Modelo de operação de unidades de fragmentação no tratamento de veículos em fim de vida

A recuperação de metais a partir de resíduos é uma actividade ligada à valorização de resíduos que se afigura essencial à preservação de recursos naturais. O tratamento de resíduos metálicos é essencial para o sucesso da reciclagem. O tratamento permite diminuir contaminações com outros materiais, melhorar a valorização e diminuir os custos de transporte. Ao tratamento encontram-se associadas diferentes operações, cuja finalidade é preparar os metais de acordo com os requisitos de processo dos recicladores. A fragmentação é uma das operações de pré-tratamento mais relevantes pela sua capacidade de processamento e pela possibilidade de permitir separar resíduos contaminados com outros materiais. O presente trabalho efectua uma análise inicial ao sector e às tecnologias existentes permitindo introduzir posteriormente o caso de estudo, a instalação em Vila Nova de Gaia da empresa Constantino Fernandes Oliveira & Filhos, SA. O enfoque ao caso de estudo aborda o tema da eficiência energética, um dos principais custos de produção. Nesta perspectiva identifica pontos críticos e estabelece medidas de eficiência energética que permitam reduzir o consumo energético. As medidas propostas foram a colocação de um variador electrónico de velocidade no ventilador principal da fragmentadora e no compressor e a substituição da iluminação existente por iluminação LED. As medidas propostas permitem a redução do consumo específico de energia de 9,39 kgep/t para 8,81 kgep/t. Sendo a fragmentação uma das operações mais relevantes na actividade da instalação estudada, no presente trabalho, efectuou-se a análise do modelo operatório com recurso à aplicação STAN de análise de fluxos de materiais. O procedimento de análise envolveu a criação de vários cenários de exploração no tratamento de resíduos. O objectivo das simulações é a contabilização dos custos referentes ao tratamento dos resíduos permitindo melhorias na vertente operacional, ambiental e económica. Um dos cenários simulados foi a remoção dos veículos em fim de vida dos resíduos a fragmentar, onde se constatou que apesar de uma redução dos resíduos processados, os proveitos por quantidade processada não alteram relativamente ao modelo operatório de base. Este facto deve-se sobretudo aos custos elevados de tratamento de resíduos gerados no processo de fragmentação dos veículos em fim de vida.

Analysis of N-nitrosames in water using micellar electrokinetic chromatography (MEKC) and development of online and offline methods of extraction and enrichment of neutral polar analytes

Evaluation of the quality of the environment is essential for human wellness as pollutants in trace amounts can cause serious health problem. Nitrosamines are a group of compounds that are considered potential carcinogens and can be found in drinking water (as disinfection byproducts), foods, beverages and cosmetics. To monitor the level of these compounds to minimize daily intakes, fast and reliable analytical techniques are required. As these compounds are relatively highly polar, extraction and enrichment from environmental samples (aqueous) are challenging. Also, the trend of analytical techniques toward the reduction of sample size and minimization of organic solvent use demands new methods of analysis. In light of fulfilling these requirements, a new method of online preconcentration tailored to an electrokinetic chromatography is introduced. In this method, electroosmotic flow (EOF) was suppressed to increase the interaction time between analyte and micellar phase, therefore the only force to mobilize the neutral analytes is the interaction of analyte with moving micelles. In absence of EOF, polarity of applied potential was switched (negative or positive) to force (anionic or cationic) micelles to move toward the detector. To avoid the excessive band broadening due to longer analysis time caused by slow moving micelles, auxiliary pressure was introduced to boost the micelle movement toward the detector using an in house designed and built apparatus. Applying the external auxiliary pressure significantly reduced the analysis times without compromising separation efficiency. Parameters, such as type of surfactants, composition of background electrolyte (BGE), type of capillary, matrix effect, organic modifiers, etc., were evaluated in optimization of the method. The enrichment factors for targeted analytes were impressive, particularly; cationic surfactants were shown to be suitable for analysis of nitrosamines due to their ability to act as hydrogen bond donors. Ammonium perfluorooctanoate (APFO) also showed remarkable results in term of peak shapes and number of theoretical plates. It was shown that the separation results were best when a high conductivity sample was paired with a BGE of lower conductivity. Using higher surfactant concentrations (up to 200 mM SDS) than usual (50 mM SDS) for micellar electrokinetic chromatography (MEKC) improved the sweeping. A new method for micro-extraction and enrichment of highly polar neutral analytes (N-Nitrosamines in particular) based on three-phase drop micro-extraction was introduced and its performance studied. In this method, a new device using some easy-to-find components was fabricated and its operation and application demonstrated. Compared to conventional extraction methods (liquid-liquid extraction), consumption of organic solvents and operation times were significantly lower.

