Padrão de pastejo de fêmeas de corte em campo nativo no Sul do Brasil.

JAI

Determination of Total Polyphenol Content in Food with the Flow-Injection

U ovom radu predložena je modificirana automatizirana metoda ubrizgavanja u protok za određivanje sadržaja ukupnih polifenola u namirnicama bazirana na Folin-Ciocalteauovoj reakciji u 0,5 mol L-1 NaOH. Metoda omogućuje automatiziranu analizu različitih uzoraka brzinom protoka 55 uzoraka na sat uz upotrebu galne kiseline kao standarda. Primjenom predložene metode na konkretne uzorke (bijelo i crno vino, zeleni, indijski te čaj od lipe, metvice i kamilice i bistri voćni sokovi od crnog ribiza i višnje), određen je njihov “indeks ukupnih polifenola” s većom repetibilnošću za razliku od ranije objavljenih metoda, manje ovisno o razrjeđenju uzorka. U odnosu na “batch” metodu, ova je metoda visoko tolerantna prema najčešćim interferentima (SO2, reducirajući šećeri i askorbinska kiselina). Rezultati dobiveni predloženom metodom pokazali su relativno slaganje s onima dobivenim referentnom Folin-Ciocalteauovom metodom.This paper describes an optimised flow-injection method for the determination of total polyphenol in food based on the Folin-Ciocalteau reaction in 0.5 mol L-1 NaOH. The method allows different types of samples to be analysed automatically at a rate of 55 samples per hour by using gallic acid as standard. By applying the proposed method to real samples (white and red wines, green, Indian, lime-tree, mentha and chamomile teas, and blackberry and cherry juices), their total polyphenol indices were determined with a higher reproducibility than obtained by earlier methods, whatever the dilution used. This method is highly tolerant towards the most common interferences (SO2, reducing sugars, and ascorbic acid) associated with the batch method. The results obtained by the proposed method relatively agree with those obtained using the referent Folin-Ciocalteau method

Sensitivity analysis for transient stability studies

The paper presents a method for security control of electric power systems effected by generation reallocation, determined by sensitivity analysis and optimisation. The model is developed considering the dynamic aspects of the network (transient stability). Security control methodology is developed using sensitivity analysis of the security margin in relation to the mechanical power of synchronous machines in the system. The power reallocated to each machine is determined by means of linear programming. To illustrate the proposed methodology, an example is presented which considers a multimachine system composed of 10 synchronous machines, 45 buses, and 72 transmission lines, based on the configuration of a southern Brazilian system

Electrical load forecasting formulation by a fast neural network

The objective of this work is to develop a methodology for electric load forecasting based on a neural network. Here, backpropagation algorithm is used with an adaptive process that based on fuzzy logic and using a decaying exponential function to avoid instability in the convergence process. This methodology results in fast training, when compared to the conventional formulation of backpropagation algorithm. The results are presented using data from a Brazilian Electric Company, and shows a very good performance for the proposal objective

Uma metodologia para o controle de seguranca dinamica de sistemas de energia eletrica

This work presents an algorithm for the security control of electric power systems using control actions like generation reallocation, determined by sensitivity analysis (linearized model) and optimization by neural networks. The model is developed taking into account the dynamic network aspects. The preventive control methodology is developed by means of sensitivity analysis of the security margin related with the mechanical power of the system synchronous machines. The reallocation power in each machine is determined using neural networks. The neural network used in this work is of Hopfield type. These networks are dedicated electric circuits which simulate the constraint set and the objective function of an optimization problem. The advantage of using these networks is the higher speed in getting the solutions when compared to conventional optimization algorithms due to the great convergence rate of the process and the facility of the method parallelization. Then, the objectives are: formulate and investigate these networks implementations in determining. The generation reallocation in digital computers. Aiming to illustrate the proposed methodology an application considering a multi-machine system is presented

Electric load forecasting using a fuzzy ART&ARTMAP neural network

This work presents a neural network based on the ART architecture ( adaptive resonance theory), named fuzzy ART& ARTMAP neural network, applied to the electric load-forecasting problem. The neural networks based on the ARTarchitecture have two fundamental characteristics that are extremely important for the network performance ( stability and plasticity), which allow the implementation of continuous training. The fuzzy ART& ARTMAP neural network aims to reduce the imprecision of the forecasting results by a mechanism that separate the analog and binary data, processing them separately. Therefore, this represents a reduction on the processing time and improved quality of the results, when compared to the Back-Propagation neural network, and better to the classical forecasting techniques (ARIMA of Box and Jenkins methods). Finished the training, the fuzzy ART& ARTMAP neural network is capable to forecast electrical loads 24 h in advance. To validate the methodology, data from a Brazilian electric company is used. (C) 2004 Elsevier B.V. All rights reserved