Estudo de caso: minimização e reúso de água em shopping center da região sul do Brasil

The main issues related to water conservation in urban centers are the increase in water supply cost, demand growth, pollution and differences in the distribution of water resources. Water conservation, the controlled and efficient use of water, includes both measures as reasonable means of water reuse. Thus, conservation practices are an effective way to meet demand and supply water to new activities and users without jeopardizing the supplying water bodies and preserving the natural environment. This study aims to examine the water management of a shopping mall and the use of rainwater harvesting combined with greywater reuse. For buildings in general, water loss is common due to leaks in the hydraulic and restroom equipment. These losses, which are caused by a high volume of water used and wasted in the system, are often the result of design errors, incorrect maintenance procedures and users' bad habits In southern Brazil, where there is rainfall almost all year long, water shortages occasionally occur, particularly in some winter mouths. One difficulty that appears on rainwater studies is the proper determination of rainwater volume that can be used to address water supply systems. In this work, the simulation method was used to determine this volume. Thus, simulations with the following variables: rainfall, catchment area and water consumption were performed. For mall's hydraulic systems, segmented alternatives are adopted. That is, focusing on the use of rainwater or greywater reuse. Other alternatives of effluent reuse have been slightly discussed due to sanitary issues, those are effluents from toilets and kitchen sinks. The adoption of greywater may be feasible if there is a significant flow of greywater to comply water demand for toilet flushing. The inspections made in this study found that the quantity of sinks was insufficient to supply an adequate amount of water to toilets and urinals. The greywater reuse system was found to be infeasible in terms of demand and supply of water. Conversely, the rainwater harvesting system was entirely feasible and easily supplied water to all restrooms and contributed to the cooling of the air conditioning system with a short payback period. One of the challenges of this work was the need to compare the actual water consumption with a water consumption parameter used in buildings. Thus, a method that addresses the generation of specific consumption indexes for specific activity (like a mall) was used. The water consumption indices showed that this mall has a satisfactory water management program.

Estudo das propriedades físico-químicas e avaliação de compostos bioativos em camarinhas ('Corema album' L.D. Don)

As camarinhas ('Corema album' L. D. Don) são pequenos frutos selvagens que se desenvolvem em arbustos dunares ao longo das costas da Península Ibérica. Apesar de serem ainda pouco conhecidas, as camarinhas são pequenos frutos que podem pertencer ao vasto grupo dos frutos vermelhos, vulgarmente conhecidos pelos seus efeitos benéficos na saúde. Desta forma, neste estudo foram realizadas várias análises às camarinhas no que respeita a propriedades de natureza física e química e em particular a alguns compostos com efeitos bioativos. Com a realização deste trabalho pretendeu-se avaliar as propriedades físico-químicas das camarinhas, bem como dos compostos bioativos com potenciais benefícios para a saúde. Nesse sentido, as bagas de camarinha foram avaliadas quanto às suas propriedades físicas (dimensões, peso, cor e textura), propriedades químicas (humidade, acidez, ºBrix, fibra, açúcares totais, açúcares redutores e vitamina C) e propriedades fenólicas (compostos fenólicos totais, orto-difenóis, flavonóides, taninos e atividade antioxidante, por DPPH e ABTS), em diferentes extratos de amostras de polpas e de grainhas liofilizadas. Os primeiros extratos ainda foram submetidos a uma simulações das condições do trato digestivo, para avaliar a bioacessibilidade dos compostos fenólicos e da atividade antioxidante. Este trabalho teve ainda como objetivo conhecer a bioacessibilidade dos compostos fenólicos totais e da sua atividade antioxidante, através do método da simulação “in vitro” das diferentes etapas do trato gastrointestinal. No que diz respeito às propriedades físicas analisadas, as camarinhas frescas demonstraram uma altura média de 8,6 mm, um diâmetro médio de 9,4 mm e uma massa média de 0,7 g. Relativamente à caraterização da cor, estas apresentavam uma cor clara (L*=79,8). Quanto à textura apresentaram alguma elasticidade média (2,9 mm) e uma baixa dureza (1,9 N). Nas análises químicas as camarinhas revelaram ser compostas, maioritariamente, por água (87,9%), por açúcares totais e fibras. Para além disso, apresentavam um teor em sólidos solúveis totais de 6,3 ºBrix, uma acidez de 1,4 g ácido tartárico/100 g e um teor de vitamina C de 2,8 mg de ácido ascórbico/100 g.Na quantificação dos compostos fenólicos totais e flavonóides, os extratos de acetona:água das amostras de polpas de camarinha branca apresentaram os valores mais elevados, 1614,1 mg EAG/100 g e 143,7 mg EQ/100 g, respetivamente. Relativamente à quantificação dos orto-difenóis e dos taninos os extratos de metanol e de acetona:água das amostras de grainhas de camarinha branca registaram os valores de 23,4 mg EAG/100 g e 915,7 mg/100 g, respetivamente. Na atividade antioxidante por DPPH e ABTS os extratos de acetona:água das amostras de polpas de camarinha branca apresentaram, respetivamente, os valores de 40,1 µmol ET/g e de 79,6 µmol ET/g. Na avaliação da bioacessibilidade verificou-se que ocorreu uma maior percentagem de compostos fenólicos disponíveis para absorção e uma maior preservação da sua atividade antioxidante nos extratos das grainhas, comparativamente aos extratos das polpas. Com este estudo concluiu-se que as camarinhas são pequenos frutos portadores de um grande potencial em diversos compostos bioativos benéficos para a saúde dos consumidores.