Mediação na redução de riscos e minimização de danos na toxicodependência: práticas e desafios

Trabalho de Projeto apresentado à Escola Superior de Educação de Paula Frassinetti para obtenção do grau de Mestre em Intervenção Comunitária, especialização em Educação Para a Saúde

Deformation mechanisms in second-phase affected microstructures and their energy balance

Based on the relationship Zener parameter (Z=second-phase size/second-phase volume fraction) vs. calcite grain size (dg), second-phase controlled aggregates and microstructures that are weakly affected by second-phases are discriminated. The latter are characterized by large but constant grain sizes, high calcite grain boundary fractions and crystallographic preferred orientations (CPO), while calcite grain size and calcite grain boundary fraction decrease continuously and CPO weakens with decreasing Z in second-phase controlled microstructures. These observations suggest that second-phase controlled microstructures predominantly deform via granular flow because pinning of calcite grain boundaries reduces the efficiency of dynamic recrystallization favoring mass transfer processes and grain boundary sliding. In contrast, the balance of grain size reduction and growth by dynamic recrystallization maintains a steady state grain size in microstructures that are only weakly affected by second-phases promoting a predominance of dislocation creep. With increasing temperature, the relationship between Z and dg persists but the calcite grain size increases continuously. Based on microstructures, the energy of each modifying process is calculated and its relative contribution is compared with energies of the competing processes (surface energy, dragging energy, dynamic recrystallization energy). The steady state microstructures result from a temperature-dependent energy minimization procedure of the system.

Relating bisimulations with attractors in boolean network models

When studying a biological regulatory network, it is usual to use boolean network models. In these models, boolean variables represent the behavior of each component of the biological system. Taking in account that the size of these state transition models grows exponentially along with the number of components considered, it becomes important to have tools to minimize such models. In this paper, we relate bisimulations, which are relations used in the study of automata (general state transition models) with attractors, which are an important feature of biological boolean models. Hence, we support the idea that bisimulations can be important tools in the study some main features of boolean network models.We also discuss the differences between using this approach and other well-known methodologies to study this kind of systems and we illustrate it with some examples.

