Análise estequiométrica da dinâmica não-linear de redes de reações químicas

Dissertação (mestrado)—Universidade de Brasília, Instituto de Química, Programa de Pós-Graduação em Química, 2016.

CLIMATE CHANGE ADAPTATION IN VULNERABLE CROP AND LIVESTOCK PRODUCTION SYSTEMS IN MGETA, TANZANIA

Increased occurrence of drought and dry spells during the growing season have resulted in increased interest in protection of tropical water catchment areas. In Mgeta, a water catchment area in the Uluguru Mountains in Tanzania, water used for vegetable and fruit production is provided through canals from the Uluguru South Forest Reserve. The clearing of forest land for cultivation in the steep slopes in the area is causing severe land degradation, which is threatening the water catchment area, livelihoods, and food security of the local communities, as well as the major population centers in the lowlands. In this paper, the economic performance of a traditional cropping-livestock system with East African (EA)-goats and pigs and extensive vegetable production is compared with a more sustainable and environmentally friendly crop-dairy goat production system. A linear programming (LP) crop-livestock model, maximizing farm income considering the environmental constraints in the area was applied for studying the economic performance of dairy goats in the production system. The model was worked out for the rainy and dry seasons and the analysis was conducted for a basic scenario representing the current situation, based on the variability in the 30 years period from 1982-2012, and in a scenario of both lower crop yields and increased crop variability due to climate change. Data obtained from a sample of 60 farmers that were interviewed using a questionnaire was used to develop and parameterize the model. The study found that in the steep slopes of the area, a crop-dairy goat system with extensive use of grass and multipurpose trees (MPTs) would do better than the traditional vegetable gardening with the EA goat production system. The crop-dairy goat system was superior both in the basic and in a climate change scenario since the yield variation of the grass and MPTs system was less affected compared to vegetable crops due to more tree cover and the use of perennial grasses. However, the goat milk production in the area was constrained by inadequate feeding and lack of an appropriate breeding program. Hence, farmers should enhance goat milk production by supplementing with more concentrate feed and by implementing goat-breeding principles. Moreover, policy measures to promote such a development are briefly discussed.

Perpectiva linear paramétrica

Tese de Doutoramento em Arquitetura, com a especialização em Comunicação Visual, apresentada na Faculdade de Arquitetura da Universidade de Lisboa, para obtenção do grau de Doutor.

Real-time load-side control of electric power systems

Two trends are emerging from modern electric power systems: the growth of renewable (e.g., solar and wind) generation, and the integration of information technologies and advanced power electronics. The former introduces large, rapid, and random fluctuations in power supply, demand, frequency, and voltage, which become a major challenge for real-time operation of power systems. The latter creates a tremendous number of controllable intelligent endpoints such as smart buildings and appliances, electric vehicles, energy storage devices, and power electronic devices that can sense, compute, communicate, and actuate. Most of these endpoints are distributed on the load side of power systems, in contrast to traditional control resources such as centralized bulk generators. This thesis focuses on controlling power systems in real time, using these load side resources. Specifically, it studies two problems.

(1) Distributed load-side frequency control: We establish a mathematical framework to design distributed frequency control algorithms for flexible electric loads. In this framework, we formulate a category of optimization problems, called optimal load control (OLC), to incorporate the goals of frequency control, such as balancing power supply and demand, restoring frequency to its nominal value, restoring inter-area power flows, etc., in a way that minimizes total disutility for the loads to participate in frequency control by deviating from their nominal power usage. By exploiting distributed algorithms to solve OLC and analyzing convergence of these algorithms, we design distributed load-side controllers and prove stability of closed-loop power systems governed by these controllers. This general framework is adapted and applied to different types of power systems described by different models, or to achieve different levels of control goals under different operation scenarios. We first consider a dynamically coherent power system which can be equivalently modeled with a single synchronous machine. We then extend our framework to a multi-machine power network, where we consider primary and secondary frequency controls, linear and nonlinear power flow models, and the interactions between generator dynamics and load control.

(2) Two-timescale voltage control: The voltage of a power distribution system must be maintained closely around its nominal value in real time, even in the presence of highly volatile power supply or demand. For this purpose, we jointly control two types of reactive power sources: a capacitor operating at a slow timescale, and a power electronic device, such as a smart inverter or a D-STATCOM, operating at a fast timescale. Their control actions are solved from optimal power flow problems at two timescales. Specifically, the slow-timescale problem is a chance-constrained optimization, which minimizes power loss and regulates the voltage at the current time instant while limiting the probability of future voltage violations due to stochastic changes in power supply or demand. This control framework forms the basis of an optimal sizing problem, which determines the installation capacities of the control devices by minimizing the sum of power loss and capital cost. We develop computationally efficient heuristics to solve the optimal sizing problem and implement real-time control. Numerical experiments show that the proposed sizing and control schemes significantly improve the reliability of voltage control with a moderate increase in cost.

