Nitrogen content identification in crop plants using spectral reflectance and artificial neural networks

The accurate identification of the nitrogen content in crop plants is extremely important since it involves economic aspects and environmental impacts. Several experimental tests have been carried out to obtain characteristics and parameters associated with the health of plants and its growing. The nitrogen content identification involves a lot of nonlinear parametes and complexes mathematical models. This paper describes a novel approach for identification of nitrogen content thought spectral reflectance of plant leaves using artificial neural networks. The network acts as identifier of relationships among pH of soil, fertilizer treatment, spectral reflectance and nitrogen content in the plants. So, nitrogen content can be estimated and generalized from an input parameter set. This approach can be form the basis for development of an accurate real time nitrogen applicator.

An efficient neural approach to economic load dispatch in power systems

A neural approach to solve the problem defined by the economic load dispatch in power systems is presented in this paper, Systems based on artificial neural networks have high computational rates due to the use of a massive number of simple processing elements and the high degree of connectivity between these elements the ability of neural networks to realize some complex nonlinear function makes them attractive for system optimization the neural networks applyed in economic load dispatch reported in literature sometimes fail to converge towards feasible equilibrium points the internal parameters of the modified Hopfield network developed here are computed using the valid-subspace technique These parameters guarantee the network convergence to feasible quilibrium points, A solution for the economic load dispatch problem corresponds to an equilibrium point of the network. Simulation results and comparative analysis in relation to other neural approaches are presented to illustrate efficiency of the proposed approach.

Neural Network-Based Approach for Identification of the Harmonic Content of a Nonlinear Load in a Single-Phase System

In this paper an alternative method based on artificial neural networks is presented to determine harmonic components in the load current of a single-phase electric power system with nonlinear loads, whose parameters can vary so much in reason of the loads characteristic behaviors as because of the human intervention. The first six components in the load current are determined using the information contained in the time-varying waveforms. The effectiveness of this method is verified by using it in a single-phase active power filter with selective compensation of the current drained by an AC controller. The proposed method is compared with the fast Fourier transform.

Discoloration process modeling by neural network

The photo-oxidation of acid orange 52 dye was performed in the presence of H2O2, utilizing UV light, aiming the discoloration process modeling and the process variable influence characterization. The discoloration process was modeled by the use of feedforward neural network. Each sample was characterized by five independent variables (dye concentration, pH, hydrogen peroxide volume, temperature and time of operation) and a dependent variable (absorbance). The neural model has also provided, through Garson Partition coefficients and the Pertubation method, the independent variable influence order determination. The results indicated that the time of operation was the predominant variable and reaction mean temperature was the lesser influent variable. The neural model obtained presented coefficients of correlation on the order 0.98, for sets of trainability, validation and testing, indicating the power of prediction of the model and its character of generalization. (c) 2007 Elsevier B.V. All rights reserved.

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.

Neural network based on adaptive resonance theory with continuous training for multi-configuration transient stability analysis of electric power systems

This work presents a methodology to analyze electric power systems transient stability for first swing using a neural network based on adaptive resonance theory (ART) architecture, called Euclidean ARTMAP neural network. The ART architectures present plasticity and stability characteristics, which are very important for the training and to execute the analysis in a fast way. The Euclidean ARTMAP version provides more accurate and faster solutions, when compared to the fuzzy ARTMAP configuration. Three steps are necessary for the network working, training, analysis and continuous training. The training step requires much effort (processing) while the analysis is effectuated almost without computational effort. The proposed network allows approaching several topologies of the electric system at the same time; therefore it is an alternative for real time transient stability of electric power systems. To illustrate the proposed neural network an application is presented for a multi-machine electric power systems composed of 10 synchronous machines, 45 buses and 73 transmission lines. (C) 2010 Elsevier B.V. All rights reserved.

Multinodal Load Forecasting in Power Electric Systems using a Neural Network with Radial Basis Function

In this paper we present the results of the use of a methodology for multinodal load forecasting through an artificial neural network-type Multilayer Perceptron, making use of radial basis functions as activation function and the Backpropagation algorithm, as an algorithm to train the network. This methodology allows you to make the prediction at various points in power system, considering different types of consumers (residential, commercial, industrial) of the electric grid, is applied to the problem short-term electric load forecasting (24 hours ahead). We use a database (Centralised Dataset - CDS) provided by the Electricity Commission de New Zealand to this work.