Solid-liquid interactions in microscale structures and devices

Liquid-solid interactions become important as dimensions approach mciro/nano-scale. This dissertation focuses on liquid-solid interactions in two distinct applications: capillary driven self-assembly of thin foils into 3D structures, and droplet wetting of hydrophobic micropatterned surfaces. The phenomenon of self-assembly of complex structures is common in biological systems. Examples include self-assembly of proteins into macromolecular structures and self-assembly of lipid bilayer membranes. The principles governing this phenomenon have been applied to induce self-assembly of millimeter scale Si thin films into spherical and other 3D structures, which are then integrated into light-trapping photovoltaic (PV) devices. Motivated by this application, we present a generalized analytical study of the self-folding of thin plates into deterministic 3D shapes, through fluid-solid interactions, to be used as PV devices. This study consists of developing a model using beam theory, which incorporates the two competing components — a capillary force that promotes folding and the bending rigidity of the foil that resists folding into a 3D structure. Through an equivalence argument of thin foils of different geometry, an effective folding parameter, which uniquely characterizes the driving force for folding, has been identified. A criterion for spontaneous folding of an arbitrarily shaped 2D foil, based on the effective folding parameter, is thus established. Measurements from experiments using different materials and predictions from the model match well, validating the assumptions used in the analysis. As an alternative to the mechanics model approach, the minimization of the total free energy is employed to investigate the interactions between a fluid droplet and a flexible thin film. A 2D energy functional is proposed, comprising the surface energy of the fluid, bending energy of the thin film and gravitational energy of the fluid. Through simulations with Surface Evolver, the shapes of the droplet and the thin film at equilibrium are obtained. A critical thin film length necessary for complete enclosure of the fluid droplet, and hence successful self-assembly into a PV device, is determined and compared with the experimental results and mechanics model predictions. The results from the modeling and energy approaches and the experiments are all consistent. Superhydrophobic surfaces, which have unique properties including self-cleaning and water repelling are desired in many applications. One excellent example in nature is the lotus leaf. To fabricate these surfaces, well designed micro/nano- surface structures are often employed. In this research, we fabricate superhydrophobic micropatterned Polydimethylsiloxane (PDMS) surfaces composed of micropillars of various sizes and arrangements by means of soft lithography. Both anisotropic surfaces, consisting of parallel grooves and cylindrical pillars in rectangular lattices, and isotropic surfaces, consisting of cylindrical pillars in square and hexagonal lattices, are considered. A novel technique is proposed to image the contact line (CL) of the droplet on the hydrophobic surface. This technique provides a new approach to distinguish between partial and complete wetting. The contact area between droplet and microtextured surface is then measured for a droplet in the Cassie state, which is a state of partial wetting. The results show that although the droplet is in the Cassie state, the contact area does not necessarily follow Cassie model predictions. Moreover, the CL is not circular, and is affected by the micropatterns, in both isotropic and anisotropic cases. Thus, it is suggested that along with the contact angle — the typical parameter reported in literature quantifying wetting, the size and shape of the contact area should also be presented. This technique is employed to investigate the evolution of the CL on a hydrophobic micropatterned surface in the cases of: a single droplet impacting the micropatterned surface, two droplets coalescing on micropillars, and a receding droplet resting on the micropatterned surface. Another parameter which quantifies hydrophobicity is the contact angle hysteresis (CAH), which indicates the resistance of the surface to the sliding of a droplet with a given volume. The conventional methods of using advancing and receding angles or tilting stage to measure the resistance of the micropatterned surface are indirect, without mentioning the inaccuracy due to the discrete and stepwise motion of the CL on micropillars. A micronewton force sensor is utilized to directly measure the resisting force by dragging a droplet on a microtextured surface. Together with the proposed imaging technique, the evolution of the CL during sliding is also explored. It is found that, at the onset of sliding, the CL behaves as a linear elastic solid with a constant stiffness. Afterwards, the force first increases and then decreases and reaches a steady state, accompanied with periodic oscillations due to regular pinning and depinning of the CL. Both the maximum and steady state forces are primarily dependent on area fractions of the micropatterned surfaces in our experiment. The resisting force is found to be proportional to the number of pillars which pin the CL at the trailing edge, validating the assumption that the resistance mainly arises from the CL pinning at the trailing edge. In each pinning-and-depinning cycle during the steady state, the CL also shows linear elastic behavior but with a lower stiffness. The force variation and energy dissipation involved can also be determined. This novel method of measuring the resistance of the micropatterned surface elucidates the dependence on CL pinning and provides more insight into the mechanisms of CAH.

Modelo origen destino para estimar el flujo de tráfico usando algoritmos genéticos

En este trabajo se ha desarrollado un nuevo método basado en Inteligencia Artificial para resolver un problema del matriz origen-destino (O-D) aplicado al caso de una red de tráfico vehicular en la ciudad de Ambato. El método implementado, basado en algoritmos genéticos (AG), resuelve el problema de minimización asociado al problema de matriz O-D. Para validar la técnica, se ha utilizado una red vial correspondiente a la zona del Mercado Modelo en la ciudad de Ambato, que es una zona de alta congestión vehicular.