Does Landscape Context Affect Habitat Value? The Importance of Seascape Ecology in Back-reef Systems

Seascape ecology provides a useful framework from which to understand the processes governing spatial variability in ecological patterns. Seascape context, or the composition and pattern of habitat surrounding a focal patch, has the potential to impact resource availability, predator-prey interactions, and connectivity with other habitats. For my dissertation research, I combined a variety of approaches to examine how habitat quality for fishes is influenced by a diverse range of seascape factors in sub-tropical, back-reef ecosystems. In the first part of my dissertation, I examined how seascape context can affect reef fish communities on an experimental array of artificial reefs created in various seascape contexts in Abaco, Bahamas. I found that the amount of seagrass at large spatial scales was an important predictor of community assembly on these reefs. Additionally, seascape context had differing effects on various aspects of habitat quality for the most common reef species, White grunt Haemulon plumierii. The amount of seagrass at large spatial scales had positive effects on fish abundance and secondary production, but not on metrics of condition and growth. The second part of my dissertation focused on how foraging conditions for fish varied across a linear seascape gradient in the Loxahatchee River estuary in Florida, USA. Gray snapper, Lutjanus griseus, traded food quality for quantity along this estuarine gradient, maintaining similar growth rates and condition among sites. Additional work focused on identifying major energy flow pathways to two consumers in oyster-reef food webs in the Loxahatchee. Algal and microphytobenthos resource pools supported most of the production to these consumers, and body size for one of the consumers mediated food web linkages with surrounding mangrove habitats. All of these studies examined a different facet of the importance of seascape context in governing ecological processes occurring in focal habitats and underscore the role of connectivity among habitats in back-reef systems. The results suggest that management approaches consider the surrounding seascape when prioritizing areas for conservation or attempting to understand the impacts of seascape change on focal habitat patches. For this reason, spatially-based management approaches are recommended to most effectively manage back-reef systems.

Multivariate orthogonal polynomials and integrable systems

Multivariate orthogonal polynomials in D real dimensions are considered from the perspective of the Cholesky factorization of a moment matrix. The approach allows for the construction of corresponding multivariate orthogonal polynomials, associated second kind functions, Jacobi type matrices and associated three term relations and also Christoffel-Darboux formulae. The multivariate orthogonal polynomials, their second kind functions and the corresponding Christoffel-Darboux kernels are shown to be quasi-determinants as well as Schur complements of bordered truncations of the moment matrix; quasi-tau functions are introduced. It is proven that the second kind functions are multivariate Cauchy transforms of the multivariate orthogonal polynomials. Discrete and continuous deformations of the measure lead to Toda type integrable hierarchy, being the corresponding flows described through Lax and Zakharov-Shabat equations; bilinear equations are found. Varying size matrix nonlinear partial difference and differential equations of the 2D Toda lattice type are shown to be solved by matrix coefficients of the multivariate orthogonal polynomials. The discrete flows, which are shown to be connected with a Gauss-Borel factorization of the Jacobi type matrices and its quasi-determinants, lead to expressions for the multivariate orthogonal polynomials and their second kind functions in terms of shifted quasi-tau matrices, which generalize to the multidimensional realm, those that relate the Baker and adjoint Baker functions to ratios of Miwa shifted tau-functions in the 1D scenario. In this context, the multivariate extension of the elementary Darboux transformation is given in terms of quasi-determinants of matrices built up by the evaluation, at a poised set of nodes lying in an appropriate hyperplane in R^D, of the multivariate orthogonal polynomials. The multivariate Christoffel formula for the iteration of m elementary Darboux transformations is given as a quasi-determinant. It is shown, using congruences in the space of semi-infinite matrices, that the discrete and continuous flows are intimately connected and determine nonlinear partial difference-differential equations that involve only one site in the integrable lattice behaving as a Kadomstev-Petviashvili type system. Finally, a brief discussion of measures with a particular linear isometry invariance and some of its consequences for the corresponding multivariate polynomials is given. In particular, it is shown that the Toda times that preserve the invariance condition lay in a secant variety of the Veronese variety of the fixed point set of the linear isometry.

