A neural approach for determination of global energetic efficiency indicator in groundwater wells

In most of the cases, the systems of water distribution from groundwater wells use electrical submersible pumps. All electrical energy is applied to the pumps; however, other components (pipes, valves, etc.) of these systems are also responsible by the higher or lower consumption of electric energy. The supervisors and operators of the systems should thus have knowledge of the global energetic behavior of the process in order to administrate it properly. This work suggests a 'Global Energetic Efficiency Indicator' for groundwater wells by using mathematical equations and neural networks. Simulation results will be presented in order to demonstrate the validity of the proposed approach.

Weather-based prediction of anthracnose severity using artificial neural network models

Data were collected and analysed from seven field sites in Australia, Brazil and Colombia on weather conditions and the severity of anthracnose disease of the tropical pasture legume Stylosanthes scabra caused by Colletotrichum gloeosporioides. Disease severity and weather data were analysed using artificial neural network (ANN) models developed using data from some or all field sites in Australia and/or South America to predict severity at other sites. Three series of models were developed using different weather summaries. of these, ANN models with weather for the day of disease assessment and the previous 24 h period had the highest prediction success, and models trained on data from all sites within one continent correctly predicted disease severity in the other continent on more than 75% of days; the overall prediction error was 21.9% for the Australian and 22.1% for the South American model. of the six cross-continent ANN models trained on pooled data for five sites from two continents to predict severity for the remaining sixth site, the model developed without data from Planaltina in Brazil was the most accurate, with >85% prediction success, and the model without Carimagua in Colombia was the least accurate, with only 54% success. In common with multiple regression models, moisture-related variables such as rain, leaf surface wetness and variables that influence moisture availability such as radiation and wind on the day of disease severity assessment or the day before assessment were the most important weather variables in all ANN models. A set of weights from the ANN models was used to calculate the overall risk of anthracnose for the various sites. Sites with high and low anthracnose risk are present in both continents, and weather conditions at centres of diversity in Brazil and Colombia do not appear to be more conducive than conditions in Australia to serious anthracnose development.

Homogeneous photodegradation of CI Reactive Blue 4 using a photo-Fenton process under artificial and solar irradiation

The oxidation of C.I. Reactive Blue 4 (RB4) by photo-Fenton process mediated by lerrioxalate was investigated under artificial and solar irradiation. The RB4 degradation in acidic medium (pH 2.5) was evaluated by the decrease in Total Organic Carbon (TOC) content and color, measured by the decrease in chromophore absorption band (600 nm). The influence of ferrioxalate and H2O2 concentrations on the dye degradation was studied and best results were obtained using 1.0 mM ferrioxalate and 10 nM of hydrogen peroxide. Under these experimental conditions, 80% of TOC and 100% of color removal were obtained for a 0.1 mM RB4 dye in 35 min of solar irradiation. (c) 2006 Elsevier Ltd. All rights reserved.

Prediction of occurrences of diverse chemical classes in the Asteraceae through artificial neural networks

The training and the application of a neural network system for the prediction of occurrences of secondary metabolites belonging to diverse chemical classes in the Asteraceae is described. From a database containing about 604 genera and 28,000 occurrences of secondary metabolites in the plant family, information was collected encompassing nine chemical classes and their respective occurrences for training of a multi-layer net using the back-propagation algorithm. The net supplied as output the presence or absence of the chemical classes as well as the number of compounds isolated from each taxon. The results provided by the net from the presence or absence of a chemical class showed a 89% hit rate; by excluding triterpenes from the analysis, only 5% of the genera studied exhibited errors greater than 10%. Copyright (C) 2004 John Wiley Sons, Ltd.

Using artificial neural networks for identification of electrical losses in transformers during the manufacturing phase

The paper describes a novel neural model to estimate electrical losses in transformer during the manufacturing phase. The network acts as an identifier of structural features on electrical loss process, so that output parameters can be estimated and generalized from an input parameter set. The model was trained and assessed through experimental data taking into account core losses, copper losses, resistance, current and temperature. The results obtained in the simulations have shown that the developed technique can be used as an alternative tool to make the analysis of electrical losses on distribution transformer more appropriate regarding to manufacturing process. Thus, this research has led to an improvement on the rational use of energy.

Potentiometric determination of saccharin in commercial artificial sweeteners using a silver electrode

A simple, precise, rapid and low-cost potentiometric method for saccharin determination in commercial artificial sweeteners is proposed. Saccharin present in several samples of artificial sweeteners is potentiometrically titrated with silver nitrate solution using a silver wire as the indicator electrode, coupled to a titroprocessor. The best pH range was from 3.0 to 3.5 and the detection limit of sodium saccharin was 2.5 mg/ml. Substances normally found along with saccharin in several commercial artificial sweeteners such as maltodextrin, glucose, sucrose, fructose, aspartame, cyclamate, caffeine, sorbitol, lactose, nitrate, methyl- and n-propyl-p-hydroxybenzoate, benzoic, citric and ascorbic acids do not interfere even in significant amounts (e.g. 20 excess relative to saccharin). Chloride ion interferes when present in concentrations larger than 10 mg l(-1); this interference is eliminated with previous extraction of the sweetener from the aqueous medium with ethyl acetate. The results obtained by applying the proposed method compared very favorably with those given by the HPLC method recommended by the FDA. (C) 2003 Elsevier Ltd. All rights reserved.

Stability analysis of the attitude of artificial satellites subject to gravity gradient torque

Using a canonical formulation, the stability of the rotational motion of artificial satellites is analyzed considering perturbations due to the gravity gradient torque. Here Andoyer's variables are used to describe the rotational motion. One of the approaches that allow the analysis of the stability of Hamiltonian systems needs the reduction of the Hamiltonian to a normal form. Firstly equilibrium points are found. Using generalized coordinates, the Hamiltonian is expanded in the neighborhood of the linearly stable equilibrium points. In a next step a canonical linear transformation is used to diagonalize the matrix associated to the linear part of the system. The quadratic part of the Hamiltonian is normalized. Based in a Lie-Hori algorithm a semi-analytic process for normalization is applied and the Hamiltonian is normalized up to the fourth order. Once the Hamiltonian is normalized up to order four, the analysis of stability of the equilibrium point is performed using the theorem of Kovalev and Savichenko. This semi-analytical approach was applied considering some data sets of hypothetical satellites. For the considered satellites it was observed few cases of stable motion. This work contributes for space missions where the maintenance of spacecraft attitude stability is required.