Dynamic Programming Approaches for Estimating and Applying Large-scale Discrete Choice Models

People go through their life making all kinds of decisions, and some of these decisions affect their demand for transportation, for example, their choices of where to live and where to work, how and when to travel and which route to take. Transport related choices are typically time dependent and characterized by large number of alternatives that can be spatially correlated. This thesis deals with models that can be used to analyze and predict discrete choices in large-scale networks. The proposed models and methods are highly relevant for, but not limited to, transport applications. We model decisions as sequences of choices within the dynamic discrete choice framework, also known as parametric Markov decision processes. Such models are known to be difficult to estimate and to apply to make predictions because dynamic programming problems need to be solved in order to compute choice probabilities. In this thesis we show that it is possible to explore the network structure and the flexibility of dynamic programming so that the dynamic discrete choice modeling approach is not only useful to model time dependent choices, but also makes it easier to model large-scale static choices. The thesis consists of seven articles containing a number of models and methods for estimating, applying and testing large-scale discrete choice models. In the following we group the contributions under three themes: route choice modeling, large-scale multivariate extreme value (MEV) model estimation and nonlinear optimization algorithms. Five articles are related to route choice modeling. We propose different dynamic discrete choice models that allow paths to be correlated based on the MEV and mixed logit models. The resulting route choice models become expensive to estimate and we deal with this challenge by proposing innovative methods that allow to reduce the estimation cost. For example, we propose a decomposition method that not only opens up for possibility of mixing, but also speeds up the estimation for simple logit models, which has implications also for traffic simulation. Moreover, we compare the utility maximization and regret minimization decision rules, and we propose a misspecification test for logit-based route choice models. The second theme is related to the estimation of static discrete choice models with large choice sets. We establish that a class of MEV models can be reformulated as dynamic discrete choice models on the networks of correlation structures. These dynamic models can then be estimated quickly using dynamic programming techniques and an efficient nonlinear optimization algorithm. Finally, the third theme focuses on structured quasi-Newton techniques for estimating discrete choice models by maximum likelihood. We examine and adapt switching methods that can be easily integrated into usual optimization algorithms (line search and trust region) to accelerate the estimation process. The proposed dynamic discrete choice models and estimation methods can be used in various discrete choice applications. In the area of big data analytics, models that can deal with large choice sets and sequential choices are important. Our research can therefore be of interest in various demand analysis applications (predictive analytics) or can be integrated with optimization models (prescriptive analytics). Furthermore, our studies indicate the potential of dynamic programming techniques in this context, even for static models, which opens up a variety of future research directions.

Thermal and exergy analysis for evaluation and optimization of performance of cooling towers for fresh water and seawater

In this study, thermal, exergetic analysis and performance evaluation of seawater and fresh wet cooling tower and the effect of parameters on its performance is investigated. With using of energy and mass balance equations, experimental results, a mathematical model and EES code developed. Due to lack of fresh water, seawater cooling is interesting choice for future of cooling, so the effect of seawater in the range of 1gr/kg to 60gr/kg for salinity on the performance characteristics like air efficiency, water efficiency, output water temperature of cooling tower, flow of the exergy, and the exergy efficiency with comparison with fresh water examined. Decreasing of air efficiency about 3%, increasing of water efficiency about 1.5% are some of these effects. Moreover with formation of fouling the performance of cooling tower decreased about 15% which this phenomena and its effects like increase in output water temperature and tower excess volume has been showed and also accommodate with others work. Also optimization for minimizing cost, maximizing air efficiency, and minimizing exergy destruction has been done, results showed that optimization on minimizing the exergy destruction has been satisfy both minimization of the cost and the maximization of the air efficiency, although it will not necessarily permanent for all inputs and optimizations. Validation of this work is done by comparing computational results and experimental data which showed that the model have a good accuracy.