New Rigorous Decomposition Methods for Mixed-integer Linear and Nonlinear Programming

Process systems design, operation and synthesis problems under uncertainty can readily be formulated as two-stage stochastic mixed-integer linear and nonlinear (nonconvex) programming (MILP and MINLP) problems. These problems, with a scenario based formulation, lead to large-scale MILPs/MINLPs that are well structured. The first part of the thesis proposes a new finitely convergent cross decomposition method (CD), where Benders decomposition (BD) and Dantzig-Wolfe decomposition (DWD) are combined in a unified framework to improve the solution of scenario based two-stage stochastic MILPs. This method alternates between DWD iterations and BD iterations, where DWD restricted master problems and BD primal problems yield a sequence of upper bounds, and BD relaxed master problems yield a sequence of lower bounds. A variant of CD, which includes multiple columns per iteration of DW restricted master problem and multiple cuts per iteration of BD relaxed master problem, called multicolumn-multicut CD is then developed to improve solution time. Finally, an extended cross decomposition method (ECD) for solving two-stage stochastic programs with risk constraints is proposed. In this approach, a CD approach at the first level and DWD at a second level is used to solve the original problem to optimality. ECD has a computational advantage over a bilevel decomposition strategy or solving the monolith problem using an MILP solver. The second part of the thesis develops a joint decomposition approach combining Lagrangian decomposition (LD) and generalized Benders decomposition (GBD), to efficiently solve stochastic mixed-integer nonlinear nonconvex programming problems to global optimality, without the need for explicit branch and bound search. In this approach, LD subproblems and GBD subproblems are systematically solved in a single framework. The relaxed master problem obtained from the reformulation of the original problem, is solved only when necessary. A convexification of the relaxed master problem and a domain reduction procedure are integrated into the decomposition framework to improve solution efficiency. Using case studies taken from renewable resource and fossil-fuel based application in process systems engineering, it can be seen that these novel decomposition approaches have significant benefit over classical decomposition methods and state-of-the-art MILP/MINLP global optimization solvers.

Water sorption-induced crystallization, structural relaxations and strength analysis of relaxation times in amorphous lactose/whey protein systems

Water sorption-induced crystallization, α-relaxations and relaxation times of freeze-dried lactose/whey protein isolate (WPI) systems were studied using dynamic dewpoint isotherms (DDI) method and dielectric analysis (DEA), respectively. The fractional water sorption behavior of lactose/WPI mixtures shown at aw ≤ 0.44 and the critical aw for water sorption-related crystallization (aw(cr)) of lactose were strongly affected by protein content based on DDI data. DEA results showed that the α-relaxation temperatures of amorphous lactose at various relaxation times were affected by the presence of water and WPI. The α-relaxation-derived strength parameter (S) of amorphous lactose decreased with aw up to 0.44 aw but the presence of WPI increased S. The linear relationship for aw(cr) and S for lactose/WPI mixtures was also established with R2 > 0.98. Therefore, DDI offers another structural investigation of water sorption-related crystallization as governed by aw(cr), and S may be used to describe real time effects of structural relaxations in noncrystalline multicomponent solids.

Symbolic dynamics generated by a hybrid chaotic systems

We consider piecewise defined differential dynamical systems which can be analysed through symbolic dynamics and transition matrices. We have a continuous regime, where the time flow is characterized by an ordinary differential equation (ODE) which has explicit solutions, and the singular regime, where the time flow is characterized by an appropriate transformation. The symbolic codification is given through the association of a symbol for each distinct regular system and singular system. The transition matrices are then determined as linear approximations to the symbolic dynamics. We analyse the dependence on initial conditions, parameter variation and the occurrence of global strange attractors.

Above ground biomass functions with vegetation indices for multiple use systems of two evergreen oaks

Remote sensing is a promising approach for above ground biomass estimation, as forest parameters can be obtained indirectly. The analysis in space and time is quite straight forward due to the flexibility of the method to determine forest crown parameters with remote sensing. It can be used to evaluate and monitoring for example the development of a forest area in time and the impact of disturbances, such as silvicultural practices or deforestation. The vegetation indices, which condense data in a quantitative numeric manner, have been used to estimate several forest parameters, such as the volume, basal area and above ground biomass. The objective of this study was the development of allometric functions to estimate above ground biomass using vegetation indices as independent variables. The vegetation indices used were the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Simple Ratio (SR) and Soil-Adjusted Vegetation Index (SAVI). QuickBird satellite data, with 0.70 m of spatial resolution, was orthorectified, geometrically and atmospheric corrected, and the digital number were converted to top of atmosphere reflectance (ToA). Forest inventory data and published allometric functions at tree level were used to estimate above ground biomass per plot. Linear functions were fitted for the monospecies and multispecies stands of two evergreen oaks (Quercus suber and Quercus rotundifolia) in multiple use systems, montados. The allometric above ground biomass functions were fitted considering the mean and the median of each vegetation index per grid as independent variable. Species composition as a dummy variable was also considered as an independent variable. The linear functions with better performance are those with mean NDVI or mean SR as independent variable. Noteworthy is that the two better functions for monospecies cork oak stands have median NDVI or median SR as independent variable. When species composition dummy variables are included in the function (with stepwise regression) the best model has median NDVI as independent variable. The vegetation indices with the worse model performance were EVI and SAVI.