Valor en Riesgo: Evaluación del desempeño de diferentes metodologías para 7 países latinoamericanos

Este documento evalúa el comportamiento de diferentes métodos (paramétrico, no paramétricos y semi-paramétricos) para estimar el VaR (valor en riesgo) de un portafolio representativo para 7 países latinoamericanos. El cálculo del VaR implica la estimación del i-esimo percentil de la distribución del valor futuro del valor de un portafolio. Los resultados no muestran la existencia de un método que se comporte mejor que los demás. Con un nivel de confianza del 95% los modelos paramétricos que emplean el EWMA se desempeñan en general bien así como con el TGARCH, pero estos modelos tienen un comportamiento pobre cuando la significancia considerada es del 1%.

Predição não-linear de curvas de produção de petróleo via redes neurais recursivas

One of the main activities in the petroleum engineering is to estimate the oil production in the existing oil reserves. The calculation of these reserves is crucial to determine the economical feasibility of your explotation. Currently, the petroleum industry is facing problems to analyze production due to the exponentially increasing amount of data provided by the production facilities. Conventional reservoir modeling techniques like numerical reservoir simulation and visualization were well developed and are available. This work proposes intelligent methods, like artificial neural networks, to predict the oil production and compare the results with the ones obtained by the numerical simulation, method quite a lot used in the practice to realization of the oil production prediction behavior. The artificial neural networks will be used due your learning, adaptation and interpolation capabilities

A stochastic model updating method for parameter variability quantification based on response surface models and Monte Carlo simulation

Stochastic model updating must be considered for quantifying uncertainties inherently existing in real-world engineering structures. By this means the statistical properties,instead of deterministic values, of structural parameters can be sought indicating the parameter variability. However, the implementation of stochastic model updating is much more complicated than that of deterministic methods particularly in the aspects of theoretical complexity and low computational efficiency. This study attempts to propose a simple and cost-efficient method by decomposing a stochastic updating process into a series of deterministic ones with the aid of response surface models and Monte Carlo simulation. The response surface models are used as surrogates for original FE models in the interest of programming simplification, fast response computation and easy inverse optimization. Monte Carlo simulation is adopted for generating samples from the assumed or measured probability distributions of responses. Each sample corresponds to an individual deterministic inverse process predicting the deterministic values of parameters. Then the parameter means and variances can be statistically estimated based on all the parameter predictions by running all the samples. Meanwhile, the analysis of variance approach is employed for the evaluation of parameter variability significance. The proposed method has been demonstrated firstly on a numerical beam and then a set of nominally identical steel plates tested in the laboratory. It is found that compared with the existing stochastic model updating methods, the proposed method presents similar accuracy while its primary merits consist in its simple implementation and cost efficiency in response computation and inverse optimization.