Modelling of an efficient dynamic smart solar photovoltaic power grid system

A smart solar photovoltaic grid system is an advent of innovation coherence of information and communications technology (ICT) with power systems control engineering via the internet [1]. This thesis designs and demonstrates a smart solar photovoltaic grid system that is selfhealing, environmental and consumer friendly, but also with the ability to accommodate other renewable sources of energy generation seamlessly, creating a healthy competitive energy industry and optimising energy assets efficiency. This thesis also presents the modelling of an efficient dynamic smart solar photovoltaic power grid system by exploring the maximum power point tracking efficiency, optimisation of the smart solar photovoltaic array through modelling and simulation to improve the quality of design for the solar photovoltaic module. In contrast, over the past decade quite promising results have been published in literature, most of which have not addressed the basis of the research questions in this thesis. The Levenberg-Marquardt and sparse based algorithms have proven to be very effective tools in helping to improve the quality of design for solar photovoltaic modules, minimising the possible relative errors in this thesis. Guided by theoretical and analytical reviews in literature, this research has carefully chosen the MatLab/Simulink software toolbox for modelling and simulation experiments performed on the static smart solar grid system. The auto-correlation coefficient results obtained from the modelling experiments give an accuracy of 99% with negligible mean square error (MSE), root mean square error (RMSE) and standard deviation. This thesis further explores the design and implementation of a robust real-time online solar photovoltaic monitoring system, establishing a comparative study of two solar photovoltaic tracking systems which provide remote access to the harvested energy data. This research made a landmark innovation in designing and implementing a unique approach for online remote access solar photovoltaic monitoring systems providing updated information of the energy produced by the solar photovoltaic module at the site location. In addressing the challenge of online solar photovoltaic monitoring systems, Darfon online data logger device has been systematically integrated into the design for a comparative study of the two solar photovoltaic tracking systems examined in this thesis. The site location for the comparative study of the solar photovoltaic tracking systems is at the National Kaohsiung University of Applied Sciences, Taiwan, R.O.C. The overall comparative energy output efficiency of the azimuthal-altitude dual-axis over the 450 stationary solar photovoltaic monitoring system as observed at the research location site is about 72% based on the total energy produced, estimated money saved and the amount of CO2 reduction achieved. Similarly, in comparing the total amount of energy produced by the two solar photovoltaic tracking systems, the overall daily generated energy for the month of July shows the effectiveness of the azimuthal-altitude tracking systems over the 450 stationary solar photovoltaic system. It was found that the azimuthal-altitude dual-axis tracking systems were about 68.43% efficient compared to the 450 stationary solar photovoltaic systems. Lastly, the overall comparative hourly energy efficiency of the azimuthal-altitude dual-axis over the 450 stationary solar photovoltaic energy system was found to be 74.2% efficient. Results from this research are quite promising and significant in satisfying the purpose of the research objectives and questions posed in the thesis. The new algorithms introduced in this research and the statistical measures applied to the modelling and simulation of a smart static solar photovoltaic grid system performance outperformed other previous works in reviewed literature. Based on this new implementation design of the online data logging systems for solar photovoltaic monitoring, it is possible for the first time to have online on-site information of the energy produced remotely, fault identification and rectification, maintenance and recovery time deployed as fast as possible. The results presented in this research as Internet of things (IoT) on smart solar grid systems are likely to offer real-life experiences especially both to the existing body of knowledge and the future solar photovoltaic energy industry irrespective of the study site location for the comparative solar photovoltaic tracking systems. While the thesis has contributed to the smart solar photovoltaic grid system, it has also highlighted areas of further research and the need to investigate more on improving the choice and quality design for solar photovoltaic modules. Finally, it has also made recommendations for further research in the minimization of the absolute or relative errors in the quality and design of the smart static solar photovoltaic module.

