugar cane bagasse integrated with the combined cycle (BIG/GTCC). It was concluded that this technology can provide a considerable increase in the electrical supply. In this work it was also developed an energetic study based on real data from a plant located in the state of Sao Paulo. A thermodynamic analysis was done in the existing equipment of the cogeneration section of the plant. And another analysis was done considering the implementation of the BIG/GTCC technology to the cogeneration system. Comparing the results of both settings, it was concluded that the utilization of the sugar cane bagasse integrated to a combined cycle increased considerably the efficiency in the generation of electricity of the plant, increasing more than six times its production capacity of electrical energy

Efeito do filme de carbono DLC aplicado por plasma como recobrimento em Polietileno de Ultra Densidade e Alto Peso Molecular (UHWPE) e planejamento virtual utilizando a técnica de elementos finitos em cirúrgias de próteses totais da ATM

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Integrated pulp and paper mill planning and scheduling

This article describes a real-world production planning and scheduling problem occurring at an integrated pulp and paper mill (P&P) which manufactures paper for cardboard out of produced pulp. During the cooking of wood chips in the digester, two by-products are produced: the pulp itself (virgin fibers) and the waste stream known as black liquor. The former is then mixed with recycled fibers and processed in a paper machine. Here, due to significant sequence-dependent setups in paper type changeovers, sizing and sequencing of lots have to be made simultaneously in order to efficiently use capacity. The latter is converted into electrical energy using a set of evaporators, recovery boilers and counter-pressure turbines. The planning challenge is then to synchronize the material flow as it moves through the pulp and paper mills, and energy plant, maximizing customer demand (as backlogging is allowed), and minimizing operation costs. Due to the intensive capital feature of P&P, the output of the digester must be maximized. As the production bottleneck is not fixed, to tackle this problem we propose a new model that integrates the critical production units associated to the pulp and paper mills, and energy plant for the first time. Simple stochastic mixed integer programming based local search heuristics are developed to obtain good feasible solutions for the problem. The benefits of integrating the three stages are discussed. The proposed approaches are tested on real-world data. Our work may help P&P companies to increase their competitiveness and reactiveness in dealing with demand pattern oscillations. (C) 2012 Elsevier Ltd. All rights reserved.

Linear parameter varying control design for rotating systems supported by journal bearings

Linear parameter varying (LPV) control is a model-based control technique that takes into account time-varying parameters of the plant. In the case of rotating systems supported by lubricated bearings, the dynamic characteristics of the bearings change in time as a function of the rotating speed. Hence, LPV control can tackle the problem of run-up and run-down operational conditions when dynamic characteristics of the rotating system change significantly in time due to the bearings and high vibration levels occur. In this work, the LPV control design for a flexible shaft supported by plain journal bearings is presented. The model used in the LPV control design is updated from unbalance response experimental results and dynamic coefficients for the entire range of rotating speeds are obtained by numerical optimization. Experimental implementation of the designed LPV control resulted in strong reduction of vibration amplitudes when crossing the critical speed, without affecting system behavior in sub- or supercritical speeds. (C) 2012 Elsevier Ltd. All rights reserved.

Network-based high level data classification

Traditional supervised data classification considers only physical features (e. g., distance or similarity) of the input data. Here, this type of learning is called low level classification. On the other hand, the human (animal) brain performs both low and high orders of learning and it has facility in identifying patterns according to the semantic meaning of the input data. Data classification that considers not only physical attributes but also the pattern formation is, here, referred to as high level classification. In this paper, we propose a hybrid classification technique that combines both types of learning. The low level term can be implemented by any classification technique, while the high level term is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies the test instances by their physical features or class topologies, while the latter measures the compliance of the test instances to the pattern formation of the data. Our study shows that the proposed technique not only can realize classification according to the pattern formation, but also is able to improve the performance of traditional classification techniques. Furthermore, as the class configuration's complexity increases, such as the mixture among different classes, a larger portion of the high level term is required to get correct classification. This feature confirms that the high level classification has a special importance in complex situations of classification. Finally, we show how the proposed technique can be employed in a real-world application, where it is capable of identifying variations and distortions of handwritten digit images. As a result, it supplies an improvement in the overall pattern recognition rate.

Network-based stochastic semisupervised learning

Semisupervised learning is a machine learning approach that is able to employ both labeled and unlabeled samples in the training process. In this paper, we propose a semisupervised data classification model based on a combined random-preferential walk of particles in a network (graph) constructed from the input dataset. The particles of the same class cooperate among themselves, while the particles of different classes compete with each other to propagate class labels to the whole network. A rigorous model definition is provided via a nonlinear stochastic dynamical system and a mathematical analysis of its behavior is carried out. A numerical validation presented in this paper confirms the theoretical predictions. An interesting feature brought by the competitive-cooperative mechanism is that the proposed model can achieve good classification rates while exhibiting low computational complexity order in comparison to other network-based semisupervised algorithms. Computer simulations conducted on synthetic and real-world datasets reveal the effectiveness of the model.

Feasibility Study of a Solar Reactor for Phenol Treatment by the Photo-Fenton process in Aqueous Solution

Solar reactors can be attractive in photodegradation processes due to lower electrical energy demand. The performance of a solar reactor for two flow configurations, i.e., plug flow and mixed flow, is compared based on experimental results with a pilot-scale solar reactor. Aqueous solutions of phenol were used as a model for industrial wastewater containing organic contaminants. Batch experiments were carried out under clear sky, resulting in removal rates in the range of 96100?%. The dissolved organic carbon removal rate was simulated by an empirical model based on neural networks, which was adjusted to the experimental data, resulting in a correlation coefficient of 0.9856. This approach enabled to estimate effects of process variables which could not be evaluated from the experiments. Simulations with different reactor configurations indicated relevant aspects for the design of solar reactors.