Bending of stochastic Kirchhoff plates on Winkler foundations via the Galerkin method and the Askey-Wiener scheme

In this paper, the method of Galerkin and the Askey-Wiener scheme are used to obtain approximate solutions to the stochastic displacement response of Kirchhoff plates with uncertain parameters. Theoretical and numerical results are presented. The Lax-Milgram lemma is used to express the conditions for existence and uniqueness of the solution. Uncertainties in plate and foundation stiffness are modeled by respecting these conditions, hence using Legendre polynomials indexed in uniform random variables. The space of approximate solutions is built using results of density between the space of continuous functions and Sobolev spaces. Approximate Galerkin solutions are compared with results of Monte Carlo simulation, in terms of first and second order moments and in terms of histograms of the displacement response. Numerical results for two example problems show very fast convergence to the exact solution, at excellent accuracies. The Askey-Wiener Galerkin scheme developed herein is able to reproduce the histogram of the displacement response. The scheme is shown to be a theoretically sound and efficient method for the solution of stochastic problems in engineering. (C) 2009 Elsevier Ltd. All rights reserved.

Numerical Simulation of Stochastic Di erential Equations

Stochastic differential equation (SDE) is a differential equation in which some of the terms and its solution are stochastic processes. SDEs play a central role in modeling physical systems like finance, Biology, Engineering, to mention some. In modeling process, the computation of the trajectories (sample paths) of solutions to SDEs is very important. However, the exact solution to a SDE is generally difficult to obtain due to non-differentiability character of realizations of the Brownian motion. There exist approximation methods of solutions of SDE. The solutions will be continuous stochastic processes that represent diffusive dynamics, a common modeling assumption for financial, Biology, physical, environmental systems. This Masters' thesis is an introduction and survey of numerical solution methods for stochastic differential equations. Standard numerical methods, local linearization methods and filtering methods are well described. We compute the root mean square errors for each method from which we propose a better numerical scheme. Stochastic differential equations can be formulated from a given ordinary differential equations. In this thesis, we describe two kind of formulations: parametric and non-parametric techniques. The formulation is based on epidemiological SEIR model. This methods have a tendency of increasing parameters in the constructed SDEs, hence, it requires more data. We compare the two techniques numerically.

GSHMC: an efficient method for molecular simulation

The hybrid Monte Carlo (HMC) method is a popular and rigorous method for sampling from a canonical ensemble. The HMC method is based on classical molecular dynamics simulations combined with a Metropolis acceptance criterion and a momentum resampling step. While the HMC method completely resamples the momentum after each Monte Carlo step, the generalized hybrid Monte Carlo (GHMC) method can be implemented with a partial momentum refreshment step. This property seems desirable for keeping some of the dynamic information throughout the sampling process similar to stochastic Langevin and Brownian dynamics simulations. It is, however, ultimate to the success of the GHMC method that the rejection rate in the molecular dynamics part is kept at a minimum. Otherwise an undesirable Zitterbewegung in the Monte Carlo samples is observed. In this paper, we describe a method to achieve very low rejection rates by using a modified energy, which is preserved to high-order along molecular dynamics trajectories. The modified energy is based on backward error results for symplectic time-stepping methods. The proposed generalized shadow hybrid Monte Carlo (GSHMC) method is applicable to NVT as well as NPT ensemble simulations.

