Mathematical tool to size rural digesters

Os biodigestores têm sido objetos de grande destaque devido a atual crise de energia e conseqüente busca de fontes alternativas. Outro fator que coloca os biodigestores em evidência é o intenso processo de modernização da agropecuária, que além da grande demanda de energia, produz um volume de resíduos animais e de culturas, que ocasiona muitas vezes problemas de ordem sanitária. O objetivo deste trabalho é fornecer uma ferramenta matemática para determinação de parâmetros para projetos de construção de biodigestores rurais, levando-se em consideração o atendimento de necessidades energéticas, obedecendo os dimensionamentos dos sistemas, fatores de rendimento e garantindo a funcionalidade. Para isto, foram formulados modelos de otimização não lineares, de fácil resolução, para os três principais tipos de biodigestores rurais. Com a resolução destes modelos são determinados a altura e o diâmetro que levem a um volume mínimo para cada tipo, com isto reduz-se a quantidade necessária de materiais de alvenaria e consequentemente o custo do biodigestor é diminuído.

Influência dos parâmetros de processamento por RTM no volume de vazios em compósitos carbono/epóxi

Pós-graduação em Engenharia Mecânica - FEG

Otimização dinâmica de rotores com eixos em compósito

Pós-graduação em Engenharia Mecânica - FEG

Otimização do processo de soldagem por resistência elétrica em compósitos PEI/fibras contínuas para aplicações aeronáuticas

Pós-graduação em Engenharia Mecânica - FEG

Bacillus lehensis-an alkali-tolerant bacterium isolated from cassava starch wastewater: optimization of parameters for cyclodextrin glycosyltransferase production

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Antibiosis associated to Lactobacillus acidophilus growth .2. Optimization of some culture parameters in whey and soy milk medium

Whey supplemented with soy milk has been used as a low-cost alternative in the growth of Lactobacillus acidophilus for the production of antimicrobial compounds. Response Surface Metodology has been employed in order to study the effects of initial pH, incubation temperature and soy milk rate for supplementation in the production of antimicrobial substance. It has been observed that both tested microrganisms used (S. aureus and E. coli) were inhibited by antimicrobial substance produced by L. acidophilus. The results obtained with E. coli inhibition did not follow the employed statistical model. on the other hand, when the tested microorganism S. aureus was used, the best inhibition results have been obtained when L. acidophilus was incubated at 36.80 degrees C in whey with 5.6 initial pH and 31,90% (v/v) rate supplemented with soy milk. The analysed antimicrobial substances were nor acids neither hidrogen peroxid.

Parameters optimization of heat treatment for obtaining luminescent PZT powders

Low crystalline PZT powder samples were successfully synthesized using polymeric precursor method and slow decomposition steps. The polymeric resin precursor was thermal treated in a muffle type oven varying the temperature from 250 °C to 700 °C and the time from 3 to 24 hours in order to investigate the order/disorder mechanism toward the amorphous powders. Powder samples with low crystalline phases were obtained at lower temperatures and long time of thermal treatment, demonstrating a kinetic dependence for organic removal and a thermodynamic barrier for crystallization processes. Through XRD and FTIR spectroscopy characterizations the long time thermal treated samples showed to be composed of the solid solution of metal oxides in absent of organic matter, originating broad XRD peaks profiles and no carbonaceous bands in FTIR spectra. A Photoluminescence characterization showed that the peak emission is higher for disordered and homogeneous phases, which only can be reached through the long time of thermal treatment.

Study of the potentiometric response of the doped spinel Li1.05Al0.02Mn1.98O4 for the optimization of a selective lithium ion sensor

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

A novel approach based on recurrent neural networks applied to nonlinear systems optimization

This paper presents an efficient approach based on recurrent neural network for solving nonlinear optimization. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid subspace technique. These parameters guarantee the convergence of the network to the equilibrium points that represent an optimal feasible solution. The main advantage of the developed network is that it treats optimization and constraint terms in different stages with no interference with each other. Moreover, the proposed approach does not require specification of penalty and weighting parameters for its initialization. A study of the modified Hopfield model is also developed to analyze its stability and convergence. Simulation results are provided to demonstrate the performance of the proposed neural network. (c) 2005 Elsevier B.V. All rights reserved.

Stability and convergence analysis of a neural model applied in nonlinear systems optimization

A neural model for solving nonlinear optimization problems is presented in this paper. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points that represent an optimal feasible solution. The network is shown to be completely stable and globally convergent to the solutions of nonlinear optimization problems. A study of the modified Hopfield model is also developed to analyze its stability and convergence. Simulation results are presented to validate the developed methodology.

A novel approach for solving constrained nonlinear optimization problems using neurofuzzy systems

A neural network model for solving constrained nonlinear optimization problems with bounded variables is presented in this paper. More specifically, a modified Hopfield network is developed and its internal parameters are completed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points. The network is shown to be completely stable and globally convergent to the solutions of constrained nonlinear optimization problems. A fuzzy logic controller is incorporated in the network to minimize convergence time. Simulation results are presented to validate the proposed approach.

Design and analysis of an efficient neural network model for solving nonlinear optimization problems

This paper presents an efficient approach based on a recurrent neural network for solving constrained nonlinear optimization. More specifically, a modified Hopfield network is developed, and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points that represent an optimal feasible solution. The main advantage of the developed network is that it handles optimization and constraint terms in different stages with no interference from each other. Moreover, the proposed approach does not require specification for penalty and weighting parameters for its initialization. A study of the modified Hopfield model is also developed to analyse its stability and convergence. Simulation results are provided to demonstrate the performance of the proposed neural network.

A neural system to robust Nonlinear optimization subject to disjoint and constrained sets

The ability of neural networks to realize some complex nonlinear function makes them attractive for system identification. This paper describes a novel method using artificial neural networks to solve robust parameter estimation problems for nonlinear models with unknown-but-bounded errors and uncertainties. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the network convergence to the equilibrium points. A solution for the robust estimation problem with unknown-but-bounded error corresponds to an equilibrium point of the network. Simulation results are presented as an illustration of the proposed approach.

Fault section estimation in electric power systems using an optimization immune algorithm

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Differential-Evolution-Based Optimization of the Dynamic Response for Parallel Operation of Inverters With No Controller Interconnection

In this paper, the use of differential evolution ( DE), a global search technique inspired by evolutionary theory, to find the parameters that are required to achieve optimum dynamic response of parallel operation of inverters with no interconnection among the controllers is proposed. Basically, in order to reach such a goal, the system is modeled in a certain way that the slopes of P-omega and Q-V curves are the parameters to be tuned. Such parameters, when properly tuned, result in system's eigenvalues located in positions that assure the system's stability and oscillation-free dynamic response with minimum settling time. This paper describes the modeling approach and provides an overview of the motivation for the optimization and a description of the DE technique. Simulation and experimental results are also presented, and they show the viability of the proposed method.