Structural integrity identification based on smart materials and neural networks

This paper presents a non-model based technique to detect, locate, and characterize structural damage by combining the impedance-based structural health monitoring technique with an artificial neural network. The impedance-based structural health monitoring technique, which utilizes the electromechanical coupling property of piezoelectric materials, has shown engineering feasibility in a variety of practical field applications. Relying on high frequency structural excitations (typically>30 kHz), this technique is very sensitive to minor structural changes in the near field of the piezoelectric sensors. In order to quantitatively assess the state of structures, two sets of artificial neural networks, which utilize measured electrical impedance signals for input patterns, were developed. By employing high frequency ranges and by incorporating neural network features, this technique is able to detect the damage in its early stage and to estimate the nature of damage without prior knowledge of the model of structures. The paper concludes with an experimental example, an investigation on a massive quarter scale model of a steel bridge section, in order to verify the performance of this proposed methodology.

Non-destructive evaluation tool for monitoring and detection of structural damage by using neural network.

This work studies the capability of generalization of Neural Network using vibration based measurement data aiming at operating condition and health monitoring of mechanical systems. The procedure uses the backpropagation algorithm to classify the input patters of a system with different stiffness ratios. It has been investigated a large set of input data, containing various stiffness ratios as well as a reduced set containing only the extreme ones in order to study generalizing capability of the network. This allows to definition of Neural Networks capable to use a reduced set of data during the training phase. Once it is successfully trained, it could identify intermediate failure condition. Several conditions and intensities of damages have been studied by using numerical data. The Neural Network demonstrated a good capacity of generalization for all case. Finally, the proposal was tested with experimental data.

Síntese e caracterização de látices acrílicos reticulados com (1,6-diacrilato propoxilato hexanodiol) via polimerização em emulsão

Latices based on acrylic acid and ethyl methacrylate, crosslinked with 1,6‐propoxylate‐hexanodiol diacrylate were synthesized via emulsion polymerization with different monomeric compositions. The resultant latices were thickened with different NaOH/(acrylic acid) molar ratios and were characterized by titrimetry, zeta potential measurements, turbidimetry, and capillary viscometry. Intrinsic viscosity was determined for an uncrosslinked copolymer, using toluene as solvent. All the latices were coagulated with NaCl and washed with water at 60°C analyzed by FTIR spectrophotometry, in order to characterize functional groups from the copolymer and crosslinking agent.

Alquilação redutiva da quitosana a partir do glutaraldeído e 3-amino-1-pr

Chitosan derivatives were prepared by reductive alkylation using glutaraldehyde and 3-amino-1-propanol. The reducing agent used was the sodium borohydride. Tests of solubility, stability and viscosity were performed in order to evaluate these parameters effects in the reaction conditions (molar ratio of the reactants and presence of nitrogen in the reaction system). The molecular structure of commercial chitosan was determined by infrared (IR) and hydrogen nuclear magnetic resonance spectroscopy (1H NMR). The intrinsic viscosity and average molecular weight of the chitosan were determined by viscosimetry in 0.3 M acetic acid aqueous solution 0.2 M sodium acetate at 25 ºC. The derivatives of chitosan soluble in aqueous acidic medium were characterized by 1H NMR. The rheological behavior of the chitosan and of the derivative of chitosan (sample QV), which presented the largest viscosity, were studied as a function of polymer concentration, temperature and ionic strength of the medium. The results of characterization of the commercial chitosan (the degree of deacetylation obtained equal 78.45 %) used in this work confirmed a sample of low molar weight (Mv = 3.57 x 104 g/mol) and low viscosity (intrinsic viscosity = 213.56 mL/g). The chemical modification of the chitosan resulted in derivatives with thickening action. The spectra of 1H NMR of the soluble derivatives in acid aqueous medium suggested the presence of hydrophobic groups grafted into chitosan in function of the chemical modification. The solubility of the derivatives of chitosan in 0.25 M acetic acid aqueous solution decreased with increase of the molar ratio of the glutaraldehyde and 3-amino-1-propanol in relation to the chitosan. The presence of nitrogen and larger amount of reducing agent in reaction system contributed to the increase of the solubility, the stability and the viscosity of the systems. The viscosity of the polymeric suspensions in function of the shear rate increased significantly with polymer concentration, suggesting the formation of strong intermolecular associations. The chitosan presented pseudoplastic behavior with the increase in polymer concentration at a low shear rate. The derivative QV presented pseudoplastic behavior at all concentrations used and in a large range of shear rate. The viscosity of chitosan in solution decreased with an increase of the temperature and with the presence of salt. However, there was an increase of the viscosity of the chitosan solution at higher temperature (65 ºC) and ionic strength of the medium which were promoted by hydrophobic associating of the acetamide groups. The solutions of the chitosan derivatives (sample QV) were significantly more viscous than chitosan solution and showed higher thermal stability in the presence of salt as a function of the hydrophobic groups grafted into chitosan backbone