Planar Odometry from a Radial Laser Scanner. A Range Flow-based Approach

In this paper we present a fast and precise method to estimate the planar motion of a lidar from consecutive range scans. For every scanned point we formulate the range flow constraint equation in terms of the sensor velocity, and minimize a robust function of the resulting geometric constraints to obtain the motion estimate. Conversely to traditional approaches, this method does not search for correspondences but performs dense scan alignment based on the scan gradients, in the fashion of dense 3D visual odometry. The minimization problem is solved in a coarse-to-fine scheme to cope with large displacements, and a smooth filter based on the covariance of the estimate is employed to handle uncertainty in unconstraint scenarios (e.g. corridors). Simulated and real experiments have been performed to compare our approach with two prominent scan matchers and with wheel odometry. Quantitative and qualitative results demonstrate the superior performance of our approach which, along with its very low computational cost (0.9 milliseconds on a single CPU core), makes it suitable for those robotic applications that require planar odometry. For this purpose, we also provide the code so that the robotics community can benefit from it.

Robust Stereo Visual Odometry through a Probabilistic Combination of Points and Line Segments

Most approaches to stereo visual odometry reconstruct the motion based on the tracking of point features along a sequence of images. However, in low-textured scenes it is often difficult to encounter a large set of point features, or it may happen that they are not well distributed over the image, so that the behavior of these algorithms deteriorates. This paper proposes a probabilistic approach to stereo visual odometry based on the combination of both point and line segment that works robustly in a wide variety of scenarios. The camera motion is recovered through non-linear minimization of the projection errors of both point and line segment features. In order to effectively combine both types of features, their associated errors are weighted according to their covariance matrices, computed from the propagation of Gaussian distribution errors in the sensor measurements. The method, of course, is computationally more expensive that using only one type of feature, but still can run in real-time on a standard computer and provides interesting advantages, including a straightforward integration into any probabilistic framework commonly employed in mobile robotics.

Plataforma interactiva para edifícios inteligentes com controlo preditivo

Esta dissertação desenvolve uma plataforma de controlo interactiva para edifícios inteligentes através de um sistema SCADA (Supervisory Control And Data Acquisition). Este sistema SCADA integra diferentes tipos de informações provenientes das várias tecnologias presentes em edifícios modernos (controlo da ventilação, temperatura, iluminação, etc.). A estratégia de controlo desenvolvida implementa um controlador em cascada hierárquica onde os "loops" interiores são executados pelos PLC's locais (Programmable Logic Controller), e o "loop" exterior é gerido pelo sistema SCADA centralizado, que interage com a rede local de PLC's. Nesta dissertação é implementado um controlador preditivo na plataforma SCADA centralizada. São apresentados testes efectuados para o controlo da temperatura e luminosidade de salas com uma grande área. O controlador preditivo desenvolvido tenta optimizar a satisfação dos utilizadores, com base nas preferências introduzidas em várias interfaces distribuídas, sujeito às restrições de minimização do desperdício de energia. De forma a executar o controlador preditivo na plataforma SCADA foi desenvolvido um canal de comunicação para permitir a comunicação entre a aplicação SCADA e a aplicação MATLAB, onde o controlador preditivo é executado. ABSTRACT: This dissertation develops an operational control platform for intelligent buildings using a SCADA system (Supervisory Control And Data Acquisition). This SCADA system integrates different types of information coming from the several technologies present in modem buildings (control of ventilation, temperature, illumination, etc.). The developed control strategy implements a hierarchical cascade controller where inner loops are performed by local PLCs (Programmable Logic Controller), and the outer loop is managed by the centralized SCADA system, which interacts with the entire local PLC network. ln this dissertation a Predictive Controller is implemented at the centralized SCADA platform. Tests applied to the control of temperature and luminosity in huge­area rooms are presented. The developed Predictive Controller tries to optimize the satisfaction of user explicit preferences coming from several distributed user-interfaces, subjected to the constraints of energy waste minimization. ln order to run the Predictive Controller at the SCADA platform a communication channel was developed to allow communication between the SCADA application and the MATLAB application where the Predictive Controller runs.

Motion compensation with minimal residue dispersion matching criteria

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnoloigia, 2016.

Linfócitos T : uma análise do perfil diferencial da expressão gênica na imunorregulação

Tese (doutorado)—Universidade de Brasília, Faculdade de Medicina, Pós-Graduação em Patologia Molecular, 2016.