Computer simulation of the stochastic transport equation

Trabalho apresentado no 37th Conference on Stochastic Processes and their Applications - July 28 - August 01, 2014 -Universidad de Buenos Aires

A stochastic approximation algorithm with Markov chain Monte-Carlo method for incomplete data estimation problems

We propose a general procedure for solving incomplete data estimation problems. The procedure can be used to find the maximum likelihood estimate or to solve estimating equations in difficult cases such as estimation with the censored or truncated regression model, the nonlinear structural measurement error model, and the random effects model. The procedure is based on the general principle of stochastic approximation and the Markov chain Monte-Carlo method. Applying the theory on adaptive algorithms, we derive conditions under which the proposed procedure converges. Simulation studies also indicate that the proposed procedure consistently converges to the maximum likelihood estimate for the structural measurement error logistic regression model.

The transform likelihood ratio method for rare event simulation with heavy tails

We present a novel method, called the transform likelihood ratio (TLR) method, for estimation of rare event probabilities with heavy-tailed distributions. Via a simple transformation ( change of variables) technique the TLR method reduces the original rare event probability estimation with heavy tail distributions to an equivalent one with light tail distributions. Once this transformation has been established we estimate the rare event probability via importance sampling, using the classical exponential change of measure or the standard likelihood ratio change of measure. In the latter case the importance sampling distribution is chosen from the same parametric family as the transformed distribution. We estimate the optimal parameter vector of the importance sampling distribution using the cross-entropy method. We prove the polynomial complexity of the TLR method for certain heavy-tailed models and demonstrate numerically its high efficiency for various heavy-tailed models previously thought to be intractable. We also show that the TLR method can be viewed as a universal tool in the sense that not only it provides a unified view for heavy-tailed simulation but also can be efficiently used in simulation with light-tailed distributions. We present extensive simulation results which support the efficiency of the TLR method.

Application of the cross-entropy method to the buffer allocation problem in a simulation-based environment

The buffer allocation problem (BAP) is a well-known difficult problem in the design of production lines. We present a stochastic algorithm for solving the BAP, based on the cross-entropy method, a new paradigm for stochastic optimization. The algorithm involves the following iterative steps: (a) the generation of buffer allocations according to a certain random mechanism, followed by (b) the modification of this mechanism on the basis of cross-entropy minimization. Through various numerical experiments we demonstrate the efficiency of the proposed algorithm and show that the method can quickly generate (near-)optimal buffer allocations for fairly large production lines.

Chaos-Galerkin solution of stochastic Timoshenko bending problems

This paper presents an accurate and efficient solution for the random transverse and angular displacement fields of uncertain Timoshenko beams. Approximate, numerical solutions are obtained using the Galerkin method and chaos polynomials. The Chaos-Galerkin scheme is constructed by respecting the theoretical conditions for existence and uniqueness of the solution. Numerical results show fast convergence to the exact solution, at excellent accuracies. The developed Chaos-Galerkin scheme accurately approximates the complete cumulative distribution function of the displacement responses. The Chaos-Galerkin scheme developed herein is a theoretically sound and efficient method for the solution of stochastic problems in engineering. (C) 2011 Elsevier Ltd. All rights reserved.

Galerkin Solution of Stochastic Beam Bending on Winkler Foundations

In this paper, the Askey-Wiener scheme and the Galerkin method are used to obtain approximate solutions to stochastic beam bending on Winkler foundation. The study addresses Euler-Bernoulli beams with uncertainty in the bending stiffness modulus and in the stiffness of the foundation. Uncertainties are represented by parameterized stochastic processes. The random behavior of beam response is modeled using the Askey-Wiener scheme. One contribution of the paper is a sketch of proof of existence and uniqueness of the solution to problems involving fourth order operators applied to random fields. From the approximate Galerkin solution, expected value and variance of beam displacement responses are derived, and compared with corresponding estimates obtained via Monte Carlo simulation. Results show very fast convergence and excellent accuracies in comparison to Monte Carlo simulation. The Askey-Wiener Galerkin scheme presented herein is shown to be a theoretically solid and numerically efficient method for the solution of stochastic problems in engineering.