Inibição à corrosão do aço AISI 1020, em meios ácido e salino, por tensoativos e substâncias nitrogenadas microemulsionados

Corrosion inhibition efficiency of saponified coconut oil (SCO) and sodium dodecilbenzene sulfonate (DBS) surfactants in AISI 1020 carbon steel was evaluated by electrochemical methods. These surfactants were also evaluated as microemulsion systems (SCO-ME and DBS-ME), of O/W type (water-rich microemulsion), in a Winsor IV region. They were obtained according to the following composition: 15% SCO, 15% butanol (30% Co-surfactant/Surfactant C/T), 10% organic phase (FO, kerosene) and 60% aqueous phase (FA). These systems were also used to solubilize the following nitrogenated substances: Diphenylcarbazide (DC), 2,4-dinitro-phenyl-thiosemicarbazide (TSC) and the mesoionic type compound 1,3,4-triazolium-2-thiolate (MI), that were investigated with the purpose of evaluating their anticorrosive effects. Comparative studies of carbon steel corrosion inhibition efficiencies of free DBS and DBS-ME, in brine and acidic media (0.5%), showed that DBS presents better inhibition results in acidic media (free DBS, 89% and DBS-ME, 93%). However, the values obtained for DBS in salted solution (72% free DBS and 77% DBS-ME) were similar to the ones observed for the SCO surfactant in brine (63% free SCO and 74% SCO-ME). Analysis of corrosion inhibition of the nitrogenated substances that were solubilized in the SCO-ME microemulsion system by the linear polarization method in brine (0.5% NaCl) showed that such compounds are very efficient an corrosion inhibitors [DC-ME-SCO (92%), TSC-ME-SCO (93%) and MI-ME-SCO (94%)]

Aplicação de argila branca comercial pura e modificada para adsorção de sulfeto de hidrogênio

In this work were synthesized matrix-based commercial white clay in its composition having large amounts of kaolinite and quartz, with a certain percentage of iron oxide for use as an adsorbent for hydrogen sulfide (H2S). To characterize the effect of initial matrix techniques were used to characterize XRD, FTIR, XRF and TG. The initial clay mineral matrix was placed in contact with 0.1 molar solutions of the salts of Co2+, Ni2+, Cr3+ and a solution 0.1 g / 100ml rhodamine B. During the synthesis process, the solutions were placed in contact with the initial matrix for a period of 48 hours in order to have ion exchange with the clay mineral. To check the amount of exchanged metals, we used the technique of X-ray Fluorescence (XRF). After synthesis was initiated the process of adsorption of H2S, where the arrays were placed in the reactor, then by passing a stream of hydrogen sulfide. The matrix along with the reactor was weighed before and after to measure the amount of gas adsorbed. Based on the gravimetric data the matrix which had the highest performance of the adsorption matrix was exchanged with Ni2+ ions, obtaining a result of 11.13 mg H2S / g matrix, then the matrix coated with rhodamine B which was reached 10.13 mg H2S / g matrix

Síntese e caracterização de compostos de coordenação homolépticos e heterolépticos com o ligante ácido kójico