Bidding and Optimization Strategies for Wind-PV Systems in Electricity Markets

The variability in non-dispatchable power generation raises important challenges to the integration of renewable energy sources into the electricity power grid. This paper provides the coordinated trading of wind and photovoltaic energy to mitigate risks due to the wind and solar power variability, electricity prices, and financial penalties arising out the generation shortfall and surplus. The problem of wind-photovoltaic coordinated trading is formulated as a linear programming problem. The goal is to obtain the optimal bidding strategy that maximizes the total profit. The wind-photovoltaic coordinated operation is modeled and compared with the uncoordinated operation. A comparison of the models and relevant conclusions are drawn from an illustrative case study of the Iberian day-ahead electricity market.

Bidding and Optimization Strategies for Wind-PV Systems in Electricity Markets Assisted by CPS

The variability in non-dispatchable power generation raises important challenges to the integration of renewable energy sources into the electricity power grid. This paper provides the coordinated trading of wind and photovoltaic energy assisted by a cyber-physical system for supporting management decisions to mitigate risks due to the wind and solar power variability, electricity prices, and financial penalties arising out the generation shortfall and surplus. The problem of wind-photovoltaic coordinated trading is formulated as a stochastic linear programming problem. The goal is to obtain the optimal bidding strategy that maximizes the total profit. The wind-photovoltaic coordinated operation is modelled and compared with the uncoordinated operation. A comparison of the models and relevant conclusions are drawn from an illustrative case study of the Iberian day-ahead electricity market.

PRACTICAL RESOLUTION OF DIFFERENTIAL SYSTEMS

3. PRACTICAL RESOLUTION OF DIFFERENTIAL SYSTEMS by Marilia Pires, University of Évora, Portugal This practice presents the main features of a free software to solve mathematical equations derived from concrete problems: i.- Presentation of Scilab (or python) ii.- Basics (number, characters, function) iii.- Graphics iv.- Linear and nonlinear systems v.- Differential equations

Análise não linear de séries temporais e aplicações à Psicologia

Nesta dissertação estudámos as séries temporais que representam a complexa dinâmica do comportamento. Demos especial atenção às técnicas de dinâmica não linear. As técnicas fornecem-nos uma quantidade de índices quantitativos que servem para descrever as propriedades dinâmicas do sistema. Estes índices têm sido intensivamente usados nos últimos anos em aplicações práticas em Psicologia. Estudámos alguns conceitos básicos de dinâmica não linear, as características dos sistemas caóticos e algumas grandezas que caracterizam os sistemas dinâmicos, que incluem a dimensão fractal, que indica a complexidade de informação contida na série temporal, os expoentes de Lyapunov, que indicam a taxa com que pontos arbitrariamente próximos no espaço de fases da representação do espaço dinâmico, divergem ao longo do tempo, ou a entropia aproximada, que mede o grau de imprevisibilidade de uma série temporal. Esta informação pode então ser usada para compreender, e possivelmente prever, o comportamento. ABSTRACT: ln this thesis we studied the time series that represent the complex dynamic behavior. We focused on techniques of nonlinear dynamics. The techniques provide us a number of quantitative indices used to describe the dynamic properties of the system. These indices have been extensively used in recent years in practical applications in psychology. We studied some basic concepts of nonlinear dynamics, the characteristics of chaotic systems and some quantities that characterize the dynamic systems, including fractal dimension, indicating the complexity of information in the series, the Lyapunov exponents, which indicate the rate at that arbitrarily dose points in phase space representation of a dynamic, vary over time, or the approximate entropy, which measures the degree of unpredictability of a series. This information can then be used to understand and possibly predict the behavior.

Circadian Phase Control - Genetic Algorithm and Non-linear Control Approach

This paper deals with the phase control for Neurospora circadian rhythm. The nonlinear control, given by tuning the parameters (considered as controlled variables) in Neurospora dynamical model, allows the circadian rhythms tracking a reference one. When there are many parameters (e.g. 3 parameters in this paper) and their values are unknown, the adaptive control law reveals its weakness since the parameters converging and control objective must be guaranteed at the same time. We show that this problem can be solved using the genetic algorithm for parameters estimation. Once the unknown parameters are known, the phase control is performed by chaos synchronization technique.