The present work has as main objective to contribute to the coordination chemistry of the ligand kojic acid, with the synthesis and characterization of the homoleptic compounds [Al(kj)3], [Fe(kj)3], [Fe(kj)2], [Cu(kj)2] e [Ru(kj)3], and the new heteroleptic complexes, trans- K2[Fe(kj)2(CN)2] and trans-Na2[Ru(kj)2(CN)2]. The obtained compounds were characterized by vibrational spectroscopy in the infrared region (IV) and Electronic spectroscopy in the ultraviolet and visible region (Uv-Vis). The infrared results indicated the coordination of the bidentate ligand kojic acid, due to reductions in the values of the stretching frequencies of the carbonyl and double bonds, compared to the free ligand for all complexes obtained. The presence of new vibrational modes indicated the change of symmetry of the molecules in the new compounds synthesized. Additionally, the presence of vibrational modes assigned to metal-oxygen also contributed to confirm the ligand coordinating to the metal ions. Through this technique, was also possible to perform correlations of the numbers of vibrational modes, in the region 1400-900 cm-1 and the compounds geometry. The heteroleptic compounds exhibited υC≡N in 2065 and 2053 cm-1, respectively, for the trans-K2[Fe(kj)2(CN)2] and trans-Na2[Ru(kj)2(CN)2], indicating coordination of the cyano ligand to metal ions FeII e RuII. Comparing the obtained values with literature data was possible to identify the complex isomerism as trans. In relation to the results of electronic spectroscopy, studies of pH variation of kojic acid provided information on the distribution of electron density in the molecule, showing characteristic spectral profile of kojic ion and its protonated form (Hkj, kojic acid), with two bands at 215 and 269 nm, or deprotonated (kj-), with bands at 226 and 315 nm. The electronic spectra obtained for all complexes in aqueous medium, in the ultraviolet region, exhibited variations of the energies assigned to kojic acid intraligand transitions while in the visible region, only transitions assigned to charge transfer of iron and ruthenium complex have been identified

Aplicabilidade da voltametria de pulso diferencial na quantificação de ácido fólico e ácido oxálico: um método comparativo

The development of more selective and sensitive analytical methods is of great importance in different areas of knowledge, covering, for example, food, biotechnological, environmental and pharmaceutical sectors. The study aimed to employ the technique electroanalytical differential pulse voltammetry (DPV) as an innovative and promising alternative for identification and quantification of organic compounds. The organic compounds were investigated in this study oxalic acid (OA) and folic acid (FA). The electrochemical oxidation of oxalic acid has been extensively studied as a model reaction in the boundary between the organic and inorganic electrochemistry. Since the AF, an essential vitamin for cell multiplication in all tissues, which is essential for DNA synthesis. The AF has been investigated using analytical techniques, liquid chromatography and molecular absorption spectrophotometry. The results obtained during the experimental procedure indicated that the process of electrochemical oxidation of oxalic acid is strongly dependent on the nature of the anode material and the oxidation mechanism, which affects their detection. Efficient removal was observed in Ti/PbO2 anodes, graphite, BDD and Pt 90, 85, 80 and 78% respectively. It was also shown that the DPV employing glassy carbon electrode offers a fast, simple, reliable and economical way to determine the AO during the process of electrochemical oxidation. Furthermore, electroanalytical methods are more expensive than commonly used chromatographic analysis and other instrumental methods involving toxic reagents and higher cost. Compared with the classical method of titration and DPV could be a good fit, confidence intervals and detection limits confirming the applicability of electroanalytical technique for monitoring the degradation of oxalic acid. For the study of AF was investigated the electrocatalytic activity of the carbon paste electrode for identification and quantification in pharmaceutical formulations by applying the DPV. The results obtained during the experimental procedure showed an irreversible oxidation peak at 9.1 V characteristic of FA. The carbon paste sensor showed low detection limit of 5.683×10−8 mol L-1 reducing matrix effects. The spectrophotometric analysis showed lower concentrations of HF compared with those obtained by HPLC and DPV. The levels of AF were obtained according to the methodology proposed by the Brazilian Pharmacopoeia. The electroanalytical method (DPV) proposed is cheaper than GC analysis commonly used by the pharmaceutical industry. The results demonstrated the potential of these electroanalytical techniques for future applications in environmental, chemical and biological sensors